Added .env

.gitignore

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+.env
 myenv
-extracted_data.csv

main.py

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-from fastapi import FastAPI , UploadFile , File , Form 
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel
-from src.genai.brainstroming_agent.agent import brainstroming_graph
-import pandas as pd
-from typing import Optional , List
-from src.genai.context_analysis_agent.agent import IntroductionChatbot
-from src.genai.business_interaction_agent.agent import BusinessInteractionChatbot
-from src.genai.context_analysis_agent.utils.utils import save_to_db
-import ast
-from src.genai.orchestration_agent.agent import orchestration_chat
-from src.genai.orchestration_agent.utils.utils import caption_image , show_analytics
-from src.genai.brainstroming_agent.utils.utils import encode_image_to_base64 , generate_final_story, generate_image
-from src.genai.idea_to_budget_agent.agent import budget_calculator
-from src.genai.ideation_agent.agent import ideation_graph
-from langgraph.errors import GraphRecursionError
-from src.genai.human_refined_ideation.agent import human_refined_idea
-from dummy_state import stored_data, long_term_memory
-import json
-
-# Store brainstorming results per thread_id
-
-app = FastAPI()
-context_analysis_graph = IntroductionChatbot()
-business_interaction_graph = BusinessInteractionChatbot()
-idea_graph = ideation_graph()
-brainstrom_graph = brainstroming_graph()
-human_refine_graph = human_refined_idea()
-# orchestrate_graph = orchestration_chat()
-
-
-class OrchestrationRequest(BaseModel):
-    message: str
-    image_base64 : Optional[list] = []
-
-@app.post("/orchestration")
-def orchestration_endpoint(request:OrchestrationRequest):
-    print('Image:',request.image_base64)
-    result = orchestration_chat(request.message , request.image_base64)
-    if result.image_caption != '':
-        stored_data['image_caption']=result.image_caption
-    if result.video_idea !='' or result.video_idea != 'null':
-        stored_data['refined_ideation']= result.video_idea
-    if result.video_story!='' or result.video_story!='null':
-        stored_data['final_story']= result.video_story
-    print('Idea:',stored_data['refined_ideation'])
-    print('Story:', stored_data['final_story'])
-
-    return {'tool_response': result.tool ,
-             'message_response': result.query_response, 
-             'image_caption':result.image_caption,
-             'video_idea': result.video_idea,
-             'video_story': result.video_story}
-
-class UserMessage(BaseModel):
-    message: str
-@app.post("/context-analysis")
-def context_analysis(msg: UserMessage):
-    response = context_analysis_graph.chat(msg.message)
-    if context_analysis_graph.is_complete(response):
-        details = context_analysis_graph.extract_details()
-        if type(details) != dict:
-            details = details.model_dump()
-        print('Business_details:',details)
-        if isinstance(details, str):
-            details= ast.literal_eval(details)
-        print('Details Type:',type(details))
-        # save_to_db(details)
-        stored_data['business_details'] = details
-        context_analysis_graph.reset()
-        return {"response": response, "business_details": details, "complete": True}
-    return {"response": response, "complete": False}
-
-@app.post("/show-analytics")
-def show_analytics_endpoint():
-    response = show_analytics(stored_data['business_details'])
-    return {'response': response}
-
-
-@app.post("/business-interaction")
-def business_interaction(interaction: str):
-    response,business_details = business_interaction_graph.chat(interaction , stored_data['business_details'])
-    stored_data['business_details']=business_details
-    return {'response': response}
-
-class IdeationRequest(BaseModel):
-    topic : List[str]
-
-@app.post("/ideation")
-def ideation_endpoint():
-    config={"recursion_limit":15, "configurable": {"thread_id": "ideation_thread123"}}
-    try:
-        result = idea_graph.invoke(
-        {
-            'business_details': [stored_data['business_details']],
-            'image_caption': [stored_data['image_caption'] if 'image_caption' in stored_data else ""]
-        },
-        config=config,
-        )
-        stored_data['final_ideation'] = result['improver_response'][-1]
-        stored_data['final_ideation']=ast.literal_eval(stored_data['final_ideation'])
-
-        return {'response':ast.literal_eval(result['improver_response'][-1])}
-    except GraphRecursionError:
-        result = idea_graph.get_state({"configurable": {"thread_id": "ideation_thread123"}})
-        return {'response': ast.literal_eval(result[0])}
-    
-class RefineIdeationRequest(BaseModel):
-    query: str
-    thread_id: Optional[str]="refine_ideas_thread"
-
-
-@app.post("/human-idea-refining")
-def human_idea_refine_endpoint(request:RefineIdeationRequest):
-    stored_data['human_ideation_interactions'].append({"role": "user", "content": request.query})
-    response = human_refine_graph.invoke(
-        {
-        'query': stored_data['human_ideation_interactions'],
-        'business_details': stored_data["business_details"],
-        'final_ideation': stored_data.get('final_ideation',["","","",""]),
-        },config={"configurable": {"thread_id": request.thread_id}}
-    )
-    stored_data['human_ideation_interactions'].append({"role": "assistant", "content": response['result']})
-    stored_data['refined_ideation'] = stored_data['human_ideation_interactions'][-1]['content']
-    return {'response' : stored_data['human_ideation_interactions'][-1]['content'] }
-
-
-
-@app.post("/budget-mapping")
-def budget_mapping_endpoint():
-    result = budget_calculator(stored_data["business_details"],stored_data['final_ideation'])
-    return {'response':result}
-
-
-class BrainstormRequest(BaseModel):
-    preferred_topics: Optional[list] = []
-    image_base64_list: Optional[list] = []
-    thread_id: Optional[str]="default-session"
-
-@app.post("/brainstorm")
-def brainstroming_endpoint(
-    request: BrainstormRequest,  # 🔥 Full JSON body here
-):
-    idea = (
-    [stored_data['refined_ideation']]
-    if stored_data.get('refined_ideation')
-    else [str(stored_data['final_ideation'])]
-    if stored_data.get('final_ideation')
-    else ['''I don't have any idea right now. Create your own **very creative** and **out of the box** video idea and generate the story for now.'''])
-
-    result = brainstrom_graph.invoke({
-        # 'idea': [stored_data.get('refined_ideation', 'final_ideation', )],
-        'idea': idea,
-        'images': request.image_base64_list,
-        'latest_preferred_topics': request.preferred_topics,
-        'business_details': stored_data['business_details']
-    },
-    config={"configurable": {"thread_id": request.thread_id}})
-    
-    stored_data['brainstroming_response'] = result
-
-    return {'response':{
-        "story": result['stories'][-1],
-        "brainstorming_topics": result['brainstroming_topics'][-1]
-    }}
-
-
-
-@app.post("/generate-final-story")
-def generate_final_story_endpoint():
-    final_story = generate_final_story(
-    stored_data.get("brainstroming_response") or stored_data.get("business_details")
-    )
-    stored_data['final_story']=final_story
-    return {
-        'response': final_story
-    }
-
-# stored_data['final_story']= '''A cinematic journey follows a street magician\'s 
-# metamorphosis from a mere trickster to a powerful performer, as he transforms his act with newfound physical strength, effortlessly executing death-defying stunts, and inspiring a captivated crowd to take action, all set against a 
-# backdrop of urban grandeur and pulsing energy.'''
-
-
-@app.post("/generate-image")  
-def generate_image_endpoint():
-    image = generate_image(str(stored_data.get('final_story','''I don't have any story right now. Just use the business details for now.'''))
-                           ,str(stored_data.get('business_details'))
-                           ,str(stored_data.get('refined_ideation','''I don't have any idea right now. Just use the business details for now.''')))
-    stored_data['generated_image']=image
-    return {
-        'response':image
-    }
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test.py

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-import faiss
-import ast
-import pandas as pd
-import numpy as np
-from src.genai.utils.models_loader import ST
-import json
-
-def retrieve_tool(business_details):
-    '''
-    Always invoke this tool.
-    Retrieve influencer's data by semantic search of **business details**.
-    '''
-    # === Load CSV ===
-    csv_path = 'extracted_data.csv'
-    df = pd.read_csv(csv_path)
-
-    # === Parse stored embeddings ===
-    df['embeddings'] = df['embeddings'].apply(lambda x: ast.literal_eval(x) if isinstance(x, str) else x)
-    embeddings = np.vstack(df['embeddings'].values).astype('float32')
-
-    # === Build FAISS index ===
-    dimension = embeddings.shape[1]
-    index = faiss.IndexFlatL2(dimension)
-    index.add(embeddings)
-
-    # === Encode the query and search ===
-    query_embedding = ST.encode(str(business_details)).reshape(1, -1).astype('float32')
-    top_k = 15
-    distances, indices = index.search(query_embedding, top_k)
-
-    # === Format results ===
-    results = []
-    for i, idx in enumerate(indices[0]):
-        res = {
-            'username': df.iloc[idx]['username'],
-            'likesCount': int(df.iloc[idx]['likesCount']),
-            'commentCount': int(df.iloc[idx]['commentCount'])
-        }
-        results.append(res)
-
-    return results 
-
-details = {
-    "business_type": "fitness and gym",
-    "platform": "Instagram, TikTok",
-    "target_audience": "young Nepali adults (ages 18–40) who are health-conscious and active on social media",
-    "business_goals": "to expand gym branches across all major cities of Nepal and build a strong fitness community",
-    "offerings": "personal training, group fitness classes, modern workout equipment, nutrition guidance, and wellness programs",
-    "Challenges_faced": "attracting loyal members, standing out in a competitive market, and promoting consistent engagement"
-}
-
-print(retrieve_tool(details))