Delete main.py

main.py

@@ -1,129 +0,0 @@
-from langgraph.graph import END
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel
-from typing import Dict, List, Any
-from langgraph.graph import StateGraph
-from service import research_task, seo_optimization_task, content_writing_task, refine_content, evaluate_content_quality, feedback_improvement, meeting_insights, upload_file
-from langchain_google_genai import ChatGoogleGenerativeAI
-app = FastAPI()
-llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")
-class ContentState(Dict):
-    idea: str
-    company_name: str
-    services: Dict[str, List[str]]  # Main services with their sub-services
-    service_area: Dict[str, Dict[str, str]]  # Each area has multiple sub-service pages
-    research_data: str
-    seo_optimization: str
-    home_page: str
-    about_us_page: str
-    service_page: str
-    individual_service_page: Dict[str, str]  # Single service pages
-    service_area_page: Dict[str, Dict[str, str]]  # Each area with its sub-services
-    quality_score: int
-    feedback: str
-    content: str
-    data: str
-    text:str
-    meeting_point:str
-    file_path:str
-workflow = StateGraph(ContentState)
-
-# ✅ Define Workflow Steps
-workflow.add_node("research_step", research_task)
-workflow.add_node("seo_step", seo_optimization_task)
-workflow.add_node("writing_step", content_writing_task)
-workflow.add_node("refine_content", refine_content)
-workflow.add_node("evaluate_content_quality", evaluate_content_quality)
-workflow.add_node("feedback_improvement", feedback_improvement)  # Node for quality rework
-workflow.add_node("human_review", lambda state: state)  # Human-in-the-loop review
-workflow.add_node("meeting_insights",meeting_insights)
-workflow.add_node("upload_file",upload_file)
-# ✅ Define Transitions
-workflow.set_entry_point("research_step")
-workflow.set_entry_point("upload_file")
-workflow.add_edge("upload_file", "meeting_insights")
-workflow.add_edge("research_step", "seo_step")
-workflow.add_edge("seo_step", "writing_step")
-workflow.add_edge("meeting_insights", "writing_step")
-workflow.add_edge("writing_step", "refine_content")
-workflow.add_edge("refine_content", "evaluate_content_quality")
-
-# Conditional Flow for Quality Check & Human Review
-workflow.add_conditional_edges(
-    "evaluate_content_quality",
-    lambda state: "feedback_improvement" if state["quality_score"] <= 8 else "human_review",
-    {
-        "feedback_improvement": "feedback_improvement",
-        "human_review": "human_review"
-    }
-)
-
-# ✅ Add Loopback from feedback_improvement to refine_content
-workflow.add_edge("feedback_improvement", "evaluate_content_quality")
-
-# ✅ Add Human-in-the-loop approval before finalization
-workflow.add_edge("human_review", END)
-
-# ✅ Compile the Graph
-content_graph = workflow.compile()
-class RequestModel(BaseModel):
-    idea: str
-    company_name: str
-    services: Dict[str, List[str]]
-    service_area: List[str]
-class UpdateRequest(BaseModel):
-    page_key: List[str]
-    user_query: str   
-def generate_content(data):  # Remove @app.post to make it an importable function
-    state = content_graph.invoke({
-        "idea": data["idea"],
-        "company_name": data["company_name"],
-        "services": data["services"],
-        "service_area": data["service_area"],
-        "quality_score": 0,
-        "file_path": data["file_path"]
-    })
-
-    response = {
-        "home_page": state.get("home_page", ""),
-        "about_us_page": state.get("about_us_page", ""),
-        "service_page": state.get("service_page", ""),
-        "individual_service_page": state.get("individual_service_page", {}),
-        "service_area_page": state.get("service_area_page", {})
-    }
-    
-    return response  # Return dictionary instead of JSONResponse
-
-@app.put("/update-page/")
-def update_page(state: dict, user_query: str):
-    """
-    Updates the selected page content based on user feedback.
-    """
-    current_content = state.get("page_content", "")
-
-    # Define the prompt for updating the content
-    prompt = f"""You are an expert content editor. Modify the content according to this exact request: {user_query}. 
-
-    - If the request asks to remove specific text, completely delete it while keeping the content natural and professional.
-    - If the request involves replacing text, swap it exactly as instructed.
-    - If the request requires rewording, refine the text while keeping the meaning intact.
-    - Do not add explanations, comments, formatting hints, or additional modifications—only return the updated version of the content.
-    - Ensure that only the requested changes are made. Do not include the previous version or additional variations.
-    
-    Here is the content before modification:
-    ---
-    {current_content}
-    ---
-
-    Return only the fully updated content without any extra details.
-    and update only the content that needs to be changed and shown with all content.
-    """
-
-    # Call Gemini to process the update
-    updated_content = llm.invoke(prompt).content.strip()
-
-    # Ensure the modified content is updated correctly
-    return {"page_content": updated_content}
-
-