Byte404

.gitignore

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+venv/
+Backend/requirements.txt
+dat.txt
+temp.txt
+Backend/test.py
+main.py
+iteration3/
+test_files/
+Backend/test/

.travis.yml

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+language: python
+python:
+  - "3.8"
+
+# Customize this script to match your requirements
+script:
+  - echo "Running tests..."
+  - pip install -r angelo_requirements.txt
+
+# This section is important for badge generation
+before_deploy:
+  - git fetch --unshallow
+

Backend/Final_LatestSorter.py

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+from fastapi import APIRouter
+import boto3
+import openai
+import time
+from botocore.exceptions import ClientError
+
+number=4
+s3_access_key = ""
+s3_secret_access_key = ""
+s3_bucket_name = "learnmateai"
+
+s3 = boto3.client("s3", aws_access_key_id=s3_access_key, aws_secret_access_key=s3_secret_access_key)
+
+# Set up OpenAI API credentials
+openai.api_key = ''
+
+def batch_text(input_text, delimiter="Module"):
+    batches = input_text.split(delimiter)
+    cleaned_batches = [batch.strip() for batch in batches if batch.strip()]
+    if(len(cleaned_batches)<3):
+        batches = input_text.split("MODULE")
+        cleaned_batches = [batch.strip() for batch in batches if batch.strip()]
+    return cleaned_batches
+
+def upload_to_s3(bucket_name, folder_name, file_name, content):
+    s3 = boto3.client('s3')
+    key = folder_name + '/' + file_name
+    s3.put_object(Body=content, Bucket=bucket_name, Key=key)
+
+def get_text_from_s3(bucket_name, file_name, encoding='utf-8'):
+    response = s3.get_object(Bucket=bucket_name, Key=file_name)
+    content = response['Body'].read()
+
+    try:
+        text_content = content.decode(encoding)
+    except UnicodeDecodeError:
+        # Handle decoding error gracefully
+        text_content = content.decode('latin-1')  # Try an alternative encoding
+
+    return text_content
+
+app = APIRouter()
+
+@app.get("/sorter")
+def process_files(user:str):
+    user=user+"/"
+        # Make an API request with a reset message
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+                                {
+                        "role": "user",
+                        "content": "forget everything told before by me"
+                    }
+        ]
+    )
+    print("reseting")
+    # Function to read and process a file
+    def process_file(file_name,user1):
+
+
+
+        # Read file from S3
+        print(user1)
+        response = s3.get_object(Bucket='learnmateai', Key=user1+'pyqs_txt/' + file_name)
+        file_content = response['Body'].read().decode('utf-8')
+
+        # Split file content into batches (adjust batch size as needed)
+        batch_size = 30000
+        batches = [file_content[i:i+batch_size] for i in range(0, len(file_content), batch_size)]
+        print(user1+"syllabus_txt/syllabus.txt")
+        response2 = s3.get_object(Bucket='learnmateai', Key= user1+"syllabus_pdf/syllabus.txt")
+        topics = response2['Body'].read().decode('utf-8')
+        # Process batches
+        Sorted_PYQ_Mod=[[]for _ in range(5)]
+        for batch in batches:
+            # Send batch to OpenAI API
+            print(batch)
+
+            response = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {
+                        "role": "user",
+                        "content": f"I will feed you a question paper as text,sort the question in the text below based on this syllabus having {number} modules :{topics} (it should look exactly like MODULE:questions ) all questions should cluster under its module , the output should exactly  have the {number} number of ""MODULE"" written under each the questions come ,it should have all MODULE even if any is empty, never give question seperately with there modules tag, i need questions to be grouped under module always,Any output you give should only be from the txt given below you should not create any new question :\n\n{batch}\n\n"
+                    }
+                ]
+            )
+
+            important_topics = response.choices[0].message.content
+            #print(important_topics)
+            #return important_topics
+            # Add a delay of 20 seconds to handle rate limit
+
+
+            text_batches = batch_text(important_topics)
+            #print(text_batches)
+
+            bucket_name = 'learnmateai'
+            folder_name = user1+'Sorted_PYQS/'
+
+
+            i=0
+            try:
+
+                for batch in enumerate(text_batches):
+                    print(batch)
+
+                    result=' '.join(str(element) for element in batch)
+                    new_content = result
+                    response = s3.get_object(Bucket=bucket_name, Key=folder_name+"Module"+str(i+1)+".txt")
+                    current_content = response['Body'].read().decode('utf-8')
+
+
+                    updated_content = current_content + new_content
+
+                    # Upload the updated content to S3
+                    s3.put_object(Bucket=bucket_name, Key=folder_name+"Module"+str(i+1)+".txt", Body=updated_content.encode('utf-8'))
+
+                    # Print uploaded file information
+                    print(f"File  uploaded to '{user1}{bucket_name}/{folder_name}'")
+                    i=i+1
+
+
+
+                time.sleep(20)
+
+            except ClientError as e:
+
+                if e.response['Error']['Code'] == 'NoSuchKey':
+                    print("File not found in S3 bucket.")
+
+                    for batch in enumerate(text_batches):
+                        print(batch)
+
+                        result=' '.join(str(element) for element in batch)
+                        new_content = result
+
+                        #print(result)
+                        updated_content =new_content
+
+                        # Upload the updated content to S3
+                        s3.put_object(Bucket=bucket_name, Key=folder_name+"Module"+str(i+1)+".txt", Body=updated_content.encode('utf-8'))
+
+                        # Print uploaded file information
+                        print(f"File  uploaded to '{user1}{bucket_name}/{folder_name}'")
+                        i=i+1
+                else:
+                    print("An error occurred:", e)
+
+
+
+
+    # Get the list of files in the "notes_txt" folder
+    response = s3.list_objects_v2(Bucket='learnmateai', Prefix=user+'pyqs_txt/')
+
+    # Process each file
+    for file in response['Contents']:
+        print(file)
+        file_name = file['Key'].split('/')[-1]
+        print(file_name)
+        process_file(file_name,user)
+
+    return {"message": "PYQS SORTED"}

Backend/Final_NotesChunker.py

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+from fastapi import APIRouter
+import boto3
+import openai
+import time
+
+s3_access_key = ""
+s3_secret_access_key = ""
+s3_bucket_name = "learnmateai"
+
+s3 = boto3.client("s3", aws_access_key_id=s3_access_key, aws_secret_access_key=s3_secret_access_key)
+
+# Set up OpenAI API credentials
+openai.api_key = ''
+
+def batch_text(input_text, delimiter="TOPIC:"):
+    batches = input_text.split(delimiter)
+    cleaned_batches = [batch.strip() for batch in batches if batch.strip()]
+    return cleaned_batches
+
+def upload_to_s3(bucket_name, folder_name, file_name, content):
+    s3 = boto3.client('s3')
+    key = folder_name + '/' + file_name
+    s3.put_object(Body=content, Bucket=bucket_name, Key=key)
+
+app = APIRouter()
+
+@app.get("/process_files")
+def process_files(user: str):
+    user=user+"/"
+    # Function to read and process a file
+    def process_file(file_name):
+        # Read file from S3
+        response = s3.get_object(Bucket='learnmateai', Key=user+'notes_txt/' + file_name)
+        file_content = response['Body'].read().decode('utf-8')
+
+        # Split file content into batches (adjust batch size as needed)
+        batch_size = 3000
+        batches = [file_content[i:i+batch_size] for i in range(0, len(file_content), batch_size)]
+
+        # Process batches
+        for batch in batches:
+            # Send batch to OpenAI API
+
+
+            response = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {
+                        "role": "user",
+                        "content": f"divide the text topic wise (it should look like TOPIC:notes) notes should very breif and be created in a way so that you will be able to recreate the full txt :\n\n{batch}\n\n"
+                    }
+                ]
+            )
+
+            important_topics = response.choices[0].message.content
+            #print(important_topics)
+            #return important_topics
+            # Add a delay of 20 seconds to handle rate limit
+            time.sleep(20)
+
+            text_batches = batch_text(important_topics)
+
+            bucket_name = 'learnmateai'
+            file=file_name.split(".")[0]
+            folder_name = f'{user}Analysed_Notes/{file}'
+
+            for i, batch in enumerate(text_batches):
+                lines = batch.split('\n')
+                file_name1 = lines[0].strip().replace(" ", "_") + '.txt'
+                content = '\n'.join(lines[1:]).strip()
+                upload_to_s3(bucket_name, folder_name, file_name1, content)
+
+                # Print uploaded file information
+                print(f"File '{file_name1}' uploaded to '{bucket_name}/{folder_name}'")
+
+    # Get the list of files in the "notes_txt" folder
+    response = s3.list_objects_v2(Bucket='learnmateai', Prefix=user+'notes_txt/')
+
+    # Process each file
+    for file in response['Contents']:
+        file_name = file['Key'].split('/')[-1]
+        process_file(file_name)
+
+    return {"message": "NOTES"}