Converted code to OOP

api/backup_prompts.py

@@ -0,0 +1,3 @@
+image_gen_backup_prompt = "I don't have any story right now. Just use the business details for now."
+brainstorm_backup_prompt = "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."
+

api/routers/brainstorm.py

@@ -1,21 +1,14 @@
 
-import json
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
 from api.stored_data import stored_data
-from pydantic import BaseModel
-from typing import Optional
-from src.genai.brainstroming_agent.agent import brainstroming_graph
+from src.genai.brainstroming_agent.agent import BrainstormingAgent
+from api.schemas.brainstorming import BrainstormRequest
+from api.backup_prompts import brainstorm_backup_prompt
 
 router=APIRouter()
-brainstrom_graph = brainstroming_graph()
-
-
-class BrainstormRequest(BaseModel):
-    preferred_topics: Optional[list] = []
-    image_base64_list: Optional[list] = []
-    thread_id: Optional[str]="default-session"
-
+agent = BrainstormingAgent()
+brainstorm_graph = agent.brainstorming_graph()
 
 @router.post("/brainstorm")
 def brainstorming_endpoint(request: BrainstormRequest):
@@ -24,14 +17,11 @@ def brainstorming_endpoint(request: BrainstormRequest):
         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."
-        ]
+        else [brainstorm_backup_prompt]
     )
 
     def event_stream():
-
-        for stream_mode, chunk in brainstrom_graph.stream(
+        for stream_mode, chunk in brainstorm_graph.stream(
             {
                 "idea": idea,
                 "images": request.image_base64_list,
@@ -41,7 +31,8 @@ def brainstorming_endpoint(request: BrainstormRequest):
             config={"configurable": {"thread_id": request.thread_id}},
             stream_mode=['messages','values'] 
         ):
-            print
             stored_data['brainstorming_response'] = chunk if isinstance(chunk, dict) else stored_data.get('brainstorming_response')
+            # if isinstance(chunk, tuple): yield f"data: {json.dumps(chunk[0].content)}"
             if isinstance(chunk, tuple): yield chunk[0].content
+
     return StreamingResponse(event_stream(), media_type="text/event-stream")

api/routers/context_analysis.py

@@ -1,20 +1,14 @@
 import ast
 import json
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
 from api.stored_data import stored_data
 from src.genai.context_analysis_agent.agent import IntroductionChatbot
-router = APIRouter()
-
+from api.schemas.context_analysis import UserMessage
 
-class UserMessage(BaseModel):
-    message: str
+router = APIRouter()
 context_analysis_graph = IntroductionChatbot()   
 
-
-## ---------------------- Passing in json ------------------------
-
 @router.post("/context-analysis")
 def context_analysis(msg: UserMessage):
     def event_generator():

api/routers/generate_final_story.py

@@ -1,14 +1,14 @@
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
 from api.stored_data import stored_data
-from src.genai.brainstroming_agent.utils.utils import generate_final_story
+from src.genai.brainstroming_agent.utils.utils import FinalStoryGenenrator
 
 router= APIRouter()
 
 @router.post("/generate-final-story")
 def generate_final_story_endpoint():
     def event_stream():
-        for chunk in generate_final_story(stored_data.get('brainstorming_response') or stored_data.get('business_details')):
+        for chunk in FinalStoryGenenrator().generate_final_story(stored_data.get('brainstorming_response') or stored_data.get('business_details')):
             yield chunk
 
     return StreamingResponse(event_stream(), media_type="text/event-stream")

api/routers/generate_image.py

@@ -1,13 +1,14 @@
 from fastapi import APIRouter
 from api.stored_data import stored_data
-from src.genai.brainstroming_agent.utils.utils import generate_image
+from src.genai.brainstroming_agent.utils.utils import ImageGenerator
+from api.backup_prompts import image_gen_backup_prompt
 router = APIRouter()
 
 @router.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.'''))
+    image = ImageGenerator().generate_image(str(stored_data.get('final_story',image_gen_backup_prompt))
                            ,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.''')))
+                           ,str(stored_data.get('refined_ideation',image_gen_backup_prompt)))
     stored_data['generated_image']=image
     return {
         'response':image

api/routers/human_idea_refining.py

@@ -1,15 +1,10 @@
 from fastapi import APIRouter
-from pydantic import BaseModel
-from typing import Optional
 from api.stored_data import stored_data
 from src.genai.human_refined_ideation.agent import human_refined_idea
+from api.schemas.human_idea_refining import RefineIdeationRequest
 router= APIRouter()
 human_refine_graph = human_refined_idea()
 
-class RefineIdeationRequest(BaseModel):
-    query: str
-    thread_id: Optional[str]="refine_ideas_thread"
-
 @router.post("/human-idea-refining")
 def human_idea_refine_endpoint(request:RefineIdeationRequest):
     stored_data['human_ideation_interactions'].append({"role": "user", "content": request.query})

api/routers/ideation.py

@@ -1,6 +1,5 @@
 import ast
 from fastapi import APIRouter
-from fastapi.responses import StreamingResponse
 from api.stored_data import stored_data
 from src.genai.ideation_agent.agent import IdeationAgent
 from langgraph.errors import GraphRecursionError

api/routers/orchestration.py

@@ -1,7 +1,4 @@
-
-import json
-from fastapi import APIRouter, Depends
-from fastapi.responses import StreamingResponse
+from fastapi import APIRouter
 from pydantic import BaseModel
 from api.stored_data import stored_data
 from src.genai.orchestration_agent.agent import OrchestrationAgent

api/schemas/brainstorming.py

@@ -0,0 +1,7 @@
+from pydantic import BaseModel 
+from typing import Optional
+
+class BrainstormRequest(BaseModel):
+    preferred_topics: Optional[list] = []
+    image_base64_list: Optional[list] = []
+    thread_id: Optional[str]="brainstorm-thread"

api/schemas/context_analysis.py

@@ -0,0 +1,4 @@
+from pydantic import BaseModel
+
+class UserMessage(BaseModel):
+    message: str

api/schemas/human_idea_refining.py

@@ -0,0 +1,6 @@
+from pydantic import BaseModel
+from typing import Optional
+
+class RefineIdeationRequest(BaseModel):
+    query: str
+    thread_id: Optional[str]="refine_ideas_thread"

logs/access.log

@@ -241,3 +241,93 @@
 2025-08-13 17:24:22,399 | INFO | access_logger | Response status: 200
 2025-08-13 17:24:37,955 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/orchestration
 2025-08-13 17:24:51,218 | INFO | access_logger | Response status: 200
+2025-08-14 11:47:15,376 | INFO | access_logger | Request: GET http://127.0.0.1:8000/docs
+2025-08-14 11:47:15,398 | INFO | access_logger | Response status: 200
+2025-08-14 11:47:15,615 | INFO | access_logger | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-14 11:47:15,630 | INFO | access_logger | Response status: 200
+2025-08-14 11:47:21,828 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-14 11:48:12,754 | INFO | access_logger | Response status: 200
+2025-08-14 11:55:36,928 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/human-idea-refining
+2025-08-14 11:55:40,634 | INFO | access_logger | Response status: 200
+2025-08-14 11:55:56,886 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 11:55:56,888 | INFO | access_logger | Response status: 200
+2025-08-14 12:03:21,797 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:03:21,799 | INFO | access_logger | Response status: 200
+2025-08-14 12:14:09,786 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:14:09,801 | INFO | access_logger | Response status: 200
+2025-08-14 12:19:21,165 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:19:21,189 | INFO | access_logger | Response status: 200
+2025-08-14 12:20:57,591 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:20:57,593 | INFO | access_logger | Response status: 200
+2025-08-14 12:21:11,123 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:21:11,126 | INFO | access_logger | Response status: 200
+2025-08-14 12:22:02,907 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:22:02,909 | INFO | access_logger | Response status: 200
+2025-08-14 12:31:51,104 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:31:51,118 | INFO | access_logger | Response status: 200
+2025-08-14 12:32:26,599 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:32:26,601 | INFO | access_logger | Response status: 200
+2025-08-14 12:34:08,810 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:34:08,885 | INFO | access_logger | Response status: 200
+2025-08-14 12:35:30,940 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:35:30,957 | INFO | access_logger | Response status: 200
+2025-08-14 12:37:18,328 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:37:18,337 | INFO | access_logger | Response status: 200
+2025-08-14 12:40:59,993 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:40:59,997 | INFO | access_logger | Response status: 200
+2025-08-14 12:42:16,539 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:42:16,541 | INFO | access_logger | Response status: 200
+2025-08-14 12:42:54,879 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 12:42:54,902 | INFO | access_logger | Response status: 200
+2025-08-14 13:45:47,494 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 13:45:47,525 | INFO | access_logger | Response status: 200
+2025-08-14 13:52:59,711 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 13:52:59,714 | INFO | access_logger | Response status: 200
+2025-08-14 13:54:03,079 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 13:54:03,109 | INFO | access_logger | Response status: 200
+2025-08-14 14:00:06,222 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 14:00:06,323 | INFO | access_logger | Response status: 200
+2025-08-14 14:03:56,592 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 14:03:56,599 | INFO | access_logger | Response status: 200
+2025-08-14 14:09:05,364 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 14:09:05,402 | INFO | access_logger | Response status: 200
+2025-08-14 14:10:08,399 | INFO | access_logger | Request: GET http://127.0.0.1:8000/docs
+2025-08-14 14:10:08,400 | INFO | access_logger | Response status: 200
+2025-08-14 14:10:08,576 | INFO | access_logger | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-14 14:10:08,589 | INFO | access_logger | Response status: 200
+2025-08-14 14:10:19,149 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 14:10:19,151 | INFO | access_logger | Response status: 200
+2025-08-14 14:11:05,613 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 14:11:05,615 | INFO | access_logger | Response status: 200
+2025-08-14 14:24:54,914 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 14:24:54,986 | INFO | access_logger | Response status: 200
+2025-08-14 14:28:36,846 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 14:28:36,859 | INFO | access_logger | Response status: 200
+2025-08-14 14:30:58,723 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-14 14:30:58,748 | INFO | access_logger | Response status: 200
+2025-08-14 14:33:02,605 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-14 14:33:02,608 | INFO | access_logger | Response status: 200
+2025-08-14 14:34:33,342 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-14 14:34:33,358 | INFO | access_logger | Response status: 200
+2025-08-15 14:32:24,485 | INFO | access_logger | Request: GET http://127.0.0.1:8000/docs
+2025-08-15 14:32:24,512 | INFO | access_logger | Response status: 200
+2025-08-15 14:32:25,516 | INFO | access_logger | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-15 14:32:25,526 | INFO | access_logger | Response status: 200
+2025-08-15 14:32:40,662 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-08-15 14:32:50,602 | INFO | access_logger | Response status: 200
+2025-08-15 14:33:02,418 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/show-analytics
+2025-08-15 14:33:03,160 | INFO | access_logger | Response status: 200
+2025-08-15 14:33:13,459 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-15 14:34:07,833 | INFO | access_logger | Response status: 200
+2025-08-15 14:36:25,214 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/human-idea-refining
+2025-08-15 14:36:28,230 | INFO | access_logger | Response status: 200
+2025-08-15 14:38:16,304 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-15 14:38:16,306 | INFO | access_logger | Response status: 200
+2025-08-15 14:39:13,673 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/generate-final-story
+2025-08-15 14:39:13,675 | INFO | access_logger | Response status: 200
+2025-08-15 14:39:29,362 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/generate-image
+2025-08-15 14:39:38,943 | INFO | access_logger | Response status: 200
+2025-08-15 15:12:47,227 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-15 15:12:47,265 | INFO | access_logger | Response status: 200
+2025-08-15 15:16:31,623 | INFO | access_logger | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-15 15:16:31,646 | INFO | access_logger | Response status: 200

logs/app.log

@@ -3,3 +3,4 @@
 2025-08-13 11:48:54,941 | ERROR | app_logger | Error while showing analytics: retrieve_data_for_analytics() missing 1 required positional argument: 'business_details'
 2025-08-13 13:53:24,416 | INFO | app_logger | Showing Analytics of the influencers after context analysis.
 2025-08-13 17:24:22,398 | INFO | app_logger | Showing Analytics of the influencers after context analysis.
+2025-08-15 14:33:03,159 | INFO | app_logger | Showing Analytics of the influencers after context analysis.

requirements.txt

@@ -1,5 +1,5 @@
 
-langgraph==0.6.4
+langgraph
 langsmith 
 langchain_groq
 pydantic 

src/genai/brainstroming_agent/agent.py

@@ -1,23 +1,24 @@
 from langgraph.graph import StateGraph, START, END
 from .utils.state import State
-from .utils.nodes import retrieve, generate_story, generate_brainstroming,caption_image
+from .utils.nodes import Retriever , ImageCaptioner , StoryGenerator, BrainstromTopicGenerator
 from langgraph.checkpoint.memory import MemorySaver
 
 memory = MemorySaver()
+class BrainstormingAgent:
+    def __init__(self):
+        self.memory = MemorySaver()
 
+    def brainstorming_graph(self):
+        builder = StateGraph(State)
+        builder.add_node("caption_image",ImageCaptioner().run)
+        builder.add_node("retrieve",Retriever().run)
+        builder.add_node("generate_story",StoryGenerator().run)
+        builder.add_node("generate_brainstroming",BrainstromTopicGenerator().run)
 
-def brainstroming_graph():
-    builder = StateGraph(State)
-    builder.add_node(caption_image)
-    builder.add_node(retrieve)
-    builder.add_node(generate_story)
-    builder.add_node(generate_brainstroming)
 
-
-    # Normal edges
-    builder.add_edge(START, "caption_image")
-    builder.add_edge("caption_image", "retrieve")
-    builder.add_edge("retrieve", "generate_story")
-    builder.add_edge("generate_story", "generate_brainstroming")
-    builder.add_edge("generate_brainstroming", END)
-    return builder.compile(checkpointer=memory)
+        builder.add_edge(START, "caption_image")
+        builder.add_edge("caption_image", "retrieve")
+        builder.add_edge("retrieve", "generate_story")
+        builder.add_edge("generate_story", "generate_brainstroming")
+        builder.add_edge("generate_brainstroming", END)
+        return builder.compile(checkpointer=self.memory)

src/genai/brainstroming_agent/utils/nodes.py

@@ -1,118 +1,112 @@
-import pandas as pd
-import ast
-from .state import State
-from .tools import retrieve_tool
-from langchain_core.messages import SystemMessage ,HumanMessage, ToolMessage, FunctionMessage
-from src.genai.utils.models_loader import llm , llm_gpt
-from src.genai.utils.data_loader import load_influencer_data
-from groq import Groq
 import os
-from .prompts import image_captioning_prompt , initial_story_prompt , refined_story_prompt , brainstroming_prompt 
-from langgraph.prebuilt import create_react_agent
+from groq import Groq
+from .state import State
+from .tools import Retrieval
 from .state import BrainstromTopicFormatter
+from src.genai.utils.models_loader import llm , llm_gpt , captioning_model
+from langchain_core.messages import SystemMessage ,HumanMessage, FunctionMessage
+from .prompts import image_captioning_prompt , initial_story_prompt , refined_story_prompt , brainstroming_prompt 
 
 
-
-def caption_image(state: State) -> State:
-    if len(state.images)>0:
-      if state.images[-1]!=None:
-          print('Captioning image')
-          client = Groq(api_key=os.environ.get('GROQ_API_KEY'))
-
-          chat_completion = client.chat.completions.create(
-              messages=[
-                  {
-                      "role": "user",
-                      "content": [
-                          {"type": "text", "text": image_captioning_prompt(state)},
-                          {
-                              "type": "image_url",
-                              "image_url": {
-                                  "url": f"data:image/jpeg;base64,{state.images[-1]}",
-                              },
-                          },
-                      ],
-                  }
-              ],
-              model="meta-llama/llama-4-scout-17b-16e-instruct",
-              max_completion_tokens=50,
-              temperature = 1
-          )
-          response=chat_completion.choices[0].message.content
-          state.image_captions.append(response)
-          return state
-    
-    else:
-       state.images.append(None)
-       state.image_captions.append(None)
-       return state
-    
-
-
-def retrieve(state: State) -> State:
-  print('Moving to retrieval process')
-  retrievals=[]
-  query_prompt = 'Represent this sentence for searching relevant passages: '
-  if len(state.latest_preferred_topics)==0:
-      for idea in state.idea:  
-          print('The idea for retrieval:', idea)
-          result = retrieve_tool(idea+query_prompt)
-          retrievals.append(result)
-          print('Retrieval process completed......')
-      state.retrievals.append(retrievals)
-
-  if len (state.latest_preferred_topics)>0:
-      print('The preferred_topics are:',state.latest_preferred_topics)
-      state.preferred_topics.append(state.latest_preferred_topics)
-      for idea in state.preferred_topics[-1]:  
-          result = retrieve_tool(idea+query_prompt)
-          retrievals.append(result)
-          print('Retrieval process completed for preferred_topics......')
-      state.latest_preferred_topics=[]
-      state.retrievals.append(retrievals)
-  return state
-
-def generate_story(state:State)-> State:
-    react_agent=create_react_agent(
-        model=llm_gpt,
-        tools=[]
+class ImageCaptioner:
+    def __init__(self):
+         self.captioning_model = captioning_model
+         self.client =  Groq(api_key=os.environ.get('GROQ_API_KEY'))
+
+    def run(self, state: State) -> State:
+        if len(state.images)>0:
+            if state.images[-1]!=None:
+                print('Captioning image')
+
+                chat_completion = self.client.chat.completions.create(
+                    messages=[
+                        {
+                            "role": "user",
+                            "content": [
+                                {"type": "text", "text": image_captioning_prompt(state)},
+                                {
+                                    "type": "image_url",
+                                    "image_url": {
+                                        "url": f"data:image/jpeg;base64,{state.images[-1]}",
+                                    },
+                                },
+                            ],
+                        }
+                    ],
+                    model=self.captioning_model,
+                    max_completion_tokens=50,
+                    temperature = 1
+                )
+                response=chat_completion.choices[0].message.content
+                state.image_captions.append(response)
+                return state
         
-    )
-    if len(state.preferred_topics)==0:
-        template = initial_story_prompt(state)
-    else:
-        template = refined_story_prompt(state)
-
-
-    messages = [SystemMessage(content=template),
-                HumanMessage(content=f'''The idea of the video is:\n{state.idea}\n'''),
-                FunctionMessage(name='generate_story_function',content=f'''The business details is:\n{state.business_details}\n
-                            The retrieved data of influencers is:\n{state.retrievals[-1]}\n
-                            The information from the image is:\n{state.image_captions[-1]} ''')]
+        else:
+            state.images.append(None)
+            state.image_captions.append(None)
+            return state
     
-    print('Messages:',messages)
-
-    response = react_agent.invoke({'messages':messages})
-    response = response['messages'][-1].content
-    print('The genrated story: ', response)
-    state.stories.append(response)
-    return state
-
-
 
-def generate_brainstroming(state:State)-> State:
-
-  template= brainstroming_prompt(state)
-
-  messages = [SystemMessage(content=template),
-              HumanMessage(content=f'''Here is the story to you for brainstorming:\n{state.stories[-1]}'''),
-              FunctionMessage(content=f'''The details of business is:\n{state.business_details}\n''', name="brainstorm_tool")]
-  print('Message for brainstorming:',messages)
-  response = llm_gpt.with_structured_output(BrainstromTopicFormatter).invoke(messages)
-  response = response.model_dump()
-  state.brainstroming_topics.append(response)
-  print('The brainstroming topics are:',state.brainstroming_topics)
-  return state
+class Retriever:
+    def __init__(self):
+      self.retrievals = []
+    
+    def run(self,state: State) -> State:
+        query_prompt = 'Represent this sentence for searching relevant passages: '
+        if len(state.latest_preferred_topics)==0:
+            for idea in state.idea:  
+                result = Retrieval(idea+query_prompt).influencers_data()
+                self.retrievals.append(result)
+            state.retrievals.append(self.retrievals)
+
+        if len (state.latest_preferred_topics)>0:
+            state.preferred_topics.append(state.latest_preferred_topics)
+            for idea in state.preferred_topics[-1]:  
+                result = Retrieval(idea+query_prompt).influencers_data()
+                self.retrievals.append(result)
+            state.latest_preferred_topics=[]
+            state.retrievals.append(self.retrievals)
+        return state
+
+class StoryGenerator:
+    def __init__(self):
+        self.llm = llm_gpt
+
+    def run(self,state:State)-> State:
+        if len(state.preferred_topics)==0:
+            template = initial_story_prompt(state)
+        else:
+            template = refined_story_prompt(state)
+
+
+        messages = [SystemMessage(content=template),
+                    HumanMessage(content=f'''The idea of the video is:\n{state.idea}\n'''),
+                    FunctionMessage(name='generate_story_function',content=f'''The business details is:\n{state.business_details}\n
+                                The retrieved data of influencers is:\n{state.retrievals[-1]}\n
+                                The information from the image is:\n{state.image_captions[-1]} ''')]
+        
+        response = self.llm.invoke(messages)
+        response = response.content
+        state.stories.append(response)
+        return state
+
+
+class BrainstromTopicGenerator:
+    def __init__(self):
+        self.llm = llm_gpt
+
+    def run(self,state:State)-> State:
+        template= brainstroming_prompt(state)
+
+        messages = [SystemMessage(content=template),
+                    HumanMessage(content=f'''Here is the story to you for brainstorming:\n{state.stories[-1]}'''),
+                    FunctionMessage(content=f'''The details of business is:\n{state.business_details}\n''', name="brainstorm_tool")]
+        print('Message for brainstorming:',messages)
+        response = self.llm.with_structured_output(BrainstromTopicFormatter).invoke(messages)
+        response = response.model_dump()
+        state.brainstroming_topics.append(response)
+        print('The brainstroming topics are:',state.brainstroming_topics)
+        return state
 
 
 

src/genai/brainstroming_agent/utils/prompts.py

@@ -4,7 +4,7 @@ You are a professional prompt engineer for image generation models.
 
 Your task is to read a full scene-by-scene video story and generate a **single, vivid, high-quality visual prompt** that captures the **overall essence and identity** of the video. The goal is to produce an image that would represent the story's core themes, mood, and style as a **display image or visual thumbnail**.
 The scene-by-scene video story will be passed to you by the human.
-🎯 Instructions:
+ Instructions:
 - Summarize the key visual themes, characters, mood, setting, and action from across the entire story.
 - Focus on the most visually iconic or emotionally powerful moment.
 - Avoid generic phrases. Be specific about details like setting, time of day, lighting, colors, characters, and mood.

src/genai/brainstroming_agent/utils/tools.py

@@ -8,46 +8,44 @@ import tiktoken
 from src.genai.utils.load_embeddings import  caption_index , caption_df
 from src.genai.utils.utils import clean_text
 
-def retrieve_tool(video_topic):
-    '''
-    Always invoke this tool.
-    Retrieve influencer's data by semantic search of **video topic**.
-    '''
-    query_embedding = np.array(embedding_model.embed_query(str(video_topic))).reshape(1, -1).astype('float32')
-    faiss.normalize_L2(query_embedding)
-
-    top_k = len(caption_df)
-    distances, indices = caption_index.search(query_embedding, top_k)
-
-    similarity_threshold = 0.35
-    selected = [(idx, sim) for idx, sim in zip(indices[0], distances[0]) if sim >= similarity_threshold]
-
-    if not selected:
-        return "No influencers found."
-
-    # === Format results ===
-    outer_list = []
-    for rank, (idx, sim) in enumerate(selected, 1):
-        row = caption_df.iloc[idx]
-        res = {
-            'rank': rank,
-            'username': row['username'],
-            'visible_text_or_brandings': row['visible_texts_or_brandings'],
-            'likesCount': row['likesCount'],
-            'commentCount': row['commentCount'],
-            'product_or_service_details': row['product_or_service_details'],
-        }
-
-        inner_list = [
-            f"[{res['rank']}]. The influencer name is: **{res['username']}** — Likes: **{res['likesCount']}**, Comments: **{res['commentCount']}**",
-            f"The branding or promotion done is:\n{res['visible_text_or_brandings']}",
-            f"The details of product or service is:\n{res['product_or_service_details']}"
-        ]
-        outer_list.append(inner_list)
-
-    cleaned_response = clean_text(str(outer_list))
-    encoding = tiktoken.encoding_for_model('gpt-4o-mini')
-    tokens = encoding.encode(cleaned_response)
-    trimmed_response = tokens[:1000]
-    return encoding.decode(trimmed_response)
-
+class Retrieval:
+    def __init__(self, video_topic):
+        self.video_topic = video_topic
+        self.query_embedding =  np.array(embedding_model.embed_query(str(self.video_topic))).reshape(1, -1).astype('float32')
+        faiss.normalize_L2(self.query_embedding)
+
+    def influencers_data(self):
+        top_k = len(caption_df)
+        distances, indices = caption_index.search(self.query_embedding, top_k)
+
+        similarity_threshold = 0.35
+        selected = [(idx, sim) for idx, sim in zip(indices[0], distances[0]) if sim >= similarity_threshold]
+
+        if not selected:
+            return "No influencers found."
+
+        outer_list = []
+        for rank, (idx, sim) in enumerate(selected, 1):
+            row = caption_df.iloc[idx]
+            res = {
+                'rank': rank,
+                'username': row['username'],
+                'visible_text_or_brandings': row['visible_texts_or_brandings'],
+                'likesCount': row['likesCount'],
+                'commentCount': row['commentCount'],
+                'product_or_service_details': row['product_or_service_details'],
+            }
+
+            inner_list = [
+                f"[{res['rank']}]. The influencer name is: **{res['username']}** — Likes: **{res['likesCount']}**, Comments: **{res['commentCount']}**",
+                f"The branding or promotion done is:\n{res['visible_text_or_brandings']}",
+                f"The details of product or service is:\n{res['product_or_service_details']}"
+            ]
+            outer_list.append(inner_list)
+
+        cleaned_response = clean_text(str(outer_list))
+        encoding = tiktoken.encoding_for_model('gpt-4o-mini')
+        tokens = encoding.encode(cleaned_response)
+        trimmed_response = tokens[:1000]
+        return encoding.decode(trimmed_response)
+    

src/genai/brainstroming_agent/utils/utils.py

@@ -1,6 +1,6 @@
 
-from langchain_core.messages import SystemMessage, ToolMessage, HumanMessage, FunctionMessage
-from .tools import retrieve_tool 
+from langchain_core.messages import SystemMessage,HumanMessage, FunctionMessage
+from .tools import Retrieval
 import base64
 from PIL import Image
 from io import BytesIO
@@ -9,129 +9,63 @@ from huggingface_hub import InferenceClient
 from .prompts import story_to_prompt , final_story_prompt
 import os
 from langgraph.prebuilt import create_react_agent
-import pandas as pd
-from datasets import load_dataset
-from src.genai.utils.models_loader import llm_gpt
+from src.genai.utils.models_loader import llm_gpt, image_generation_model
 
+class FinalStoryGenenrator:
+    def __init__(self):
+        self.llm = llm_gpt
+        self.agent = create_react_agent(model=llm_gpt,tools=[])
 
+    def generate_final_story(self,final_state):
+        if 'preferred_topics' in final_state:
+            if len(final_state['preferred_topics'])>0:
+                template = final_story_prompt(final_state)
+                messages = [SystemMessage(content=template),
+                            HumanMessage(content=f'''The idea of the video is:\n{final_state['idea']}\n '''),
+                            FunctionMessage(content=f'''The business details is:\n{final_state['business_details']}\nThe data of influencers is:\n{final_state['retrievals'][-1]}''',name='final_story_tool')]
 
-def generate_final_story(final_state):
-    if 'preferred_topics' in final_state:
-        if len(final_state['preferred_topics'])>0:
-            template = final_story_prompt(final_state)
-            messages = [SystemMessage(content=template),
-                        HumanMessage(content=f'''The idea of the video is:\n{final_state['idea']}\n '''),
-                        FunctionMessage(content=f'''The business details is:\n{final_state['business_details']}\nThe data of influencers is:\n{final_state['retrievals'][-1]}''',name='final_story_tool')]
-            print('The message of final story:',messages)
 
-            react_agent=create_react_agent(
-            model=llm_gpt,
-            tools=[])
-
-            for message_chunk , metadata in react_agent.stream({'messages':messages},stream_mode='messages'):
-                yield message_chunk.content
+                for message_chunk , metadata in self.agent.stream({'messages':messages},stream_mode='messages'):
+                    yield message_chunk.content
 
+            else:
+                for chunk in final_state['stories'][-1]:
+                    yield chunk
         else:
-            for chunk in final_state['stories'][-1]:
-                yield chunk
-    else:
-        template = final_story_prompt(final_state)
-        influencers_data = retrieve_tool(final_state)
-        messages = [SystemMessage(content=template),
-                        FunctionMessage(content=f'''The business details is:\n{str(final_state)}\nThe data of influencers is:\n{influencers_data}''',name='final_story_tool')]
-        react_agent=create_react_agent(
-            model=llm_gpt,
-            tools=[])
-        
-        for message_chunk , metadata in react_agent.stream({'messages':messages},stream_mode='messages'):
-            yield message_chunk.content
-
-        
+            template = final_story_prompt(final_state)
+            influencers_data = Retrieval(str(final_state)).influencers_data()
+            messages = [SystemMessage(content=template),
+                            FunctionMessage(content=f'''The business details is:\n{str(final_state)}\nThe data of influencers is:\n{influencers_data}''',name='final_story_tool')]
+            
+            for message_chunk , metadata in self.agent.stream({'messages':messages},stream_mode='messages'):
+                yield message_chunk.content
 
+class ImageGenerator:
+    def __init__(self):
+        self.llm = llm_gpt
+        self.image_generation_model = image_generation_model
+
+    def generate_prompt(self, final_story,business_details,refined_ideation):
+        messages = [SystemMessage(content=story_to_prompt()),
+                    HumanMessage(content=f'''The scene-by-scene video story is {final_story}'''),
+                    FunctionMessage(content=f'''The business details is:\n{business_details}\nThe idea is{refined_ideation}''',name='prompt_generation_id')
+                    ]
+        prompt = self.llm.invoke(messages)
+        return prompt.content
+    
+    def generate_image(self,final_story, business_details, refined_ideation):
+        prompt = self.generate_prompt(final_story, business_details, refined_ideation)
+        client = InferenceClient(provider="hf-inference",api_key=os.environ.get('HUGGINGFACEHUB_ACCESS_TOKEN'))
+        image = client.text_to_image( prompt,model=self.image_generation_model)
+        buffered = BytesIO()
+        image.save(buffered, format="PNG")  
+        buffered.seek(0)
+        img_base64 = base64.b64encode(buffered.read()).decode("utf-8")
+        return img_base64
 
 def encode_image_to_base64(uploaded_file: UploadFile) -> str:
     return base64.b64encode(uploaded_file.file.read()).decode("utf-8")
-  
 
-# Convert base64 string to PIL image (optional for LangGraph processing)
 def process_image(base64_str: str) -> Image.Image:
     image_data = base64.b64decode(base64_str)
     return Image.open(BytesIO(image_data))
-
-
-def generate_prompt(final_story,business_details,refined_ideation):
-    print('************Entering prompt generator****************')
-    messages = [SystemMessage(content=story_to_prompt()),
-                HumanMessage(content=f'''The scene-by-scene video story is {final_story}'''),
-                FunctionMessage(content=f'''The business details is:\n{business_details}\nThe idea is{refined_ideation}''',name='prompt_generation_id')
-                ]
-
-    prompt = llm_gpt.invoke(messages)
-    print('The prompt is:',prompt)
-    return prompt.content
-
-def generate_image(final_story, business_details, refined_ideation):
-    prompt = generate_prompt(final_story, business_details, refined_ideation)
-    print('************Finished prompt generator****************')
-
-    client = InferenceClient(
-        provider="hf-inference",
-        api_key=os.environ.get('HUGGINGFACEHUB_ACCESS_TOKEN'),
-    )
-
-    print('************Finished calling generator****************')
-
-
-        # output is a PIL.Image object
-    image = client.text_to_image(
-        prompt,
-        model="black-forest-labs/FLUX.1-schnell",
-    )
-    print('*****************Image Created*******************')
-
-    # Convert image to BytesIO buffer
-    buffered = BytesIO()
-    image.save(buffered, format="PNG")  # you can also use "JPEG" if preferred
-    buffered.seek(0)
-
-    # Encode to base64 string
-    img_base64 = base64.b64encode(buffered.read()).decode("utf-8")
-    print('*****************Image Encoded to Base64*******************')
-
-    return img_base64
-
-
-def save_to_db(business_details):
-    dataset = load_dataset("subashdvorak/tiktok-agentic-story")['train']
-    # dataset = load_influencer_data()
-    df = pd.DataFrame(dataset)
-
-    # 2. Flatten all business detail values to a set of lowercase strings
-    all_values = set()
-    for v in business_details.values():
-        if isinstance(v, str):
-            all_values.add(v.lower())
-        elif isinstance(v, list):
-            all_values.update(map(str.lower, map(str, v)))
-
-    # 3. Match rows where ANY column contains ANY of the values
-    def row_matches(row):
-        return any(
-            str(cell).lower().find(val) != -1
-            for cell in row
-            for val in all_values
-        )
-
-    # 4. Apply row-wise matching
-    matched_df = df[df.apply(row_matches, axis=1)]
-    matched_df.to_csv('extracted_data.csv')
-
-
-
-
-    
-
-
-
-
-

src/genai/context_analysis_agent/agent.py

@@ -2,22 +2,23 @@ from langchain_groq import ChatGroq
 from langgraph.graph import StateGraph, MessagesState, START, END
 from langgraph.checkpoint.memory import MemorySaver
 from .utils.state import State 
-from .utils.nodes import introduction_node, extract_business_details
-from src.genai.utils.models_loader import llm
+from .utils.nodes import IntroductionNode
+from .utils.utils import DetailsExtractor
+from src.genai.utils.models_loader import llm_gpt
 
 business_state = State()
 
 class IntroductionChatbot:
     def __init__(self):
         self.memory = MemorySaver()
-        self.llm = llm
+        self.llm = llm_gpt
         self.workflow = self._initialize_workflow()
         self.interact_agent = self.workflow.compile(checkpointer=self.memory)
         self.messages = []
 
     def _initialize_workflow(self):
         workflow = StateGraph(MessagesState)
-        workflow.add_node("chatbot", lambda state: introduction_node(state, self.llm))
+        workflow.add_node("chatbot", lambda state: IntroductionNode().run(state, self.llm))
         workflow.add_edge(START, "chatbot")
         workflow.add_edge("chatbot", END)
         return workflow
@@ -37,7 +38,7 @@ class IntroductionChatbot:
         return "Thanks for providing all your required business details" in latest_response
 
     def extract_details(self):
-        response = extract_business_details(business_state.interactions)
+        response = DetailsExtractor().run(business_state.interactions)
         print('Extracted details:', response)
         return response
     

src/genai/context_analysis_agent/utils/nodes.py

@@ -1,21 +1,19 @@
+from .state import DetailsFormatter
 from langchain_core.messages import SystemMessage
-from .prompts import introduction_prompt, details_extract_prompt
-from .state import State , DetailsFormatter
-from src.genai.utils.models_loader import llm
+from src.genai.utils.models_loader import llm_gpt
+from .prompts import introduction_prompt
+
+class IntroductionNode:
+    def __init__(self):
+        self.llm = llm_gpt
+
+    def run(self, state, llm):
+        template = introduction_prompt
+        messages = [SystemMessage(content=template)] + state["messages"]
+        response = llm.invoke(messages)
+        return {"messages": [response]}
 
-# This node generates chatbot responses using the LLM
-def introduction_node(state, llm):
-    template = introduction_prompt
-    messages = [SystemMessage(content=template)] + state["messages"]
-    response = llm.invoke(messages)
-    return {"messages": [response]}
 
-# This function extracts structured business details
-def extract_business_details(interactions):
-    template = details_extract_prompt(interactions)
-    messages = [SystemMessage(content=template)]
-    response=llm.with_structured_output(DetailsFormatter).invoke(messages)
-    return response
 
     
     

src/genai/context_analysis_agent/utils/utils.py

@@ -1,29 +1,47 @@
-# from utils.data_loader import load_dataset
 import pandas as pd
-from utils.data_loader import load_influencer_data
-
-def save_to_db(business_details):
-    # dataset = load_dataset("subashdvorak/tiktok-agentic-story")['train']
-    dataset = load_influencer_data()
-    df = pd.DataFrame(dataset)
-
-    # 2. Flatten all business detail values to a set of lowercase strings
-    all_values = set()
-    for v in business_details.values():
-        if isinstance(v, str):
-            all_values.add(v.lower())
-        elif isinstance(v, list):
-            all_values.update(map(str.lower, map(str, v)))
-
-    # 3. Match rows where ANY column contains ANY of the values
-    def row_matches(row):
+from src.genai.utils.load_embeddings import caption_df
+from src.genai.utils.models_loader import llm_gpt
+from .prompts import details_extract_prompt
+from langchain_core.messages import SystemMessage
+from .state import DetailsFormatter
+
+class DetailsExtractor:
+    def __init__(self):
+        self.llm = llm_gpt
+
+    def run(interactions):
+        template = details_extract_prompt(interactions)
+        messages = [SystemMessage(content=template)]
+        response=llm_gpt.with_structured_output(DetailsFormatter).invoke(messages)
+        return response
+
+class SaveToDB:
+    def __init__(self, caption_df):
+        self.df = caption_df.drop(columns=['embeddings'], errors='ignore')
+
+    def _prepare_values(self, business_details):
+        """Extract lowercase string values from business_details dict."""
+        all_values = set()
+        for v in business_details.values():
+            if isinstance(v, str):
+                all_values.add(v.lower())
+            elif isinstance(v, list):
+                all_values.update(map(str.lower, map(str, v)))
+        return all_values
+
+    def _row_matches(self, row, all_values):
+        """Check if any value in all_values exists in the row."""
         return any(
             str(cell).lower().find(val) != -1
             for cell in row
             for val in all_values
         )
 
-    # 4. Apply row-wise matching
-    matched_df = df[df.apply(row_matches, axis=1)]
-    matched_df.to_csv('extracted_data.csv')
-    print('Dataset updated according to business')
+    def save_to_csv(self, business_details, output_file='extracted_data.csv'):
+        """Filter dataframe rows based on business_details and save to CSV."""
+        all_values = self._prepare_values(business_details)
+        matched_df = self.df[self.df.apply(self._row_matches, axis=1, args=(all_values,))]
+        matched_df.to_csv(output_file, index=False)
+
+
+

src/genai/ideation_agent/utils/prompts.py

@@ -1,8 +1,105 @@
 
+def ideator_prompt(state):
+    return f"""
+You are a **precision-focused, culturally-attuned video ideator** trusted by top global brands to craft **crisp, reference-grounded, and high-impact video concepts** for social platforms like TikTok, Instagram, and YouTube.
 
+Your task is to create **exactly 4 highly original video ideas**, each in **exactly 40 words**.  
+These are not scripts or taglines — they are **powerful conceptual seeds**: short, visual, emotionally charged ideas that can spark full videos.  
 
+---
 
-def ideator_prompt(state):
+### STRICT CREATIVE SOURCE:
+You are provided **10 reference creative ideas** retrieved from IMDb movie descriptions.  
+These are your **ONLY valid creative source**.  
+- You MUST carefully read, analyze, and mix elements from these 10 ideas.  
+- Do not invent entirely new storylines beyond the styles, tones, emotions, and motifs in the IMDb ideas.  
+- All creativity must come from **blending, reimagining, and recombining** parts of the 10 reference ideas.  
+- Avoid hallucinating scenes or concepts that are unrelated to the references.
+
+---
+
+### Output Rules:
+- **4 final ideas** only.  
+- Each idea must be **exactly 40 words**.  
+- Each idea must be **very different** from the others in plot, tone, and visual feel.  
+- Begin from an **unexpected or abstract scene**, but base it on the IMDb inspirations.  
+- Conclude each idea in a way that subtly connects to the **business details** below.
+- Avoid repeating characters, settings, or themes across the 4 ideas.
+- Must be cinematic, visually vivid, and emotionally engaging.
+
+---
+
+### Business Details:
+{state.business_details[-1]}
+
+---
+
+Now, based strictly on the 10 IMDb ideas provided to you as your creative dataset —  
+**Analyze them, identify their story arcs, emotions, symbols, and pacing, then mix and adapt them into 4 completely original yet reference-rooted 40-word video concepts**.  
+Do not draw from anything outside the provided IMDb ideas.  
+Every idea must end in a subtle, creative tie-in to the business details.
+"""
+
+def critic_prompt(state):
+    return f"""
+You are a **precision-focused, detail-oriented video idea critic**. You’ve been assigned to **critique 4 video ideas** created by another ideator and then **refine or improve them strictly based on reference material provided**.
+
+---
+
+### STRICT CREATIVE SOURCE:
+You are provided **10 reference creative ideas** retrieved from IMDb movie descriptions.  
+These are your **ONLY valid creative source**.  
+- All critique and improvements must be grounded in the tone, emotional beats, plot devices, and styles from these IMDb ideas.  
+- You may mix or adapt elements from multiple IMDb ideas, but you must not invent unrelated concepts.  
+- No hallucinations or off-reference scenarios.
+
+If **valid image information** is provided, it must strongly guide your improvements, as it represents the reference style the user wants.
+
+---
+
+### Your job:
+1. **Identify flaws collectively** across the 4 original ideas — repetition, weak twist, dull opening, poor link to business details, lack of diversity, etc.
+2. **Improve or replace** each idea while keeping it aligned with the IMDb references, ensuring strong cinematic depth and emotional impact.
+3. Maintain the **original’s core** if it’s already good, but refine for clarity, pacing, or strength where possible.
+
+---
+
+### Very Important Creative Guidelines:
+- **Each improved idea must be exactly 40 words**.  
+- Begin with an **unexpected or abstract plot, character, or visual scene** (inspired from IMDb references) — never directly with the business context.  
+- Conclude each idea with a **creative and subtle tie-in** to the business details below.  
+- The 4 improved ideas must be **completely diverse** in theme, tone, and visual feel.  
+- Avoid repeating characters, locations, or similar situations across the 4 ideas.  
+- Must be cinematic, visually vivid, emotionally resonant, and platform-native (Instagram/TikTok/YouTube).  
+- Improvements must be **strictly reference-based** — no unrelated creativity.  
+
+---
+
+### Business Details:
+{state.business_details[-1]}
+
+### Original Ideas by Ideator:
+{state.ideator_response[-1]}
+
+---
+
+**Format your response like this:**
+
+---
+Faults: Faults in any of the 4 ideas of ideator  
+
+---
+Improved Ideas from Critic:  
+**improved_first idea of 40 words**  
+**improved_second idea of 40 words**  
+**improved_third idea of 40 words**  
+**improved_fourth idea of 40 words**  
+
+---
+"""
+
+
+def ideator_prompt_old(state):
     return f"""
 You are a **bold, imaginative, and culturally-attuned video ideator** trusted by top global brands to craft **crisp, original, and high-impact video concepts** for social platforms like TikTok, Instagram, and YouTube.
 
@@ -67,7 +164,7 @@ Now, based on the business details above **and inspired by the 10 IMDb reference
 
 
 
-def critic_prompt(state):
+def critic_prompt_old(state):
     return f"""
 You are a sharp, imaginative, and detail-oriented **video idea critic**. You’ve been assigned to **critique 4 video ideas** created by another ideator and then **refine or improve** them.
 If some **valid and usuable information**  of image is provided to you by the user, you have to **strongly focus** in that information of image because that is the reference of what type of idea does the user wants.

src/genai/utils/models_loader.py

@@ -58,6 +58,7 @@ ideator_llm = llm_anthropic
 critic_llm = llm_anthropic
 validator_llm = llm_anthropic
 
-
+captioning_model = "meta-llama/llama-4-scout-17b-16e-instruct"
+image_generation_model = "black-forest-labs/FLUX.1-schnell"