Inferenced with web page

.gitignore

@@ -1,2 +1,4 @@
 myenv
 .env
+static
+templates

main.py

@@ -2,18 +2,88 @@ from fastapi import FastAPI
 from pydantic import BaseModel
 from my_agent.agent import build_graph
 import pandas as pd
+from typing import Optional
 
 app = FastAPI()
 graph = build_graph()
 
-# Optional: define input schema
 class RequestInput(BaseModel):
-    # query: list =[ "I want to make a promotional video of restaurant near lakeside"]
     query: list 
+    preferred_topics: Optional[list] = [] 
 
 
 @app.post("/run")
 def run_graph(input_data: RequestInput):
     result = graph.invoke({'topic' : input_data.query})
-    return {'returned_story': result['final_story']}
-    
+    return {'final_story': result['final_story']}
+    
+
+# *********************INFERENCING PART****************************
+# import asyncio
+# from fastapi import FastAPI, Request, Form
+# from fastapi.responses import HTMLResponse
+# from fastapi.staticfiles import StaticFiles
+# from fastapi.templating import Jinja2Templates
+# from my_agent.agent import build_graph
+# from starlette.concurrency import run_in_threadpool
+
+# app = FastAPI()
+# graph = build_graph()
+
+# app.mount("/static", StaticFiles(directory="static"), name="static")
+# templates = Jinja2Templates(directory="templates")
+
+# # Store session state in memory (simple approach)
+# session_data = {
+#     "topic": [],
+#     "preferred_topics": [],
+# }
+
+# @app.get("/", response_class=HTMLResponse)
+# def get_home(request: Request):
+#     return templates.TemplateResponse("index.html", {"request": request})
+
+# @app.post("/submit", response_class=HTMLResponse)
+# async def handle_query(request: Request, user_query: str = Form(...)):
+#     session_data["topic"] = [user_query]
+#     result = graph.invoke({"topic": session_data["topic"]})
+#     brainstorming = result.get("brainstroming_topics", [{}])[-1].values()
+#     return templates.TemplateResponse("index.html", {
+#         "request": request,
+#         "topics": brainstorming,
+#         "story": None,
+#         "show_buttons": True
+#     })
+
+# @app.post("/select", response_class=HTMLResponse)
+# async def select_topics(request: Request, selected_topics: list[str] = Form(...), action: str = Form(...)):
+#     session_data["preferred_topics"].append(selected_topics)
+
+#     if action == "go_deeper":
+#         result = await run_in_threadpool(
+#             graph.invoke,
+#             {"topic": session_data["topic"], "preferred_topics": session_data["preferred_topics"]}
+#         )
+#         new_brainstorm = result.get("brainstroming_topics", [{}])[-1].values()
+#         print()
+#         print('*****RESPONSE IS GENERATED********')
+#         print()
+#         return templates.TemplateResponse("index.html", {
+#             "request": request,
+#             "topics": new_brainstorm,
+#             "story": None,
+#             "show_buttons": True
+#         })
+
+#     elif action == "generate_final_story":
+#         result = await run_in_threadpool(
+#             graph.invoke,
+#             {"topic": session_data["topic"], "preferred_topics": session_data["preferred_topics"]}
+#         )
+#         return templates.TemplateResponse("index.html", {
+#             "request": request,
+#             "topics": [],
+#             "story": result.get("final_story"),
+#             "show_buttons": False
+#         })
+

my_agent/utils/data_loader.py

@@ -1,8 +1,19 @@
+# from datasets import load_dataset
+
+# def load_influencer_data():
+#     dataset = load_dataset("subashdvorak/tiktok-agentic-story",revision="embedded")
+#     data= dataset['train'].add_faiss_index('embeddings')
+#     return data
+
+
 from datasets import load_dataset
 
+print("Loading dataset and indexing FAISS...")  # Optional: for debugging
+
+dataset = load_dataset("subashdvorak/tiktok-agentic-story", revision="embedded")
+data = dataset['train'].add_faiss_index('embeddings')
+
 def load_influencer_data():
-    dataset = load_dataset("subashdvorak/tiktok-agentic-story",revision="embedded")
-    data= dataset['train'].add_faiss_index('embeddings')
     return data
 
 

my_agent/utils/nodes.py

@@ -133,6 +133,34 @@ def select_preferred_topics(state: State)-> State:
     return state
 
 
+# def select_preferred_topics(state: State) -> State:
+#     print("---API_feedback_mode---")
+
+#     if not state.brainstroming_topics:
+#         print("No brainstormed topics found.")
+#         state.carry_on = False
+#         return state
+
+#     # Get the latest set of brainstormed topics
+#     topic_values = list(state.brainstroming_topics[-1].values())
+#     print(f"Available topics: {topic_values}")
+
+#     # Ensure preferred_topics is well-formed
+#     if state.preferred_topics and isinstance(state.preferred_topics[-1], list):
+#         latest_selection = state.preferred_topics[-1]
+#         if latest_selection:
+#             print("User selected topics:")
+#             print(latest_selection)
+#             state.carry_on = True
+#             return state
+
+#     print("No preferred topics selected via API. Ending feedback loop.")
+#     state.carry_on = False
+#     return state
+
+
+
+
 def generate_final_story(state:State)-> State:
     template = f'''I want to create a detailed storyline for a video in the given topic. You have to provide me that storyline what to include in the video.
   Now, i am giving you the topic of the video. But the need is to generate the story focusing on the format that i'll provide to you.

requirements.txt

@@ -10,3 +10,4 @@ uvicorn
 numpy 
 pandas
 langchain_huggingface
+python-multipart