Completed response rendering between endpoints

.gitignore

@@ -1,4 +1,5 @@
 myenv
 .env
 static
-templates
+templates
+main_demo.py

main.py

@@ -5,89 +5,103 @@ from my_agent.agent import build_graph
 import pandas as pd
 from typing import Optional , List
 from my_agent.utils.initial_interaction import BusinessInteractionChatbot
-import base64
-from PIL import Image
-from io import BytesIO
+from my_agent.utils.utils import encode_image_to_base64 , generate_final_story 
+
 import json
+from my_agent.utils.nodes import generate_final_story
+from collections import defaultdict
+
+# Store brainstorming results per thread_id
 
 app = FastAPI()
 interaction_chatbot = BusinessInteractionChatbot()
 graph = build_graph()
 
 
-
-
+stored_data={}
 
 class UserMessage(BaseModel):
     message: str
-details_for_brainstrom = {}
 @app.post("/business-interaction")
 def business_chat(msg: UserMessage):
-    global details_for_brainstrom
     response = interaction_chatbot.chat(msg.message)
     if interaction_chatbot.is_complete(response):
         details = interaction_chatbot.extract_details()
-        details_for_brainstrom = details
+        stored_data['business_details'] = details
         return {"response": response, "business_details": details, "complete": True}
     return {"response": response, "complete": False}
 
 
 
-# class RequestInput(BaseModel):
-#     query: list 
-#     preferred_topics: Optional[list] = [] 
-#     images: Optional[list[str]] = []  # base64-encoded image strings
-
-# @app.post("/brainstrom")
-# def run_graph(input_data: RequestInput):
-#     image_objects = []
-#     for img_b64 in input_data.images:
-#         image_objects.append(process_image(img_b64))  # decode and load images
-
-#     result = graph.invoke({
-#         'topic': input_data.query,
-#         'images': image_objects,
-#         'business_details': details_for_brainstrom
-#     })
-
-#     return {
-#         'final_story': result['final_story'],
-#         'business_details': result['business_details'],
-#     }
 
 
 
-
-# Convert uploaded image to base64 string
-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))
-
 @app.post("/brainstrom")
-async def run_graph(
+def run_graph(
     query: List[str],  # sent as JSON body
     preferred_topics: Optional[list] = [],
     images: Optional[List[UploadFile]] = [],  # ✅ Optional UploadFile list
-    thread_id: Optional[str] = "default-session"
+    thread_id: Optional[str] = "default-session",
 ):
     # Convert uploaded images to base64
     image_base64_list = [encode_image_to_base64(img) for img in images]
 
-    # Convert base64 to image objects (if LangGraph expects PIL.Image)
-    image_objects = [process_image(img_b64) for img_b64 in image_base64_list]
-
     # Invoke LangGraph
     result = graph.invoke({
         'topic': query,
         'images': image_base64_list,
-        'latest_preferred_topics':preferred_topics
+        'latest_preferred_topics':preferred_topics,
+        'business_details': stored_data['business_details']
     },
     config={"configurable": {"thread_id": thread_id}})
+    stored_data['brainstroming_response']=result
+    # brainstorm_store[thread_id] = result
 
     return {
         'response': result,
     }
+
+
+
+
+@app.post("/generate-final-story")
+def generate_final_story_endpoint(
+):
+    final_story =   generate_final_story(stored_data["brainstroming_response"])
+    return {
+        'response': final_story
+    }
+    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

my_agent/utils/nodes.py

@@ -193,14 +193,14 @@ def route_after_selection(state:State):
   elif len(state.latest_preferred_topics)>0:
     return True
 
-def generate_final_story(state:State)-> State:
-    if len(state.preferred_topics)>0:
+def generate_final_story(query):
+    if len(query['preferred_topics'])>0:
         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.
-      You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{state.retrievals[-1]}.
-      \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{state.topic}\n\n
+      You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{query['retrievals'][-1]}.
+      \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{query['topic']}\n\n
 
-      **Final Reminder** You have to strongly focus on these topics while creating the storyline: {[item for sublist in state.preferred_topics for item in sublist]}'''
+      **Final Reminder** You have to strongly focus on these topics while creating the storyline: {[item for sublist in query['preferred_topics'] for item in sublist]}'''
         messages = [SystemMessage(content=template)]
         response = llm.bind_tools([StoryFormatter]).invoke(messages)
         print('The final response is:',response)
@@ -210,13 +210,12 @@ def generate_final_story(state:State)-> State:
             response = response.content
         else:
           response = "No response"
-        state.final_story.append(response)
-        state.stories.append(response)
-        return state
+        # state.final_story.append(response)
+        # state.stories.append(response)
+        return response
     
-    state.final_story.append(state.stories[-1])
-    state.latest_preferred_topics=[]
-    return state
+    else:
+        return query['stories'][-1]
 
 
 

my_agent/utils/utils.py

@@ -0,0 +1,42 @@
+
+from langchain_core.messages import SystemMessage
+from .tools import StoryFormatter 
+from .models_loader import llm
+import base64
+from PIL import Image
+from io import BytesIO
+from fastapi import UploadFile
+def generate_final_story(query):
+    if len(query['preferred_topics'])>0:
+        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.
+      You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{query['retrievals'][-1]}.
+      \n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{query['topic']}\n\n
+
+      **Final Reminder** You have to strongly focus on these topics while creating the storyline: {[item for sublist in query['preferred_topics'] for item in sublist]}'''
+        messages = [SystemMessage(content=template)]
+        response = llm.bind_tools([StoryFormatter]).invoke(messages)
+        print('The final response is:',response)
+        if hasattr(response, 'tool_calls') and response.tool_calls:
+            response = response.tool_calls[0]['args']
+        elif hasattr(response, 'content'):
+            response = response.content
+        else:
+          response = "No response"
+        # state.final_story.append(response)
+        # state.stories.append(response)
+        return response
+    
+    else:
+        return query['stories'][-1]
+
+
+
+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))