Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from langchain.agents import initialize_agent, Tool
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms import HuggingFacePipeline
+import json
+import subprocess
+import os
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
+# Load the LLM and tokenizer
+MODEL_NAME = "unit-mesh/autodev-coder-deepseek-6.7b-finetunes"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
+
+# Create a Hugging Face pipeline
+hf_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=0 if torch.cuda.is_available() else -1,
+    max_length=500,
+    temperature=0.7,
+)
+
+# Wrap the pipeline in a LangChain LLM
+llm = HuggingFacePipeline(pipeline=hf_pipeline)
+
+# Define tools for the agents
+tools = [
+    Tool(
+        name="Code Formatter",
+        func=lambda x: subprocess.run(["black", "-"], input=x.encode(), capture_output=True).stdout.decode(),
+        description="Formats code using Black.",
+    ),
+    Tool(
+        name="API Generator",
+        func=lambda x: json.dumps({"endpoints": {"example": "POST - Example endpoint."}}),
+        description="Generates API details from code.",
+    ),
+    Tool(
+        name="Task Decomposer",
+        func=lambda x: json.dumps({"tasks": ["Design UI", "Develop Backend", "Test App", "Deploy App"]}),
+        description="Breaks down app requirements into smaller tasks.",
+    ),
+]
+
+# Define prompt templates
+ui_designer_prompt = PromptTemplate(
+    input_variables=["input"],
+    template="You are a UI Designer. Your task is: {input}",
+)
+
+backend_developer_prompt = PromptTemplate(
+    input_variables=["input"],
+    template="You are a Backend Developer. Your task is: {input}",
+)
+
+qa_engineer_prompt = PromptTemplate(
+    input_variables=["input"],
+    template="You are a QA Engineer. Your task is: {input}",
+)
+
+devops_engineer_prompt = PromptTemplate(
+    input_variables=["input"],
+    template="You are a DevOps Engineer. Your task is: {input}",
+)
+
+# Initialize agents
+ui_designer_agent = initialize_agent(
+    tools=tools,
+    llm=llm,
+    agent="zero-shot-react-description",
+    verbose=True,
+)
+
+backend_developer_agent = initialize_agent(
+    tools=tools,
+    llm=llm,
+    agent="zero-shot-react-description",
+    verbose=True,
+)
+
+qa_engineer_agent = initialize_agent(
+    tools=tools,
+    llm=llm,
+    agent="zero-shot-react-description",
+    verbose=True,
+)
+
+devops_engineer_agent = initialize_agent(
+    tools=tools,
+    llm=llm,
+    agent="zero-shot-react-description",
+    verbose=True,
+)
+
+# Multi-Agent Workflow
+def multi_agent_workflow(requirements: str) -> str:
+    """
+    Execute a multi-agent workflow to generate a complex app.
+    Args:
+        requirements (str): App requirements.
+    Returns:
+        str: Generated app code and API details.
+    """
+    global api_details
+
+    # Step 1: Task Decomposition
+    try:
+        task_decomposition = ui_designer_agent.run(
+            f"Break down the following app requirements into smaller tasks: {requirements}"
+        )
+        tasks = json.loads(task_decomposition)["tasks"]
+    except Exception as e:
+        logger.error(f"Task decomposition failed: {str(e)}")
+        return f"Task decomposition failed: {str(e)}"
+
+    # Step 2: Code Generation
+    try:
+        ui_code = ui_designer_agent.run(f"Generate the UI code for: {tasks[0]}")
+        backend_code = backend_developer_agent.run(f"Generate the backend code for: {tasks[1]}")
+    except Exception as e:
+        logger.error(f"Code generation failed: {str(e)}")
+        return f"Code generation failed: {str(e)}"
+
+    # Step 3: Code Formatting
+    try:
+        formatted_ui_code = ui_designer_agent.run(f"Format the following code: {ui_code}")
+        formatted_backend_code = backend_developer_agent.run(f"Format the following code: {backend_code}")
+    except Exception as e:
+        logger.error(f"Code formatting failed: {str(e)}")
+        return f"Code formatting failed: {str(e)}"
+
+    # Step 4: Integration
+    try:
+        combined_code = f"{formatted_ui_code}\n\n{formatted_backend_code}"
+    except Exception as e:
+        logger.error(f"Code integration failed: {str(e)}")
+        return f"Code integration failed: {str(e)}"
+
+    # Step 5: Testing
+    try:
+        test_results = qa_engineer_agent.run(f"Test the following app: {combined_code}")
+    except Exception as e:
+        logger.error(f"Testing failed: {str(e)}")
+        return f"Testing failed: {str(e)}"
+
+    # Step 6: Deployment
+    try:
+        deployment_status = devops_engineer_agent.run(f"Deploy the following app: {combined_code}")
+    except Exception as e:
+        logger.error(f"Deployment failed: {str(e)}")
+        return f"Deployment failed: {str(e)}"
+
+    # Step 7: API Documentation
+    try:
+        api_details = backend_developer_agent.run(f"Generate API details for: {combined_code}")
+    except Exception as e:
+        logger.error(f"API documentation failed: {str(e)}")
+        return f"API documentation failed: {str(e)}"
+
+    # Return the results
+    return f"""
+Generated App Code:
+{combined_code}
+
+Test Results:
+{test_results}
+
+Deployment Status:
+{deployment_status}
+
+API Details:
+{api_details}
+"""
+
+# Gradio Interface
+def app_generator(requirements: str):
+    """
+    Generate an app based on the provided requirements.
+    Args:
+        requirements (str): App requirements.
+    Returns:
+        str: Generated app code and API details.
+    """
+    return multi_agent_workflow(requirements)
+
+# Gradio UI
+with gr.Blocks() as ui:
+    gr.Markdown("# Autonomous App Generator with LangChain Agents")
+    with gr.Row():
+        requirements_input = gr.Textbox(label="App Requirements", placeholder="Describe the app you want to build...")
+        generate_button = gr.Button("Generate App")
+    output = gr.Textbox(label="Generated App Code and API Details", lines=20)
+    generate_button.click(app_generator, inputs=requirements_input, outputs=output)
+
+# Run the Gradio app
+if __name__ == "__main__":
+    ui.launch()