diff --git a/.dockerignore b/.dockerignore index fad1a39..2be7691 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,8 +1,8 @@ # .dockerignore -venv/ +assets/ +qdrant_data/ +redpanda_data/ __pycache__/ +*.pyc .git/ .env -qdrant_data/ -redpanda_data/ -*.db diff --git a/.gitignore b/.gitignore index e094ad4..59dadc9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,28 +1,104 @@ +# Python __pycache__/ -*.pyc -*.pyo -*.pyd +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# Virtual Environments venv/ .venv/ env/ -.env +ENV/ +env.bak/ +venv.bak/ +# Environment variables +.env +.env.local +.env.*.local +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ .DS_Store .AppleDouble .LSOverride +# OS +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db -.vscode/ -.idea/ - - +# Project-specific data directories assets/videos/ *.mp4 - - qdrant_data/ redpanda_data/ - +# Logs *.log +logs/ +*.log.* + +# Testing +.pytest_cache/ +.coverage +htmlcov/ +.tox/ +.hypothesis/ +.mypy_cache/ +.dmypy.json +dmypy.json + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# pip +pip-log.txt +pip-delete-this-directory.txt + +# Model cache (HuggingFace, etc.) +.cache/ +models/ +*.pt +*.pth +*.ckpt + +# Temporary files +*.tmp +*.temp +*.bak +*.swp +*~ + +# Docker +.dockerignore diff --git a/Dockerfile b/Dockerfile index 3d5cead..6cf922b 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,25 +1,32 @@ -# Use a slim Python image (lightweight and fast) -# If you need GPU support later, change this to an nvidia/cuda image or pytorch/pytorch -FROM python:3.10-slim +# Use Python 3.12 to match your project requirements +FROM python:3.12-slim -# Set the working directory to match the volume mount in your compose file -WORKDIR /app - -# Install system dependencies (often needed for AI/Inference libraries like OpenCV or numpy) +# 1. Install System Dependencies RUN apt-get update && apt-get install -y \ - build-essential \ libgl1 \ libglib2.0-0 \ + ffmpeg \ + curl \ && rm -rf /var/lib/apt/lists/* -# Copy requirements first to leverage Docker caching -COPY requirements.txt . +# 2. Install uv +COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv + +# 3. Set up the application +WORKDIR /app + +# 4. Install Dependencies +# Only copy pyproject.toml (since we deleted uv.lock) +COPY pyproject.toml ./ + +# Run sync WITHOUT --frozen (this resolves dependencies fresh) +RUN uv sync -# Install Python dependencies -RUN pip install --no-cache-dir -r requirements.txt +# CRITICAL: Add the virtual environment to the PATH +ENV PATH="/app/.venv/bin:$PATH" -# Copy the rest of the code +# 5. Copy the Code COPY . . -# This CMD is a fallback; your docker-compose 'command' overrides this. -CMD ["python3", "app/server.py"] +# 6. Default Command +CMD ["uvicorn", "app.server:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/app/audio.py b/app/audio.py new file mode 100644 index 0000000..ff96f7d --- /dev/null +++ b/app/audio.py @@ -0,0 +1,110 @@ +import logging +import os +import uuid + +import ffmpeg +import numpy as np +from fastembed import TextEmbedding +from faster_whisper import WhisperModel + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# --- LOAD MODELS --- +try: + WHISPER = WhisperModel("base", device="cpu", compute_type="int8") + EMBEDDER = TextEmbedding(model_name="BAAI/bge-small-en-v1.5") +except Exception as e: + logger.critical(f"Failed to load Audio models: {e}") + + +def transcribe_chunk( + video_path: str, video_id: str, start_time: float, end_time: float +): + """ + 1. Pipes audio from FFmpeg directly to memory (No Disk I/O). + 2. Uses VAD to skip silence (Saves CPU). + 3. Merges short segments into semantic blocks (Increases Accuracy). + """ + duration = end_time - start_time + + # 1. GHOST CHECK + if not os.path.exists(video_path): + logger.error(f"AUDIO ERROR: File not found at {video_path}") + return [] + + try: + # 2. EXTRACT AUDIO TO MEMORY (Zero-Copy) + # We request raw PCM data (s16le) at 16khz mono + out, _ = ( + ffmpeg.input(video_path, ss=start_time, t=duration) + .output("pipe:", format="s16le", acodec="pcm_s16le", ac=1, ar=16000) + .run(capture_stdout=True, capture_stderr=True) + ) + + # Convert raw bytes to float32 numpy array (Required by Whisper) + # normalized between -1 and 1 + audio_array = ( + np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 + ) + + # 3. TRANSCRIBE WITH VAD + # vad_filter=True stops the model from hallucinating on silence + segments, _ = WHISPER.transcribe(audio_array, vad_filter=True) + + # 4. SEMANTIC MERGING + # Group segments until they form a coherent thought (~200 chars) + points = [] + buffer_text = [] + buffer_start = None + buffer_end = 0.0 + + # Generator to list so we can iterate + segments = list(segments) + + for i, seg in enumerate(segments): + if not buffer_text: + buffer_start = seg.start + + buffer_text.append(seg.text.strip()) + buffer_end = seg.end + + # Current semantic block + full_text = " ".join(buffer_text) + + # Heuristic: If block is long enough OR it's the last segment + if len(full_text) >= 256 or i == len(segments) - 1: + # Embed the GROUPS, not the fragments + vector_gen = EMBEDDER.embed([full_text]) + vector = list(vector_gen)[0].tolist() # Unpack generator + + points.append( + { + "id": str(uuid.uuid4()), + "vector": vector, + "payload": { + "video_id": video_id, + "text": full_text, + # Map relative whisper time back to absolute video time + "timestamp": start_time + buffer_start, + "end_timestamp": start_time + buffer_end, + "type": "audio_transcript", + "strategy": "semantic_merge_256", + }, + } + ) + + # Reset buffer + buffer_text = [] + buffer_start = None + + return points + + except ffmpeg.Error as e: + error_msg = e.stderr.decode("utf8") if e.stderr else "Unknown FFmpeg error" + logger.error(f"FFMPEG MEMORY FAIL: {error_msg}") + return [] + + except Exception as e: + logger.error(f"General Audio Error: {e}") + return [] diff --git a/app/database.py b/app/database.py deleted file mode 100644 index aac7b93..0000000 --- a/app/database.py +++ /dev/null @@ -1,54 +0,0 @@ -import datetime -import sqlite3 - -DB_NAME = "vidsearch.db" - - -def init_db(): - """Creates the table if it doesn't exist""" - conn = sqlite3.connect(DB_NAME) - c = conn.cursor() - c.execute(""" - CREATE TABLE IF NOT EXISTS videos ( - id TEXT PRIMARY KEY, - title TEXT, - url TEXT, - status TEXT, - duration REAL, - created_at TIMESTAMP - ) - """) - conn.commit() - conn.close() - - -def add_video(job_id, url, title="Unknown Video"): - conn = sqlite3.connect(DB_NAME) - c = conn.cursor() - c.execute( - "INSERT INTO videos (id, title, url, status, created_at) VALUES (?, ?, ?, ?, ?)", - (job_id, title, url, "processing", datetime.datetime.now()), - ) - conn.commit() - conn.close() - - -def update_status(job_id, status): - conn = sqlite3.connect(DB_NAME) - c = conn.cursor() - c.execute("UPDATE videos SET status = ? WHERE id = ?", (status, job_id)) - conn.commit() - conn.close() - - -def get_all_videos(): - conn = sqlite3.connect(DB_NAME) - # Return results as a dictionary-like object - conn.row_factory = sqlite3.Row - c = conn.cursor() - c.execute("SELECT * FROM videos ORDER BY created_at DESC") - return c.fetchall() - - -# Initialize immediately when this module is imported -init_db() diff --git a/app/database/__init__.py b/app/database/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/database/index.py b/app/database/index.py new file mode 100644 index 0000000..0c280c4 --- /dev/null +++ b/app/database/index.py @@ -0,0 +1,125 @@ +import hashlib +import os +import uuid + +from sqlalchemy import Column, DateTime, ForeignKey, String, Table, create_engine, func +from sqlalchemy.orm import declarative_base, relationship, sessionmaker + +# --- CONFIG --- +# This matches the DATABASE_URL in your docker-compose +DATABASE_URL = os.getenv( + "DATABASE_URL", "postgresql://user:password@localhost:5432/semantic_cuts" +) + +engine = create_engine(DATABASE_URL) +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) +Base = declarative_base() + +# --- MODELS --- + +# 1. The Link Table (Many-to-Many) +# "User X has Video Y in their library" +user_library = Table( + "user_library", + Base.metadata, + Column("user_id", String, primary_key=True), # The User ID (e.g. "tenant-alpha") + Column("video_id", String, ForeignKey("videos.id"), primary_key=True), + Column("added_at", DateTime, default=func.now()), +) + + +# 2. The Content Table +# "The physical video asset" +class Video(Base): + __tablename__ = "videos" + + id = Column(String, primary_key=True, default=lambda: uuid.uuid4().hex) + url_hash = Column(String, unique=True, index=True, nullable=False) # SHA256 of URL + url = Column(String, nullable=False) + title = Column(String) + status = Column(String, default="queued") # queued, processing, completed, error + created_at = Column(DateTime, default=func.now()) + + +# --- HELPERS --- +def init_db(): + """Creates the tables if they don't exist.""" + Base.metadata.create_all(bind=engine) + + +def get_url_hash(url: str) -> str: + """Consistent hashing to find duplicates.""" + return hashlib.sha256(url.strip().encode()).hexdigest() + + +def update_status(job_id: str, status: str): + """Used by Manager/Worker to update status.""" + db = SessionLocal() + try: + video = db.query(Video).filter(Video.id == job_id).first() + if video: + video.status = status + db.commit() + except Exception as e: + print(f"DB Error: {e}") + finally: + db.close() + + +def add_video_to_library(db, url: str, title: str, user_id: str): + """ + The Smart Ingest Logic: + - If video exists: Link it to user. Return (video, is_new=False). + - If video new: Create it, Link it. Return (video, is_new=True). + """ + url_hash = get_url_hash(url) + + # 1. Check if the video physically exists + video = db.query(Video).filter(Video.url_hash == url_hash).first() + is_new = False + + if not video: + is_new = True + video = Video(id=uuid.uuid4().hex, url_hash=url_hash, url=url, title=title) + db.add(video) + db.commit() + db.refresh(video) + + # 2. Link to the User (Access Control) + # Check if this specific user already has it + link_exists = ( + db.query(user_library).filter_by(user_id=user_id, video_id=video.id).first() + ) + + if not link_exists: + stmt = user_library.insert().values(user_id=user_id, video_id=video.id) + db.execute(stmt) + db.commit() + + return video, is_new + + +def get_user_video_ids(db, user_id: str): + """Get the list of video IDs this user is allowed to search.""" + results = db.query(user_library.c.video_id).filter_by(user_id=user_id).all() + # Results is list of tuples [('id1',), ('id2',)], flatten it: + return [r[0] for r in results] + + +def get_all_videos(): + """Get all videos for admin UI.""" + db = SessionLocal() + try: + videos = db.query(Video).all() + return [ + { + "id": v.id, + "url": v.url, + "title": v.title, + "status": v.status, + "created_at": v.created_at.isoformat() if v.created_at else None, + } + for v in videos + ] + finally: + db.close() diff --git a/app/main.py b/app/main.py deleted file mode 100644 index 51e39f8..0000000 --- a/app/main.py +++ /dev/null @@ -1,56 +0,0 @@ -import json -import uuid - -from fastapi import FastAPI, HTTPException -from kafka import KafkaProducer -from pydantic import BaseModel - -app = FastAPI() - -producer = KafkaProducer( - bootstrap_servers="127.0.0.1:9094", - value_serializer=lambda v: json.dumps(v).encode("utf-8"), -) - - -class VideoRequest(BaseModel): - url: str # could be a YT video - user_id: str = uuid.uuid4().hex - - -@app.get("/health") -async def health(): - return {"status": "ok"} - - -@app.post("/video") -async def video(payload: VideoRequest): - job_id = str(uuid.uuid4().hex) - - task_payload = { - "job_id": job_id, - "user_id": payload.user_id, - "url": payload.url, - "status": "queued", - } - - try: - producer.send("video_ingestion", task_payload) - producer.flush() # WHY? - - print(f"Job {job_id} queued") - - return { - "message": "Job queued successfully", - "job_id": job_id, - "status": "started_processing", - } - - except Exception as e: - raise HTTPException(status_code=500, detail=f"[Kafka Error]{str(e)}") - - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/app/manager.py b/app/manager.py index 7beeab9..298536c 100644 --- a/app/manager.py +++ b/app/manager.py @@ -2,142 +2,126 @@ import os import cv2 -import database import redis import yt_dlp from kafka import KafkaAdminClient, KafkaConsumer, KafkaProducer from kafka.admin import NewTopic +# --- INTERNAL IMPORTS --- +from app.database import index as database + # --- CONFIG --- INGEST_TOPIC = "video_ingestion" CHUNK_TOPIC = "chunk_processing" -KAFKA_BROKER = "127.0.0.1:9094" -REDIS_HOST = "localhost" -REDIS_PORT = 6379 +KAFKA_BROKER = os.getenv("KAFKA_BROKER", "localhost:9094") +REDIS_HOST = os.getenv("REDIS_HOST", "localhost") -CHUNK_DURATION = 5 # Seconds per minion +CHUNK_DURATION = 30 # Increased to 30s for better context NUM_PARTITIONS = 4 -# --- INFRASTRUCTURE --- producer = KafkaProducer( bootstrap_servers=KAFKA_BROKER, value_serializer=lambda v: json.dumps(v).encode("utf-8"), ) - -redis_client = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, decode_responses=True) +redis_client = redis.Redis(host=REDIS_HOST, port=6379, decode_responses=True) def setup_topic(): - """Ensures the topic exists and has enough partitions for parallel processing""" admin_client = KafkaAdminClient(bootstrap_servers=KAFKA_BROKER) - - existing_topics = admin_client.list_topics() - - if CHUNK_TOPIC not in existing_topics: - print(f"Creating topic '{CHUNK_TOPIC}' with {NUM_PARTITIONS} partitions...") - topic_list = [ - NewTopic( - name=CHUNK_TOPIC, num_partitions=NUM_PARTITIONS, replication_factor=1 - ) - ] - admin_client.create_topics(new_topics=topic_list, validate_only=False) - else: - # Optional: You could check partition count here and increase it, - print(f"Topic '{CHUNK_TOPIC}' exists.") + if CHUNK_TOPIC not in admin_client.list_topics(): + print(f"Creating topic '{CHUNK_TOPIC}'...") + admin_client.create_topics( + [ + NewTopic( + name=CHUNK_TOPIC, + num_partitions=NUM_PARTITIONS, + replication_factor=1, + ) + ] + ) def download(url, job_id): - """Downloads video to a shared folder accessible by all workers""" - output_path = f"assets/videos/{job_id}.mp4" - os.makedirs(os.path.dirname(output_path), exist_ok=True) + # DOWNLOAD TO SHARED VOLUME + output_path = f"/app/assets/{job_id}.mp4" if os.path.exists(output_path): - print(f" Video already exists, skipping download.") return output_path - print(f" Downloading {url}...") + print(f"Downloading {url}...") ydl_opts = { "format": "best[ext=mp4]", "outtmpl": output_path, "quiet": True, "no_warnings": True, - "nocheckcertificate": True, # Fix for 403 errors } try: with yt_dlp.YoutubeDL(ydl_opts) as ydl: ydl.download([url]) return output_path except Exception as e: - print(f" Download failed: {e}") + print(f"Download failed: {e}") return None def dispatch_job(job): job_id = job["job_id"] url = job["url"] + user_id = job.get("user_id", "demo_user") + print(f"Manager: Processing {job_id} for user {user_id}") - print(f" Manager: Received Job {job_id}") - - database.add_video(job_id, url, title=f"Video {job_id[:8]}") - # 1. Download (The "Shared Asset") + # 1. Download video_path = download(url, job_id) if not video_path: return - # 2. Probe (Get Duration) + # 2. Update Status + database.update_status(job_id, "processing") + + # 3. Probe Video cap = cv2.VideoCapture(video_path) fps = cap.get(cv2.CAP_PROP_FPS) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) duration_sec = total_frames / fps cap.release() - print(f" Duration: {duration_sec:.2f}s | FPS: {fps}") - - # 3. Split (The Logic) - # We chop the video into 30-second tasks + # 4. Split chunks = [] for start in range(0, int(duration_sec), CHUNK_DURATION): end = min(start + CHUNK_DURATION, duration_sec) chunks.append((start, end)) - total_chunks = len(chunks) - print(f" Slicing into {total_chunks} chunks...") - - # 4. Update Scoreboard (Redis) - # We set the "Remaining Count" to the number of chunks - redis_key = f"job:{job_id}:pending" - redis_client.set(redis_key, total_chunks) + redis_client.set(f"job:{job_id}:pending", len(chunks)) - # 5. Dispatch (Fan-Out) + # 5. Fan-out for i, (start, end) in enumerate(chunks): - chunk_payload = { + payload = { "job_id": job_id, - "video_path": video_path, # Minions read this local file + "video_path": video_path, # Sends absolute path (/app/assets/...) "start_time": start, "end_time": end, "chunk_index": i, - "total_chunks": total_chunks, + "user_id": user_id, } - producer.send(CHUNK_TOPIC, chunk_payload) - - database.update_status(job_id, "processing") + producer.send(CHUNK_TOPIC, payload) producer.flush() - print(f" Dispatched {total_chunks} tasks to '{CHUNK_TOPIC}'") + print(f"Dispatched {len(chunks)} chunks.") def start_manager(): - print(f" Manager listening on '{INGEST_TOPIC}'...") - + # Initialize database + database.init_db() setup_topic() - consumer = KafkaConsumer( INGEST_TOPIC, bootstrap_servers=KAFKA_BROKER, value_deserializer=lambda m: json.loads(m.decode("utf-8")), group_id="manager_group", + max_poll_records=1, # Only download one video at a time + max_poll_interval_ms=1200000, # Allow 20 minutes for download ) - + print("Manager listening...") for message in consumer: dispatch_job(message.value) diff --git a/app/minion.py b/app/minion.py deleted file mode 100644 index 3b483ad..0000000 --- a/app/minion.py +++ /dev/null @@ -1,175 +0,0 @@ -import json -import os -import uuid - -import cv2 -import redis -import requests -from app.scene_detect import SceneDetector -from kafka import KafkaConsumer -from qdrant_client import QdrantClient -from qdrant_client.models import Distance, PointStruct, VectorParams - -# --- CONFIG --- -CHUNK_TOPIC = "chunk_processing" - -# The Minion now talks to the "Inference Service" instead of loading the model itself -INFERENCE_URL = os.getenv("INFERENCE_API_URL", "http://localhost:8001/embed") -KAFKA_BROKER = os.getenv("KAFKA_BROKER", "localhost:9094") -REDIS_HOST = "localhost" -REDIS_PORT = 6379 -QDRANT_HOST = "localhost" -QDRANT_PORT = 6333 -COLLECTION_NAME = "video_frames" - -# --- INFRASTRUCTURE --- -print(f"Minion reporting for duty. Inference Server: {INFERENCE_URL}") - -qdrant = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT) -redis_client = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, decode_responses=True) - -# Ensure Collection Exists -if not qdrant.collection_exists(COLLECTION_NAME): - qdrant.create_collection( - collection_name=COLLECTION_NAME, - vectors_config=VectorParams(size=512, distance=Distance.COSINE), - ) -print("Ready to work.") - - -def get_embedding_from_server(frame): - """ - Sends the raw image frame to the Inference API (server.py) - and receives the 512-float vector. - """ - # Encode frame to JPEG in memory - success, buffer = cv2.imencode(".jpg", frame) - if not success: - return None - - try: - response = requests.post(INFERENCE_URL, files={"file": buffer.tobytes()}) - if response.status_code == 200: - return response.json()["vector"] - else: - print(f"Error from Inference Server: {response.text}") - return None - except Exception as e: - print(f"Connection Error to Inference Server: {e}") - return None - - -def process_chunk(task): - job_id = task["job_id"] - video_path = task["video_path"] - start_time = task["start_time"] - end_time = task["end_time"] - chunk_index = task["chunk_index"] - - print(f"Processing Chunk #{chunk_index} ({start_time}s - {end_time}s)") - - # Instantiate Detector FRESH for every chunk - detector = SceneDetector(threshold=0.7) - - # --- STUTTER FIX: DEBOUNCE LOGIC --- - # We will ignore any cuts that happen less than 1.0 second after the previous one - last_cut_timestamp = -1.0 - MIN_SCENE_DURATION = 1.0 - - cap = cv2.VideoCapture(video_path) - fps = cap.get(cv2.CAP_PROP_FPS) - - # JUMP to the start time - start_frame = int(start_time * fps) - cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) - - points_to_upload = [] - current_frame = start_frame - - while cap.isOpened(): - ret, frame = cap.read() - if not ret: - break - - timestamp = current_frame / fps - - # STOP if we reached the end of our assigned chunk - if timestamp > end_time: - break - - # 1. COOLDOWN CHECK (Fixes the "stutter" bursts) - # If we just cut recently, skip the heavy processing - if ( - last_cut_timestamp != -1.0 - and (timestamp - last_cut_timestamp) < MIN_SCENE_DURATION - ): - current_frame += 1 - continue - - # 2. SCENE DETECTION - is_new_scene = detector.process_frame(frame) - - if is_new_scene: - # 3. REMOTE INFERENCE (Call the separate Brain service) - vector = get_embedding_from_server(frame) - - if vector: - points_to_upload.append( - PointStruct( - id=str(uuid.uuid4()), - vector=vector, - payload={ - "video_id": job_id, - "url": task.get("url", "local_file"), - "timestamp": timestamp, - "second_formatted": f"{int(timestamp // 60)}:{int(timestamp % 60):02d}", - "type": "scene_cut", - }, - ) - ) - print(f"Scene Change at {timestamp:.1f}s -> Encoded.") - - # Update the last cut time so we enforce the cooldown - last_cut_timestamp = timestamp - - current_frame += 1 - - cap.release() - - # Upload to DB - if points_to_upload: - qdrant.upsert(collection_name=COLLECTION_NAME, points=points_to_upload) - print(f"Saved {len(points_to_upload)} vectors for Chunk #{chunk_index}.") - else: - print(f"No scenes detected in Chunk #{chunk_index} (Static video?)") - - # --- THE REDUCE STEP --- - redis_key = f"job:{job_id}:pending" - # Force cast to int for type safety - remaining = int(redis_client.decr(redis_key)) - - if remaining <= 0: - print(f"JOB {job_id} COMPLETE! (I was the last one)") - else: - print(f"There are {remaining} chunks left for other minions.") - - -def start_minion(): - print(f"Minion listening on '{CHUNK_TOPIC}'...") - consumer = KafkaConsumer( - CHUNK_TOPIC, - bootstrap_servers=KAFKA_BROKER, - value_deserializer=lambda m: json.loads(m.decode("utf-8")), - group_id="minion_group", - auto_offset_reset="latest", - ) - - for message in consumer: - try: - process_chunk(message.value) - except Exception as e: - print(f"Error processing chunk: {e}") - - -if __name__ == "__main__": - start_minion() diff --git a/app/search.py b/app/search.py deleted file mode 100644 index 9823f3c..0000000 --- a/app/search.py +++ /dev/null @@ -1,60 +0,0 @@ -import torch -from qdrant_client import QdrantClient -from transformers import CLIPModel, CLIPProcessor - -# --- CONFIGURATION --- -QDRANT_HOST = "localhost" -QDRANT_PORT = 6333 -COLLECTION_NAME = "video_frames" - -print("Loading CLIP Model...") -model_id = "openai/clip-vit-base-patch32" -model = CLIPModel.from_pretrained(model_id) -processor = CLIPProcessor.from_pretrained(model_id) -print("Model Loaded.") - -client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT) - - -def search_video(query_text): - print(f"\nSearching for: '{query_text}'...") - - # 1. Vectorize Text - inputs = processor(text=[query_text], return_tensors="pt", padding=True) - with torch.no_grad(): - text_features = model.get_text_features(**inputs) - - # 2. Normalize - text_vector = text_features[0] - text_vector = text_vector / text_vector.norm(p=2, dim=-1, keepdim=True) - text_vector = text_vector.cpu().numpy().tolist() - - # 3. Search Qdrant (FIXED) - try: - search_result = client.query_points( - collection_name=COLLECTION_NAME, - query=text_vector, - limit=3, - with_payload=True, - ) - hits = search_result.points - except Exception as e: - print(f"Search Error: {e}") - return - - # 4. Display Results - print(f"found {len(hits)} hits:") - for i, hit in enumerate(hits): - score = hit.score - # Safety check: payload might be empty if something went wrong - payload = hit.payload or {} - timestamp = payload.get("second_formatted", "N/A") - print(f" #{i + 1} | Score: {score:.3f} | Time: {timestamp}") - - -if __name__ == "__main__": - while True: - query = input("\nEnter search query (or 'q' to quit): ") - if query.lower() == "q": - break - search_video(query) diff --git a/app/server.py b/app/server.py index 71ae5dd..a57e59b 100644 --- a/app/server.py +++ b/app/server.py @@ -2,307 +2,214 @@ import json import logging import os -import tempfile +import uuid +from typing import List, Optional -import cv2 -import numpy as np import redis import torch -import uvicorn -from fastapi import BackgroundTasks, FastAPI, File, UploadFile +from fastapi import FastAPI, File, Header, HTTPException, UploadFile +from fastembed import TextEmbedding from kafka import KafkaProducer from PIL import Image +from pydantic import BaseModel from qdrant_client import QdrantClient -from qdrant_client.http import models from transformers import CLIPModel, CLIPProcessor -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) +from app.database import index +from app.utils.index import deduplicate_results, reciprocal_rank_fusion -app = FastAPI(title="Semantic Cuts - Inference Engine") +# Initialize database on startup +index.init_db() -# --- CONFIGURATION --- +# --- CONFIG --- +# Detect if running in Docker or Local +IN_DOCKER = os.path.exists("/.dockerenv") -# Detect if running inside Docker or Local -IN_DOCKER = os.getenv("KUBERNETES_SERVICE_HOST") or os.path.exists("/.dockerenv") +KAFKA_BROKER = os.getenv("KAFKA_BROKER", "localhost:9094") +QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost") +REDIS_HOST = os.getenv("REDIS_HOST", "localhost") -# Define Hostnames -QDRANT_HOST = "qdrant" if IN_DOCKER else "localhost" -REDIS_HOST = "redis" if IN_DOCKER else "localhost" -REDPANDA_HOST = "redpanda" if IN_DOCKER else "localhost" -REDPANDA_PORT = 9092 if IN_DOCKER else 9094 +INGEST_TOPIC = "video_ingestion" +AUDIO_COLLECTION = "audio_transcripts" +VIDEO_COLLECTION = "video_frames" -COLLECTION_NAME = "video_frames" -VECTOR_SIZE = 512 +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger("API") -# --- INFRASTRUCTURE CONNECTIONS --- +app = FastAPI(title="Semantic Cuts") -# Redis -try: - redis_client = redis.Redis(host=REDIS_HOST, port=6379, db=0) - redis_client.ping() - logger.info(f"Redis connected at {REDIS_HOST}:6379") -except Exception as e: - logger.error(f"Redis connection failed: {e}") - redis_client = None -# Qdrant -try: - qdrant_client = QdrantClient(host=QDRANT_HOST, port=6333) - logger.info(f"Qdrant connected at {QDRANT_HOST}:6333") -except Exception as e: - logger.error(f"Qdrant connection failed: {e}") - qdrant_client = None - -# Redpanda (Kafka) try: producer = KafkaProducer( - bootstrap_servers=f"{REDPANDA_HOST}:{REDPANDA_PORT}", + bootstrap_servers=KAFKA_BROKER, value_serializer=lambda v: json.dumps(v).encode("utf-8"), + acks="all", ) - logger.info(f"Redpanda connected at {REDPANDA_HOST}:{REDPANDA_PORT}") + logger.info(f"Kafka connected at {KAFKA_BROKER}") except Exception as e: - logger.error(f"Redpanda connection failed: {e}") + logger.critical(f"Kafka Failed: {e}") producer = None -# --- ML MODEL --- +try: + qdrant = QdrantClient(host=QDRANT_HOST, port=6333) + logger.info(f"Qdrant connected at {QDRANT_HOST}") +except Exception as e: + logger.critical(f"Qdrant Failed: {e}") -DEVICE = "cuda" if torch.cuda.is_available() else "cpu" -logger.info(f"Loading CLIP model on {DEVICE}...") -model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(DEVICE) -processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") -logger.info("Model loaded successfully.") +redis_client = redis.Redis(host=REDIS_HOST, port=6379, decode_responses=True) +# --- MODELS --- +DEVICE = "cpu" # Force CPU to avoid CUDA complications in Docker for now +logger.info(f"Loading CLIP on {DEVICE}...") +clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(DEVICE) +clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") -@app.on_event("startup") -def startup_event(): - """Initialize Qdrant collection on server startup.""" - if not qdrant_client: - logger.warning("Qdrant not connected, skipping initialization.") - return +logger.info("Loading Text Embedding...") +text_model = TextEmbedding(model_name="BAAI/bge-small-en-v1.5") - try: - if not qdrant_client.collection_exists(COLLECTION_NAME): - logger.info(f"Creating collection: {COLLECTION_NAME}") - qdrant_client.create_collection( - collection_name=COLLECTION_NAME, - vectors_config=models.VectorParams( - size=VECTOR_SIZE, distance=models.Distance.COSINE - ), - ) - else: - logger.info(f"Collection {COLLECTION_NAME} already exists.") - except Exception as e: - logger.error(f"Failed to initialize Qdrant: {e}") - - -def process_video(file_path: str, video_id: str): - """ - Background task: Extracts frames, embeds them, and saves to Qdrant. - TODO: Incorporate smart scene detection from yesterday to prevent memory usage. - """ - logger.info(f"Starting processing for video: {video_id}") - - cap = cv2.VideoCapture(file_path) - if not cap.isOpened(): - logger.error(f"Could not open video file: {file_path}") - return - - fps = cap.get(cv2.CAP_PROP_FPS) - if fps <= 0: - fps = 30 # Fallback if FPS detection fails - - frame_interval = int(fps) # Process 1 frame per second - - current_frame = 0 - points_to_upload = [] - - while True: - ret, frame = cap.read() - if not ret: - break - - # Only process every Nth frame - if current_frame % frame_interval == 0: - try: - # Convert BGR (OpenCV) to RGB (PIL) - rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - pil_image = Image.fromarray(rgb_frame) - - # Inference - inputs = processor( - images=pil_image, return_tensors="pt", padding=True - ).to(DEVICE) - with torch.no_grad(): - outputs = model.get_image_features(**inputs) - - # Normalize - embedding = outputs[0].cpu().numpy() - norm = np.linalg.norm(embedding) - if norm > 0: - embedding = embedding / norm - - # Prepare Qdrant Point - timestamp = current_frame / fps - point_id = f"{video_id}_{int(timestamp)}" - - # Create a point UUID deterministically if needed, or let Qdrant handle it if using integers. - # Here we use a string ID logic or hash it if Qdrant requires UUIDs. - # For simplicity in this example, we assume Qdrant supports string IDs or we use a UUID helper. - import uuid - - final_id = str(uuid.uuid5(uuid.NAMESPACE_DNS, point_id)) - - points_to_upload.append( - models.PointStruct( - id=final_id, - vector=embedding.tolist(), - payload={ - "video_id": video_id, - "timestamp": timestamp, - "frame_index": current_frame, - }, - ) - ) - except Exception as e: - logger.error(f"Error processing frame {current_frame}: {e}") - - current_frame += 1 - - cap.release() - - # Bulk upload to Qdrant - if points_to_upload and qdrant_client: - try: - qdrant_client.upsert( - collection_name=COLLECTION_NAME, points=points_to_upload - ) - logger.info( - f"Uploaded {len(points_to_upload)} frames to Qdrant for {video_id}." - ) - except Exception as e: - logger.error(f"Failed to upload to Qdrant: {e}") - # Cleanup temp file - try: - os.remove(file_path) - except OSError: - pass - - # Notify Redpanda - if producer: - try: - producer.send( - "video_processed", - { - "video_id": video_id, - "status": "completed", - "frames_processed": len(points_to_upload), - }, - ) - producer.flush() - except Exception as e: - logger.error(f"Failed to send Kafka message: {e}") +class VideoRequest(BaseModel): + url: str + title: Optional[str] = "Untitled" + - logger.info(f"Finished processing video: {video_id}") +class SearchResult(BaseModel): + video_id: str + timestamp: float + end_timestamp: Optional[float] = None + text: Optional[str] = None + score: float + type: str @app.get("/health") def health(): - return { - "status": "operational", - "device": DEVICE, - "infrastructure": { - "qdrant": "connected" if qdrant_client else "disconnected", - "redpanda": "connected" if producer else "disconnected", - "redis": "connected" if redis_client else "disconnected", - }, - } - - -@app.post("/ingest") -async def ingest_video(background_tasks: BackgroundTasks, file: UploadFile = File(...)): - """ - Uploads a video, saves it temporarily, and triggers background processing. - """ - try: - # Create a temp file to store the upload - suffix = os.path.splitext(file.filename)[1] - if not suffix: - suffix = ".mp4" + return {"status": "ok"} - with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp: - content = await file.read() - tmp.write(content) - tmp_path = tmp.name - video_id = os.path.splitext(file.filename)[0] +@app.post("/video") +async def queue_video( + payload: VideoRequest, + x_user_id: str = Header(default="demo_user", alias="x-user-id"), +): + if not producer: + raise HTTPException(status_code=503, detail="Kafka Instance Unavailable") - # Trigger background task - background_tasks.add_task(process_video, tmp_path, video_id) + if not x_user_id: + raise HTTPException(status_code=400, detail="User ID is missing") - return { - "status": "processing_started", - "video_id": video_id, - "message": "Video is being processed in the background.", - } + # Add video to database (smart ingest: deduplicates by URL hash) + db = index.SessionLocal() + try: + video, is_new = index.add_video_to_library( + db, payload.url, payload.title or "Untitled", x_user_id + ) + job_id = video.id + if is_new: + logger.info(f"New video added: {job_id}") + else: + logger.info(f"Existing video linked to user: {job_id}") except Exception as e: - logger.error(f"Ingest failed: {e}") - return {"error": str(e)} + db.rollback() + logger.error(f"Database error: {e}") + raise HTTPException(status_code=500, detail=f"Database error: {e}") + finally: + db.close() + + task_payload = { + "job_id": job_id, + "url": payload.url, + "title": payload.title, + "user_id": x_user_id, + "status": "queued", + } + + producer.send(INGEST_TOPIC, task_payload) + producer.flush() + redis_client.set(f"job:{job_id}:status", "queued") + logger.info(f"Queued Job {job_id}") + return {"job_id": job_id, "status": "queued"} -@app.get("/search") -def search_video(query: str, limit: int = 5): - """ - Search functionality using the modern Qdrant 'query_points' API. - """ - if not qdrant_client: - return {"error": "Qdrant is not connected"} +@app.post("/embed") +async def embed_image(file: UploadFile = File(...)): try: - # 1. Vectorize Text (Using your Torch logic) - inputs = processor(text=[query], return_tensors="pt", padding=True).to(DEVICE) + content = await file.read() + image = Image.open(io.BytesIO(content)) + inputs = clip_processor(images=image, return_tensors="pt").to(DEVICE) with torch.no_grad(): - text_features = model.get_text_features(**inputs) + outputs = clip_model.get_image_features(**inputs) + vector = outputs / outputs.norm(p=2, dim=-1, keepdim=True) + return {"vector": vector.cpu().numpy().tolist()[0]} + except Exception as e: + logger.error(f"Embedding failed: {e}") + raise HTTPException(status_code=500, detail=str(e)) + - # 2. Normalize (Using Torch as per your snippet) - text_vector = text_features[0] - text_vector = text_vector / text_vector.norm(p=2, dim=-1, keepdim=True) - text_vector_list = text_vector.cpu().numpy().tolist() +@app.get("/search", response_model=List[SearchResult]) +async def search(query: str, limit: int = 10): + audio_results = [] + video_results = [] - # 3. Search Qdrant (Using query_points) - search_result = qdrant_client.query_points( - collection_name=COLLECTION_NAME, - query=text_vector_list, + # 1. AUDIO SEARCH + try: + text_vec = list(text_model.embed([query]))[0] + audio_hits = qdrant.query_points( + collection_name=AUDIO_COLLECTION, + query=text_vec, limit=limit, with_payload=True, - ) - - # Access points from the result - hits = search_result.points + ).points - # 4. Format Results - results = [] - for hit in hits: - payload = hit.payload or {} - results.append( + for hit in audio_hits: + audio_results.append( { - "score": hit.score, - "video_id": payload.get("video_id", "unknown"), - # We save 'timestamp' as a float (e.g., 12.0), so we read that back - "timestamp": payload.get("timestamp", 0.0), - "frame_index": payload.get("frame_index"), + "video_id": hit.payload.get("video_id"), + "timestamp": hit.payload.get("timestamp"), + "text": hit.payload.get("text"), + "type": "audio", + "raw_score": hit.score, } ) + except Exception as e: + logger.error(f"Audio Search Failed: {e}") - return {"query": query, "results": results} + # 2. VISUAL SEARCH + try: + inputs = clip_processor(text=[query], return_tensors="pt", padding=True).to( + DEVICE + ) + with torch.no_grad(): + text_features = clip_model.get_text_features(**inputs) + clip_vec = text_features / text_features.norm(p=2, dim=-1, keepdim=True) + video_hits = qdrant.query_points( + collection_name=VIDEO_COLLECTION, + query=clip_vec.cpu().numpy().tolist()[0], + limit=limit, + with_payload=True, + ).points + + for hit in video_hits: + video_results.append( + { + "video_id": hit.payload.get("video_id"), + "timestamp": hit.payload.get("timestamp"), + "text": "[Visual Match]", + "type": "visual", + "raw_score": hit.score, + } + ) except Exception as e: - logger.error(f"Search failed: {e}") - return {"error": str(e)} + logger.error(f"Visual Search Failed: {e}") + + # 3. MERGE & CLEAN + # Rank them fairly + fused_results = reciprocal_rank_fusion(audio_results, video_results) + # Remove clusters of results (e.g. 10s, 11s, 12s -> just 10s) + final_results = deduplicate_results(fused_results) -if __name__ == "__main__": - uvicorn.run(app, host="0.0.0.0", port=8001) + return final_results[:limit] diff --git a/app/utils/__init__.py b/app/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/utils/index.py b/app/utils/index.py new file mode 100644 index 0000000..58fbead --- /dev/null +++ b/app/utils/index.py @@ -0,0 +1,55 @@ +from typing import Dict, List + + +def reciprocal_rank_fusion( + list_a: List[Dict], list_b: List[Dict], k: int = 60 +) -> List[Dict]: + """ + Merges two lists by RANK rather than raw score. + Essential because CLIP scores (0.25) are lower than BGE scores (0.75). + """ + fused_scores = {} + + # Helper to process a list + def process_list(results_list): + for rank, item in enumerate(results_list): + # Unique ID for the specific moment in the specific video + doc_id = f"{item['video_id']}_{int(item['timestamp'])}" + + if doc_id not in fused_scores: + fused_scores[doc_id] = {"item": item, "score": 0} + + # The RRF Formula + fused_scores[doc_id]["score"] += 1 / (k + rank + 1) + + process_list(list_a) + process_list(list_b) + + # Sort by new fused score + results = [val["item"] for val in fused_scores.values()] + for val in fused_scores.values(): + val["item"]["score"] = val["score"] + + results.sort(key=lambda x: x["score"], reverse=True) + return results + + +def deduplicate_results(results: List[Dict], time_threshold: float = 5.0) -> List[Dict]: + """ + Prevents clutter. If we have hits at 10s, 11s, and 12s, just keep 10s. + """ + clean_results = [] + seen_timestamps = [] # simple list of (video_id, timestamp) + + for res in results: + is_duplicate = False + for vid, ts in seen_timestamps: + if res["video_id"] == vid and abs(res["timestamp"] - ts) < time_threshold: + is_duplicate = True + break + + if not is_duplicate: + clean_results.append(res) + seen_timestamps.append((res["video_id"], res["timestamp"])) + + return clean_results diff --git a/app/scene_detector.py b/app/utils/scene_detect.py similarity index 96% rename from app/scene_detector.py rename to app/utils/scene_detect.py index 53295f4..7446f9e 100644 --- a/app/scene_detector.py +++ b/app/utils/scene_detect.py @@ -1,3 +1,7 @@ +""" +Deprecated: Use StructualSceneDetector instead. +""" + import cv2 import numpy as np diff --git a/app/utils/structural_scene_detector.py b/app/utils/structural_scene_detector.py new file mode 100644 index 0000000..f9d0f78 --- /dev/null +++ b/app/utils/structural_scene_detector.py @@ -0,0 +1,44 @@ +import cv2 +import numpy as np + + +class StructuralSceneDetector: + def __init__(self, threshold=10): + # Threshold is Hamming Distance. + # < 10 means images are very similar. + # > 10 means a scene change likely occurred. + self.threshold = threshold + self.last_hash = None + + def _dhash(self, image): + # 1. Grayscale (Structure matters, not color) + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # 2. Resize to 9x8 (72 pixels total) + # We use 9x8 so we can have 8 columns of difference comparisons. + resized = cv2.resize(gray, (9, 8)) + + # 3. Compute Differences (The "Gradient") + # specific logic: is the left pixel brighter than the right pixel? + diff = resized[:, 1:] > resized[:, :-1] + + # 4. Convert to integer hash + return sum([2**i for (i, v) in enumerate(diff.flatten()) if v]) + + def process_frame(self, frame_bgr): + curr_hash = self._dhash(frame_bgr) + + if self.last_hash is None: + self.last_hash = curr_hash + return True + + # 5. Hamming Distance (Bitwise XOR) + # How many bits are different between the two hashes? + hamming_dist = bin(self.last_hash ^ curr_hash).count("1") + + if hamming_dist > self.threshold: + # Structure changed significantly! + self.last_hash = curr_hash + return True + + return False diff --git a/app/worker.py b/app/worker.py new file mode 100644 index 0000000..e247548 --- /dev/null +++ b/app/worker.py @@ -0,0 +1,228 @@ +import json +import logging +import os +import threading +import time +import uuid + +import cv2 +import redis +import requests +from kafka import KafkaConsumer +from kafka.errors import CommitFailedError +from qdrant_client import QdrantClient +from qdrant_client.models import Distance, PointStruct, VectorParams + +# --- INTERNAL IMPORTS --- +from app.audio import transcribe_chunk +from app.database import index as database +from app.utils.structural_scene_detector import StructuralSceneDetector + +# --- CONFIG --- +CHUNK_TOPIC = "chunk_processing" +INFERENCE_URL = os.getenv("INFERENCE_API_URL", "http://api:8000/embed") +KAFKA_BROKER = os.getenv("KAFKA_BROKER", "localhost:9094") +QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost") +REDIS_HOST = os.getenv("REDIS_HOST", "localhost") + +# --- LOGGING --- +logging.basicConfig( + level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s" +) +logger = logging.getLogger("Worker") + +# --- INFRASTRUCTURE --- +try: + qdrant = QdrantClient(host=QDRANT_HOST, port=6333) + redis_client = redis.Redis(host=REDIS_HOST, port=6379, decode_responses=True) +except Exception as e: + logger.critical(f"Infra connection failed: {e}") + exit(1) + +# Ensure Collections +try: + for name, dim in [("video_frames", 512), ("audio_transcripts", 384)]: + if not qdrant.collection_exists(name): + qdrant.create_collection( + name, vectors_config=VectorParams(size=dim, distance=Distance.COSINE) + ) +except Exception as e: + logger.error(f"Collection check failed: {e}") + + +def get_embedding(frame): + success, buffer = cv2.imencode(".jpg", frame) + if not success: + return None + try: + res = requests.post(INFERENCE_URL, files={"file": buffer.tobytes()}) + return res.json()["vector"] if res.status_code == 200 else None + except Exception: + return None + + +def process_chunk(task): + """ + Standard processing logic. No Kafka specific code here. + """ + job_id = task["job_id"] + video_path = task["video_path"] + user_id = task.get("user_id", "demo_user") + + if not os.path.exists(video_path): + logger.critical(f"FILE MISSING: {video_path}") + return + + # --- VISUAL PROCESSING --- + try: + detector = StructuralSceneDetector(threshold=12) + cap = cv2.VideoCapture(video_path) + fps = cap.get(cv2.CAP_PROP_FPS) or 30 + + start_frame = int(task["start_time"] * fps) + max_frame = int(task["end_time"] * fps) + cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) + + visual_points = [] + current_frame = start_frame + last_cut_frame = start_frame + HEARTBEAT_FRAMES = 60 * fps + + while cap.isOpened() and current_frame < max_frame: + ret, frame = cap.read() + if not ret: + break + + is_scene_change = detector.process_frame(frame) + is_heartbeat = (current_frame - last_cut_frame) > HEARTBEAT_FRAMES + + if is_scene_change or is_heartbeat: + timestamp = current_frame / fps + cut_type = "scene_cut" if is_scene_change else "heartbeat" + + # Log sparsely to avoid spamming production logs + logger.debug(f"Visual event ({cut_type}) at {timestamp:.2f}s") + + vec = get_embedding(frame) + if vec: + visual_points.append( + PointStruct( + id=str(uuid.uuid4()), + vector=vec, + payload={ + "video_id": job_id, + "timestamp": timestamp, + "type": cut_type, + "user_id": user_id, + "method": "dhash", + }, + ) + ) + last_cut_frame = current_frame + current_frame += 1 + cap.release() + + if visual_points: + qdrant.upsert("video_frames", visual_points) + logger.info(f"Saved {len(visual_points)} visual vectors.") + + except Exception as e: + logger.error(f"Visual processing failed: {e}") + raise e # Re-raise to signal failure to the thread wrapper + + # --- AUDIO PROCESSING --- + try: + audio_data = transcribe_chunk( + video_path, job_id, task["start_time"], task["end_time"] + ) + if audio_data: + points = [ + PointStruct( + id=p["id"], + vector=p["vector"], + payload={**p["payload"], "user_id": user_id}, + ) + for p in audio_data + ] + qdrant.upsert("audio_transcripts", points) + except Exception as e: + logger.error(f"Audio processing failed: {e}") + # Audio failure might be non-critical, decide if you want to raise e + + # --- REDUCE STEP --- + try: + pending_key = f"job:{job_id}:pending" + remaining = redis_client.decr(pending_key) + if remaining <= 0: + logger.info(f"JOB {job_id} COMPLETE!") + # Update database status + database.update_status(job_id, "completed") + redis_client.set(f"job:{job_id}:status", "completed") + except Exception as e: + logger.error(f"Error updating job status: {e}") + + +def start(): + logger.info("Worker Ready (Threaded Mode).") + + consumer = KafkaConsumer( + CHUNK_TOPIC, + bootstrap_servers=KAFKA_BROKER, + value_deserializer=lambda m: json.loads(m.decode("utf-8")), + group_id="minion_group", + max_poll_records=1, # Fetch only one + enable_auto_commit=False, # We commit manually + max_poll_interval_ms=600000, # 10m safety net (backup for thread loop) + ) + + for msg in consumer: + task = msg.value + logger.info( + f"Processing chunk {task.get('chunk_index')} for job {task.get('job_id')}" + ) + + # 1. PAUSE: Stop fetching to prevent rebalance during processing + partitions = consumer.assignment() + consumer.pause(*partitions) # <--- FIXED: Added * to unpack + + # 2. THREAD: Run processing in background + thread_exc = [] + + def worker(): + try: + process_chunk(task) + except Exception as e: + thread_exc.append(e) + + t = threading.Thread(target=worker) + t.start() + + # 3. HEARTBEAT: Keep the session alive while thread works + while t.is_alive(): + consumer.poll(0) # Sends heartbeat + time.sleep(1) + + t.join() # Ensure finish + + # 4. RESUME & COMMIT + consumer.resume(*partitions) # <--- FIXED: Added * to unpack + + if thread_exc: + logger.error(f"Chunk failed: {thread_exc[0]}") + # OPTIONAL: Send to Dead Letter Queue (DLQ) here + # For now, we commit to avoid the Infinite Loop (Poison Pill) + consumer.commit() + else: + try: + consumer.commit() + logger.info("Chunk committed successfully.") + except CommitFailedError: + logger.critical( + "Commit failed! Processing took too long even with heartbeats." + ) + + +if __name__ == "__main__": + # Initialize database + database.init_db() + start() diff --git a/docker-compose.yml b/docker-compose.yml index a5e0eeb..15473b5 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,19 +1,66 @@ -version: "3.8" - services: - inference: + api: build: . - container_name: inference_engine - command: python3 app/server.py + container_name: semantic_api + command: uv run uvicorn app.server:app --host 0.0.0.0 --port 8000 ports: - - "8001:8001" + - "8000:8000" volumes: - - .:/app + - .:/app # Sync code changes + - /app/.venv # <--- PROTECTS THE PYTHON ENVIRONMENT + - shared_data:/app/assets # Shared DB & Video files environment: - - CUDA_VISIBLE_DEVICES=0 # Optional: Only if you have NVIDIA GPU setup + - KAFKA_BROKER=redpanda:9092 + - REDIS_HOST=redis + - QDRANT_HOST=qdrant + - DATABASE_URL=postgresql://user:password@postgres:5432/semantic_cuts + depends_on: + - redpanda + - redis + - qdrant + - postgres restart: always - # --- INFRASTRUCTURE --- + orchestrator: + build: . + container_name: semantic_orchestrator + command: uv run python -m app.manager + volumes: + - .:/app # Sync code changes + - /app/.venv # <--- PROTECTS THE PYTHON ENVIRONMENT + - shared_data:/app/assets # Shared DB & Video files + environment: + - KAFKA_BROKER=redpanda:9092 + - REDIS_HOST=redis + - DATABASE_URL=postgresql://user:password@postgres:5432/semantic_cuts + depends_on: + - redpanda + - postgres + restart: always + + worker: + build: . + container_name: semantic_worker + command: uv run python -m app.worker + volumes: + - .:/app # Sync code changes + - /app/.venv # <--- PROTECTS THE PYTHON ENVIRONMENT + - shared_data:/app/assets # Must access the downloaded video + environment: + - KAFKA_BROKER=redpanda:9092 + - REDIS_HOST=redis + - QDRANT_HOST=qdrant + - INFERENCE_API_URL=http://api:8000/embed + - DATABASE_URL=postgresql://user:password@postgres:5432/semantic_cuts + depends_on: + - api + - orchestrator + - postgres + restart: always + + # ---------------------------------------------------------------- + # INFRASTRUCTURE + # ---------------------------------------------------------------- qdrant: image: qdrant/qdrant:latest container_name: qdrant @@ -38,7 +85,6 @@ services: - --advertise-kafka-addr PLAINTEXT://redpanda:9092,OUTSIDE://localhost:9094 ports: - "9094:9094" - - "8081:8081" volumes: - ./redpanda_data:/var/lib/redpanda/data restart: always @@ -48,6 +94,23 @@ services: container_name: redis ports: - "6379:6379" + restart: always + + postgres: + image: postgres:15-alpine + restart: always + environment: + POSTGRES_USER: user + POSTGRES_PASSWORD: password + POSTGRES_DB: semantic_cuts + ports: + - "5432:5432" + volumes: + - postgres_data:/var/lib/postgresql/data + # REMOVED: networks: - app_network (so it uses the default network) +# Define the shared volume name volumes: - shared_video_storage: + postgres_data: + driver: local + shared_data: diff --git a/main.py b/main.py new file mode 100644 index 0000000..b1fba8a --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from semantic-cuts!") + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..8dfc409 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,26 @@ +[project] +name = "semantic-cuts" +version = "0.1.0" +description = "Distributed Video Search Engine" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "fastapi>=0.110.0", + "uvicorn[standard]>=0.29.0", + "python-multipart>=0.0.9", # Required for File Uploads + "requests>=2.31.0", + "redis>=5.0.3", + "kafka-python>=2.0.2", + "qdrant-client>=1.8.2", + "opencv-python-headless>=4.9.0.80", # Headless for Docker + "transformers>=4.39.0", + "torch>=2.2.2", + "pillow>=10.0.0,<12.0.0", # Fixed conflict + "fastembed>=0.2.6", + "faster-whisper>=1.0.1", # The one you are missing now + "ffmpeg-python>=0.2.0", # The one you missed before + "yt-dlp>=2024.4.9", + "sqlalchemy>=2.0.45", + "psycopg2-binary>=2.9.11", + "pandas>=2.0.0", +] diff --git a/requirements.txt b/requirements.txt index 858cf10..9e10126 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,6 +8,8 @@ python-multipart # --- Infrastructure (Messaging & DB) --- kafka-python qdrant-client +sqlalchemy>=2.0.45 +psycopg2-binary>=2.9.11 # --- Video Processing (Worker) --- opencv-python-headless @@ -21,5 +23,6 @@ transformers # --- Frontend (UI) --- streamlit watchdog +pandas>=2.0.0 redis diff --git a/scripts/cleanup_qdrant.py b/scripts/cleanup_qdrant.py new file mode 100644 index 0000000..2e1be5c --- /dev/null +++ b/scripts/cleanup_qdrant.py @@ -0,0 +1,17 @@ +from qdrant_client import QdrantClient + +# Connect to Qdrant +client = QdrantClient(host="localhost", port=6333) + +collections = ["video_frames", "audio_transcripts"] + +print("WARNING: Wiping Database locally..") + +for name in collections: + if client.collection_exists(name): + client.delete_collection(name) + print(f"Deleted collection: {name}") + else: + print(f"Collection not found: {name}") + +print("\nDatabase is clean. Please restart the Server/Worker to recreate them.") diff --git a/ui/admin.py b/ui/admin.py index a5be2e5..6d6eda2 100644 --- a/ui/admin.py +++ b/ui/admin.py @@ -2,25 +2,19 @@ import sys import time -# 1. Get the path to the current file's directory (ui/) -current_dir = os.path.dirname(os.path.abspath(__file__)) +# Add project root to path for proper imports +project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, project_root) -# 2. Get the path to the 'app' directory (sibling to ui/) -# Logic: Go up one level (..), then down into 'app' -app_dir = os.path.join(os.path.dirname(current_dir), "app") - -# 3. Add the 'app' directory explicitly to Python's search path -sys.path.append(app_dir) - -# 4. Now import 'database' directly (since we are technically "inside" the app folder now) -import database +# Now import properly +from app.database import index as database # 4. Now you can imacport from the 'app' folder import pandas as pd import requests import streamlit as st -st.set_page_config(page_title="VidSearch Admin", page_icon="🛡️", layout="wide") +st.set_page_config(page_title="VidSearch Admin", layout="wide") API_URL = "http://localhost:8000/video" st.title("Admin Console") @@ -45,7 +39,7 @@ API_URL, json={"url": video_url, "user_id": user_id} ) if response.status_code == 200: - st.toast("Job Queued Successfully!", icon="✅") + st.toast("Job Queued Successfully!") time.sleep(1) st.rerun() else: @@ -56,6 +50,8 @@ # --- RIGHT: LIBRARY --- with col2: st.subheader("2. Video Library") + # Initialize database if needed + database.init_db() videos = database.get_all_videos() if not videos: diff --git a/ui/frontend.py b/ui/frontend.py index e862ded..bd68dd4 100644 --- a/ui/frontend.py +++ b/ui/frontend.py @@ -7,7 +7,7 @@ from transformers import CLIPModel, CLIPProcessor # --- CONFIGURATION --- -st.set_page_config(page_title="Semantic Cuts", page_icon="🎬", layout="wide") +st.set_page_config(page_title="Semantic Cuts", layout="wide") QDRANT_HOST = "localhost" QDRANT_PORT = 6333 @@ -45,7 +45,7 @@ def load_models(): ) if response.status_code == 200: - st.success(" Job Queued!") + st.success("Job Queued!") st.info(f"Job ID: {response.json().get('job_id')}") st.markdown("Check your **Worker Terminal** to see progress.") else: @@ -59,13 +59,13 @@ def load_models(): try: # Simple health check info = client.get_collection(COLLECTION_NAME) - st.success(f" Database Connected") + st.success(f"Database Connected") st.caption(f"Indexed Vectors: {info.points_count}") 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