Merge pull request #414 from vyomakesh09/master

refactor execution scripts and workflows

.github/workflows/run_examples.yml

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+name: Run Examples Script
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+  schedule:
+    # Runs at 3:00 AM UTC every day
+    - cron: '0 3 * * *'
+
+jobs:
+  run-examples:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Setup Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.9'
+
+    - name: Install dependencies
+      run: |
+        pip install -r requirements.txt
+        # Assuming your script might also need pytest and swarms
+        pip install pytest
+        pip install swarms
+
+    - name: Make Script Executable and Run
+      run: |
+        chmod +x ./swarms/scripts/run_examples.sh
+        ./swarms/scripts/run_examples.sh

playground/examples/example_agent.py

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+import os
+import sys
+
+from dotenv import load_dotenv
+
+# Import the OpenAIChat model and the Agent struct
+from swarms import OpenAIChat, Agent
+
+# Load the environment variables
+load_dotenv()
+
+# Get the API key from the environment
+api_key = os.environ.get("OPENAI_API_KEY")
+
+# Initialize the language model
+llm = OpenAIChat(
+    temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000
+)
+
+
+print(f'this is a test msg for stdout and stderr: {sys.stdout}, {sys.stderr}')
+
+## Initialize the workflow
+agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
+
+# Run the workflow on a task
+out = agent.run("Generate a 10,000 word blog on health and wellness.")
+
+print(out)

playground/examples/example_anthropic.py

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+# Import necessary modules and classes
+from swarms.models import Anthropic
+
+# Initialize an instance of the Anthropic class
+model = Anthropic(anthropic_api_key="")
+
+# Using the run method
+# completion_1 = model.run("What is the capital of France?")
+# print(completion_1)
+
+# Using the __call__ method
+completion_2 = model("How far is the moon from the earth?", stop=["miles", "km"])
+print(completion_2)

playground/examples/example_concurrentworkflow.py

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+import os
+from dotenv import load_dotenv
+from swarms import OpenAIChat, Task, ConcurrentWorkflow, Agent
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Load environment variables
+llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
+agent = Agent(llm=llm, max_loops=1)
+
+# Create a workflow
+workflow = ConcurrentWorkflow(max_workers=5)
+
+# Create tasks
+task1 = Task(agent, "What's the weather in miami")
+task2 = Task(agent, "What's the weather in new york")
+task3 = Task(agent, "What's the weather in london")
+
+# Add tasks to the workflow
+workflow.add(tasks=[task1, task2, task3])
+
+# Run the workflow
+workflow.run()

playground/examples/example_dalle3.py

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+'''from swarms.models import Dalle3
+
+# Create an instance of the Dalle3 class with high quality
+dalle3 = Dalle3(quality="high")
+
+# Define a text prompt
+task = "A high-quality image of a sunset"
+
+# Generate a high-quality image from the text prompt
+image_url = dalle3(task)
+
+# Print the generated image URL
+print(image_url)
+'''

playground/examples/example_gpt4vison.py

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+from swarms import GPT4VisionAPI
+
+# Initialize with default API key and custom max_tokens
+api = GPT4VisionAPI(max_tokens=1000)
+
+# Define the task and image URL
+task = "Describe the scene in the image."
+img = "/home/kye/.swarms/swarms/examples/Screenshot from 2024-02-20 05-55-34.png"
+
+# Run the GPT-4 Vision model
+response = api.run(task, img)
+
+# Print the model's response
+print(response)

playground/examples/example_huggingfacellm.py

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+from swarms.models import HuggingfaceLLM
+import torch
+
+try:
+    inference = HuggingfaceLLM(
+        model_id="gpt2",
+        quantize=False,
+        verbose=True,
+    )
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    inference.model.to(device)
+
+    prompt_text = "Create a list of known biggest risks of structural collapse with references"
+    inputs = inference.tokenizer(prompt_text, return_tensors="pt").to(device)
+
+    generated_ids = inference.model.generate(
+        **inputs,
+        max_new_tokens=1000,  # Adjust the length of the generation
+        temperature=0.7,  # Adjust creativity
+        top_k=50,  # Limits the vocabulary considered at each step
+        pad_token_id=inference.tokenizer.eos_token_id,
+        do_sample=True  # Enable sampling to utilize temperature
+    )
+
+    generated_text = inference.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+    print(generated_text)
+except Exception as e:
+    print(f"An error occurred: {e}")

playground/examples/example_idefics.py

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+# Import the idefics model from the swarms.models module
+from swarms.models import Idefics
+
+# Create an instance of the idefics model
+model = Idefics()
+
+# Define user input with an image URL and chat with the model
+user_input = (
+    "User: What is in this image?"
+    " https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+)
+response = model.chat(user_input)
+print(response)
+
+# Define another user input with an image URL and chat with the model
+user_input = (
+    "User: And who is that?"
+    " https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052"
+)
+response = model.chat(user_input)
+print(response)
+
+# Set the checkpoint of the model to "new_checkpoint"
+model.set_checkpoint("new_checkpoint")
+
+# Set the device of the model to "cpu"
+model.set_device("cpu")
+
+# Set the maximum length of the chat to 200
+model.set_max_length(200)
+
+# Clear the chat history of the model
+model.clear_chat_history()

playground/examples/example_kosmos.py

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+from swarms import Kosmos
+
+# Initialize the model
+model = Kosmos()
+
+# Generate
+out = model.run("Analyze the reciepts in this image", "docs.jpg")
+
+# Print the output
+print(out)

playground/examples/example_logistics.py

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+from swarms.structs import Agent
+import os
+from dotenv import load_dotenv
+from swarms.models import GPT4VisionAPI
+from swarms.prompts.logistics import (
+    Health_Security_Agent_Prompt,
+    Quality_Control_Agent_Prompt,
+    Productivity_Agent_Prompt,
+    Safety_Agent_Prompt,
+    Security_Agent_Prompt,
+    Sustainability_Agent_Prompt,
+    Efficiency_Agent_Prompt,
+)
+
+# Load ENV
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+
+# GPT4VisionAPI
+llm = GPT4VisionAPI(openai_api_key=api_key)
+
+# Image for analysis
+factory_image = "factory_image1.jpg"
+
+# Initialize agents with respective prompts
+health_security_agent = Agent(
+    llm=llm,
+    sop=Health_Security_Agent_Prompt,
+    max_loops=1,
+    multi_modal=True,
+)
+
+# Quality control agent
+quality_control_agent = Agent(
+    llm=llm,
+    sop=Quality_Control_Agent_Prompt,
+    max_loops=1,
+    multi_modal=True,
+)
+
+
+# Productivity Agent
+productivity_agent = Agent(
+    llm=llm,
+    sop=Productivity_Agent_Prompt,
+    max_loops=1,
+    multi_modal=True,
+)
+
+# Initiailize safety agent
+safety_agent = Agent(llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True)
+
+# Init the security agent
+security_agent = Agent(
+    llm=llm, sop=Security_Agent_Prompt, max_loops=1, multi_modal=True
+)
+
+
+# Initialize sustainability agent
+sustainability_agent = Agent(
+    llm=llm,
+    sop=Sustainability_Agent_Prompt,
+    max_loops=1,
+    multi_modal=True,
+)
+
+
+# Initialize efficincy agent
+efficiency_agent = Agent(
+    llm=llm,
+    sop=Efficiency_Agent_Prompt,
+    max_loops=1,
+    multi_modal=True,
+)
+
+# Run agents with respective tasks on the same image
+health_analysis = health_security_agent.run(
+    "Analyze the safety of this factory", factory_image
+)
+quality_analysis = quality_control_agent.run(
+    "Examine product quality in the factory", factory_image
+)
+productivity_analysis = productivity_agent.run(
+    "Evaluate factory productivity", factory_image
+)
+safety_analysis = safety_agent.run(
+    "Inspect the factory's adherence to safety standards",
+    factory_image,
+)
+security_analysis = security_agent.run(
+    "Assess the factory's security measures and systems",
+    factory_image,
+)
+sustainability_analysis = sustainability_agent.run(
+    "Examine the factory's sustainability practices", factory_image
+)
+efficiency_analysis = efficiency_agent.run(
+    "Analyze the efficiency of the factory's manufacturing process",
+    factory_image,
+)

playground/examples/example_mixtral.py

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+from swarms.models import Mixtral
+
+# Initialize the Mixtral model with 4 bit and flash attention!
+mixtral = Mixtral(load_in_4bit=True, use_flash_attention_2=True)
+
+# Generate text for a simple task
+generated_text = mixtral.run("Generate a creative story.")
+
+# Print the generated text
+print(generated_text)

playground/examples/example_qwenvlmultimodal.py

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+from swarms import QwenVLMultiModal
+
+# Instantiate the QwenVLMultiModal model
+model = QwenVLMultiModal(
+    model_name="Qwen/Qwen-VL-Chat",
+    device="cuda",
+    quantize=True,
+)
+
+# Run the model
+response = model("Hello, how are you?", "https://example.com/image.jpg")
+
+# Print the response
+print(response)

playground/examples/example_recursiveworkflow.py

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+import os
+from dotenv import load_dotenv
+from swarms import OpenAIChat, Task, RecursiveWorkflow, Agent
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Load environment variables
+llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
+agent = Agent(llm=llm, max_loops=1)
+
+# Create a workflow
+workflow = RecursiveWorkflow(stop_token="<DONE>")
+
+# Create tasks
+task1 = Task(agent, "What's the weather in miami")
+task2 = Task(agent, "What's the weather in new york")
+task3 = Task(agent, "What's the weather in london")
+
+# Add tasks to the workflow
+workflow.add(task1)
+workflow.add(task2)
+workflow.add(task3)
+
+# Run the workflow
+workflow.run()