Implemented the analytics too in orchestrator

api/routers/analytics_chatbot.py

@@ -15,15 +15,13 @@ router = APIRouter()
 agent=ChatbotAgent()
 graph = agent.chatbot_graph()  
 
-@router.post("/analytics-chatbot")
-def context_analysis(msg: UserMessage):
-    user_query=process_query(msg.message)
+@router.get("/analytics-chatbot")
+def get_analytics(msg:str):
+    user_query=process_query(msg)
     config={"configurable": {"thread_id": "analytics-chatbot-thread"}}
     try:
         result=graph.invoke({'messages':user_query},config=config)
         return {
-            'endpoint': result['endpoint'] ,
-            'parameters': result['parameters'],
             'response': result['response'],
         }
     except Exception as e:

api/routers/generate_final_story.py

@@ -8,7 +8,7 @@ router= APIRouter()
 app_logger = logging.getLogger("app_logger")
 error_logger = logging.getLogger("error_logger")
 
-@router.post("/generate-final-story")
+@router.get("/generate-final-story")
 def generate_final_story_endpoint():
     try:
         def event_stream():

api/routers/generate_image.py

@@ -8,7 +8,7 @@ router = APIRouter()
 app_logger = logging.getLogger("app_logger")
 error_logger = logging.getLogger("error_logger")
 
-@router.post("/generate-image") 
+@router.get("/generate-image") 
 def generate_image_endpoint():
     try:
         image = ImageGenerator().generate_image(str(stored_data.get('final_story',image_gen_backup_prompt))

api/routers/human_idea_refining.py

@@ -9,16 +9,16 @@ human_refine_graph = human_refined_idea()
 app_logger = logging.getLogger("app_logger")
 error_logger = logging.getLogger("error_logger")
 
-@router.post("/human-idea-refining")
-def human_idea_refine_endpoint(request:RefineIdeationRequest):
+@router.get("/human-idea-refining")
+def human_idea_refine_endpoint(query:str):
     try:
-        stored_data['human_ideation_interactions'].append({"role": "user", "content": request.query})
+        stored_data['human_ideation_interactions'].append({"role": "user", "content": 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}}
+            },config={"configurable": {"thread_id": "human-idea-refine-thread"}}
         )
         stored_data['human_ideation_interactions'].append({"role": "assistant", "content": response['result']})
         stored_data['refined_ideation'] = stored_data['human_ideation_interactions'][-1]['content']

api/routers/orchestration.py

@@ -2,28 +2,34 @@ import logging
 from fastapi import APIRouter
 from api.stored_data import stored_data
 from src.genai.orchestration_agent.agent import OrchestrationAgent
+# from src.genai.orchestration_agent.utils.utils import handle_tools
 from api.schemas.orchestration import OrchestrationRequest
+from check import handle_tools
 
 app_logger = logging.getLogger("app_logger")
 error_logger = logging.getLogger("error_logger")
 
 router= APIRouter()
 agent = OrchestrationAgent()
+graph = agent.orchestration_graph()
 
 @router.post("/orchestration", description="Gives the analytics of influencers.")
 def orchestration_endpoint(request:OrchestrationRequest):
     try:
-        result = agent.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
+        config={"configurable": {"thread_id": "orchestration-thread"}}
+        result = graph.invoke({'messages':[request.message],
+                               'image_base64': request.image_base64}, config=config)
+        print('Result tools:', result['tools'])
+        tools=handle_tools(result['tools'], stored_data)
+        print('Tools:',tools)
+        if result['image_caption'] is not None: 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']
         app_logger.info('Orchestrator executed')
             
-        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}
+        return {'tool_response':tools,
+                'message_response': result['query_response'],}
+
     except Exception as e:
         error_logger.error(f'Unable to run orchestration: {e}')
         raise 

api/routers/show_analytics.py

@@ -7,7 +7,7 @@ app_logger = logging.getLogger("app_logger")
 error_logger = logging.getLogger("error_logger")
 
 router=APIRouter()
-@router.post("/show-analytics")
+@router.get("/show-analytics")
 def show_analytics_endpoint():
     try:
         response = AnalyticsViewer(stored_data['business_details']).show_analytics()

check.py

@@ -1,28 +1,9 @@
-import requests
+state = {}
+state['tools']= ['ideation', 'generate-story']
+base_url = 'http://127.0.0.1:8000/api/'
 
-# Define the endpoint URL
-url = "https://reveltrends.vercel.app/api/v1/overview/buzz_trend"
+for tool in state['tools']:
+    url = f'''{base_url}{tool}'''
+    print('The url is:', url)
 
-# Set the parameters
-params = {
-    "influencer_username": "divyadhakal_",
-    "period": "weekly"
-}
-
-# Optional: headers if your API requires authentication
-headers = {
-    "Authorization": "Bearer YOUR_API_KEY",  # replace with your API key if needed
-    "Content-Type": "application/json"
-}
-
-# Make the GET request
-response = requests.get(url, params=params, headers=headers)
-
-# Check if the request was successful
-if response.status_code == 200:
-    data = response.json()
-    print("Response data:", data)
-else:
-    print("Request failed with status code:", response.status_code)
-    print("Response:", response.text)
 

logs/access.log

@@ -952,3 +952,198 @@
 2025-09-14 17:44:12,219 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
 2025-09-14 17:45:22,641 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/analytics-chatbot
 2025-09-14 17:45:29,855 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 11:50:06,693 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/analytics-chatbot
+2025-09-15 11:50:06,694 | INFO | access_logger | api/main.py:21 | Response status: 404
+2025-09-15 11:50:56,104 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 11:50:57,898 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 11:52:47,352 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 11:52:48,299 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 11:53:07,927 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 11:53:09,685 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 11:53:47,286 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 11:53:49,010 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 11:54:59,228 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 11:55:01,098 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 11:55:11,270 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 11:55:12,882 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 11:58:40,712 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 11:58:42,390 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 11:59:07,842 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 11:59:09,628 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 12:07:35,403 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 12:07:42,354 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 12:08:33,997 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 12:08:35,706 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 12:08:51,470 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
+2025-09-15 12:08:53,422 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-15 12:09:35,825 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
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+2025-09-15 12:34:44,250 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
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+2025-09-15 12:35:07,580 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
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+2025-09-15 13:58:13,346 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
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+2025-09-15 14:06:50,566 | INFO | access_logger | api/main.py:19 | Request: POST http://localhost:8000/api/analytics-chatbot
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+2025-09-18 23:26:31,140 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/favicon.ico
+2025-09-18 23:26:31,140 | INFO | access_logger | api/main.py:21 | Response status: 404
+2025-09-18 23:26:34,806 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-09-18 23:26:34,807 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-18 23:26:35,085 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-09-18 23:26:35,093 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-18 23:26:46,994 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:28:05,982 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:34:11,812 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:38:47,616 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:38:48,527 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-18 23:40:12,179 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:40:24,570 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:40:42,113 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:53:07,604 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:54:16,252 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:54:16,670 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-18 23:56:47,087 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:59:35,816 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-18 23:59:40,126 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 00:03:23,846 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-19 00:03:24,241 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 00:04:47,367 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-19 00:04:47,830 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 00:05:40,499 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-19 00:05:40,809 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 00:06:25,132 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-19 00:06:25,785 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 00:09:13,114 | INFO | access_logger | api/main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-09-19 00:09:13,638 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:50:09,389 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/
+2025-09-19 12:50:09,389 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:50:09,390 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/
+2025-09-19 12:50:09,391 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:50:09,435 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/favicon.ico
+2025-09-19 12:50:09,436 | INFO | access_logger | api/main.py:21 | Response status: 404
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+2025-09-19 12:50:12,550 | INFO | access_logger | api/main.py:21 | Response status: 200
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+2025-09-19 12:50:12,634 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:52:52,297 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-09-19 12:52:52,298 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:52:52,338 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-09-19 12:52:52,345 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:52:53,490 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/
+2025-09-19 12:52:53,492 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:52:56,202 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-09-19 12:52:56,203 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:52:56,238 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-09-19 12:52:56,238 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 12:53:25,025 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=I%20want%20the%20sentiment%20distribution%20of%20divya%20dhakal
+2025-09-19 12:54:02,309 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/api/analytics-chatbot?msg=I%20want%20the%20sentiment%20distribution%20of%20divya%20dhakal
+2025-09-19 12:54:09,594 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 13:01:22,631 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-09-19 13:01:22,632 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 13:01:22,672 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-09-19 13:01:22,678 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 13:03:36,189 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-09-19 13:03:36,190 | INFO | access_logger | api/main.py:21 | Response status: 200
+2025-09-19 13:03:36,231 | INFO | access_logger | api/main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-09-19 13:03:36,237 | INFO | access_logger | api/main.py:21 | Response status: 200

logs/app.log

@@ -110,3 +110,12 @@
 2025-09-14 13:43:49,289 | INFO | app_logger | api/routers/ideation.py:33 | Executed the ideation pipeline.
 2025-09-14 13:46:03,905 | INFO | app_logger | api/routers/ideation.py:33 | Executed the ideation pipeline.
 2025-09-14 13:48:33,762 | INFO | app_logger | api/routers/ideation.py:33 | Executed the ideation pipeline.
+2025-09-18 23:38:48,525 | INFO | app_logger | api/routers/orchestration.py:26 | Orchestrator executed
+2025-09-18 23:54:16,668 | INFO | app_logger | api/routers/orchestration.py:26 | Orchestrator executed
+2025-09-18 23:56:47,468 | INFO | app_logger | api/routers/orchestration.py:26 | Orchestrator executed
+2025-09-18 23:59:40,124 | INFO | app_logger | api/routers/orchestration.py:26 | Orchestrator executed
+2025-09-19 00:03:24,239 | INFO | app_logger | api/routers/orchestration.py:28 | Orchestrator executed
+2025-09-19 00:04:47,828 | INFO | app_logger | api/routers/orchestration.py:28 | Orchestrator executed
+2025-09-19 00:05:40,808 | INFO | app_logger | api/routers/orchestration.py:28 | Orchestrator executed
+2025-09-19 00:06:25,784 | INFO | app_logger | api/routers/orchestration.py:28 | Orchestrator executed
+2025-09-19 00:09:13,637 | INFO | app_logger | api/routers/orchestration.py:28 | Orchestrator executed

logs/errors.log

@@ -40,3 +40,37 @@ unique_selected_ideas.0
 unique_selected_ideas.0
   Input should be a valid dictionary [type=dict_type, input_value=[{'hook': 'Can my gear su...with special effects.'}], input_type=list]
     For further information visit https://errors.pydantic.dev/2.11/v/dict_type
+2025-09-17 16:34:43,839 | ERROR | error_logger | api/routers/orchestration.py:28 | Unable to run orchestration: Error code: 400 - {'error': {'message': "tool call validation failed: parameters for tool ToolResponseFormatter did not match schema: errors: [missing properties: 'tool', 'query_response']", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=ToolResponseFormatter> {"name": "analytics", "tools": ["analytics"]} </function>'}}
+2025-09-17 16:40:32,718 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "tool call validation failed: parameters for tool ToolResponseFormatter did not match schema: errors: [missing properties: 'tool']", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=ToolResponseFormatter> {"name": "Influencer Divya dhakal has got 66 likes.", "tools": ["analytics"], "query_response": "Acknowledging your request to get information about influencer Divya dhakal."}</function>'}}
+2025-09-17 16:46:59,118 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "tool call validation failed: parameters for tool ToolResponseFormatter did not match schema: errors: [missing properties: 'tool']", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=ToolResponseFormatter> {"name": "tools", "tools": [], "query_response": "Hello! I\'m here to help you with generating content ideas, stories, and visuals. How can I assist you today?", "influencer_data": {"name": "Divya Dhakal", "likes": 66}} </function>'}}
+2025-09-17 16:49:17,668 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "tool call validation failed: parameters for tool ToolResponseFormatter did not match schema: errors: [missing properties: 'tool']", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=ToolResponseFormatter> {"name": "Divya Dhakal", "likes": 66, "query_response": "Acknowledging your request. No tools required for this query."}</function>'}}
+2025-09-17 16:50:47,677 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "tool call validation failed: parameters for tool ToolResponseFormatter did not match schema: errors: [missing properties: 'tool']", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=ToolResponseFormatter> {"name": "tools", "tools": [], "query_response": "Hello! I\'m here to help you with generating content ideas, stories, and visuals. How can I assist you today?", "influencer_data": {"name": "Divya Dhakal", "likes": 66}} </function>'}}
+2025-09-17 16:52:54,768 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Failed to call a function. Please adjust your prompt. See 'failed_generation' for more details.", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{}<function>'}}
+2025-09-17 16:53:07,622 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Failed to call a function. Please adjust your prompt. See 'failed_generation' for more details.", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{}<function>'}}
+2025-09-17 16:53:15,451 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Failed to call a function. Please adjust your prompt. See 'failed_generation' for more details.", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{}<function>'}}
+2025-09-17 16:53:17,253 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Failed to call a function. Please adjust your prompt. See 'failed_generation' for more details.", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{}<function>'}}
+2025-09-17 16:53:18,990 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Failed to call a function. Please adjust your prompt. See 'failed_generation' for more details.", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{}<function>'}}
+2025-09-17 16:53:28,546 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Failed to call a function. Please adjust your prompt. See 'failed_generation' for more details.", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{}<function>'}}
+2025-09-17 16:56:55,386 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Failed to call a function. Please adjust your prompt. See 'failed_generation' for more details.", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{}<function>'}}
+2025-09-17 16:57:59,301 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': 'Tool choice is required, but model did not call a tool', 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{\n  "tools": [],\n  "query_response": "Hello! How can I assist you today? 😊"\n}'}}
+2025-09-17 16:58:58,690 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Tool call validation failed: tool call validation failed: attempted to call tool 'tools' which was not in request.tools", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{"name": "tools", "arguments": {"tools": []}}'}}
+2025-09-17 16:59:42,521 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Tool call validation failed: tool call validation failed: attempted to call tool 'commentary' which was not in request.tools", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{"name": "commentary", "arguments": {"tools": []}}'}}
+2025-09-17 17:05:10,589 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Tool call validation failed: tool call validation failed: attempted to call tool 'json' which was not in request.tools", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{"name": "json", "arguments": {\n  "tools": []\n}}'}}
+2025-09-17 17:06:33,147 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Tool call validation failed: tool call validation failed: attempted to call tool 'json' which was not in request.tools", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{"name": "json", "arguments": {\n  "tools": []\n}}'}}
+2025-09-17 17:08:19,208 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "'messages' : minimum number of items is 1", 'type': 'invalid_request_error'}}
+2025-09-17 17:13:47,203 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "'messages' : minimum number of items is 1", 'type': 'invalid_request_error'}}
+2025-09-17 17:15:08,322 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "'messages' : minimum number of items is 1", 'type': 'invalid_request_error'}}
+2025-09-17 17:17:51,001 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "'messages' : minimum number of items is 1", 'type': 'invalid_request_error'}}
+2025-09-17 17:20:09,690 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "'messages' : minimum number of items is 1", 'type': 'invalid_request_error'}}
+2025-09-17 17:24:27,652 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': 'tool call validation failed: parameters for tool UserReferenceResponseFormatter did not match schema: errors: [`/video_idea`: expected string, but got null, `/video_story`: expected string, but got null]', 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{"video_idea": null, "video_story": null}</function>'}}
+2025-09-17 17:25:35,033 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': 'Tool call validation failed: tool call validation failed: parameters for tool UserReferenceResponseFormatter did not match schema: errors: [`/video_idea`: expected string, but got null, `/video_story`: expected string, but got null]', 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{"name": "UserReferenceResponseFormatter", "arguments": {\n  "video_idea": null,\n  "video_story": null\n}}'}}
+2025-09-17 17:26:29,098 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': 'Tool choice is required, but model did not call a tool', 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{\n  "tools": ["ideation"],\n  "query_response": "Great! Let’s brainstorm some fresh video ideas tailored to your business. Let me know a bit about your industry or target audience, and I’ll start generating concepts for you."\n}'}}
+2025-09-17 17:26:56,020 | ERROR | error_logger | api/routers/orchestration.py:30 | Unable to run orchestration: Error code: 400 - {'error': {'message': "Tool call validation failed: tool call validation failed: attempted to call tool 'json' which was not in request.tools", 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{"name": "json", "arguments": {\n  "tools": ["ideation"],\n  "query_response": "Great! Let’s brainstorm some fresh video ideas tailored to your business. Let me know a bit about your industry, target audience, and any themes you have in mind, and I’ll generate some creative concepts for you."\n}}'}}
+2025-09-18 23:26:46,999 | ERROR | error_logger | api/routers/orchestration.py:31 | Unable to run orchestration: Checkpointer requires one or more of the following 'configurable' keys: thread_id, checkpoint_ns, checkpoint_id
+2025-09-18 23:28:06,546 | ERROR | error_logger | api/routers/orchestration.py:32 | Unable to run orchestration: Error code: 401 - {'error': {'message': 'Invalid API Key', 'type': 'invalid_request_error', 'code': 'invalid_api_key'}}
+2025-09-18 23:34:12,324 | ERROR | error_logger | api/routers/orchestration.py:32 | Unable to run orchestration: Error code: 400 - {'error': {'message': 'Tool call validation failed: tool call validation failed: parameters for tool UserReferenceResponseFormatter did not match schema: errors: [`/video_idea`: expected string, but got null, `/video_story`: expected string, but got null]', 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '{"name": "UserReferenceResponseFormatter", "arguments": {\n  "video_idea": null,\n  "video_story": null\n}}'}}
+2025-09-18 23:40:12,666 | ERROR | error_logger | api/routers/orchestration.py:32 | Unable to run orchestration: Error code: 400 - {'error': {'message': 'tool call validation failed: parameters for tool UserReferenceResponseFormatter did not match schema: errors: [`/video_story`: expected string, but got null, `/video_idea`: expected string, but got null]', 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{"video_idea": null, "video_story": null}</function>'}}
+2025-09-18 23:40:24,844 | ERROR | error_logger | api/routers/orchestration.py:32 | Unable to run orchestration: Error code: 400 - {'error': {'message': 'tool call validation failed: parameters for tool UserReferenceResponseFormatter did not match schema: errors: [`/video_idea`: expected string, but got null, `/video_story`: expected string, but got null]', 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{"video_idea": null, "video_story": null}</function>'}}
+2025-09-18 23:40:42,373 | ERROR | error_logger | api/routers/orchestration.py:32 | Unable to run orchestration: Error code: 400 - {'error': {'message': 'tool call validation failed: parameters for tool UserReferenceResponseFormatter did not match schema: errors: [`/video_idea`: expected string, but got null, `/video_story`: expected string, but got null]', 'type': 'invalid_request_error', 'code': 'tool_use_failed', 'failed_generation': '<function=UserReferenceResponseFormatter>{"video_idea": null, "video_story": null}</function>'}}
+2025-09-18 23:53:08,173 | ERROR | error_logger | api/routers/orchestration.py:32 | Unable to run orchestration: 'video_idea'
+2025-09-18 23:56:47,469 | ERROR | error_logger | api/routers/orchestration.py:32 | Unable to run orchestration: unhashable type: 'list'

src/genai/analytics_chatbot/utils/name_variations.json

@@ -1,39 +1,44 @@
 {
+  "imsurakshyakc": [
+    "surakshya kc",
+    "surakshya",
+    "kc surakshya"
+  ],
   "divyadhakal_": [
     "divya dhakal",
     "divya",
     "dhakal divya",
     "dibya dhakal"
   ],
-  "ishtukarkee": [
-    "istu karki",
-    "istu karkee",
-    "ishtu karki",
-    "ishtu karkee",
-    "ishtoo karkee",
-    "ishtu",
-    "istu"
-  ],
-  "kano_mama": [
-    "kano mama",
-    "kano",
-    "mama kano"
+  "mydarlingfood": [
+    "my darling food",
+    "darling food",
+    "food darling"
   ],
   "munachiya": [
     "muna chiya",
     "muna",
     "chiya muna"
   ],
-  "nepal_food": [
-    "nepal food",
-    "nepalfood",
-    "food nepal"
+  "sanjogkoirala_": [
+    "sanjog koirala",
+    "koirala sanjog",
+    "sanjog"
   ],
-  "ggkaam610": [
-    "ggkaam",
-    "ggkaam 610",
-    "gg kaam",
-    "gg_kaam"
+  "_anupchau": [
+    "anup chau",
+    "chau anup",
+    "anup"
+  ],
+  "_richa.s": [
+    "richa s",
+    "richa",
+    "s richa"
+  ],
+  "_its.me.muskan_": [
+    "muskan",
+    "its me muskan",
+    "muskan me"
   ],
   "adars_fpv": [
     "adarsh",
@@ -50,6 +55,11 @@
     "anisha",
     "kafle anisha"
   ],
+  "_prabha__gurung_": [
+    "prabha gurung",
+    "gurung prabha",
+    "prabha"
+  ],
   "diwasg": [
     "diwash gurung",
     "diwash",
@@ -61,10 +71,24 @@
     "grubfood",
     "grub food"
   ],
-  "imsurakshyakc": [
-    "surakshya kc",
-    "surakshya",
-    "kc surakshya"
+  "ggkaam": [
+    "ggkaam",
+    "gg kaam",
+    "gg_kaam"
+  ],
+  "gracebhattarai": [
+    "grace bhattarai",
+    "bhattarai grace",
+    "grace"
+  ],
+  "ishtu_karkee": [
+    "istu karki",
+    "istu karkee",
+    "ishtu karki",
+    "ishtu karkee",
+    "ishtoo karkee",
+    "ishtu",
+    "istu"
   ],
   "jholeyism": [
     "jholey",
@@ -72,9 +96,49 @@
     "jholay",
     "jhuley"
   ],
-  "mrbvlog2": [
+  "mrbvlog2.0": [
+    "mrb vlog",
     "mrb vlogs",
-    "mrb",
     "mr b vlog"
+  ],
+  "nepal.food": [
+    "nepal food",
+    "nepalfood",
+    "food nepal"
+  ],
+  "nnzeella_shrestha": [
+    "nizella shrestha",
+    "neezella shrestha",
+    "shrestha nizella"
+  ],
+  "pramuditaaudas": [
+    "pramudita audas",
+    "audash pramudita",
+    "pramudita"
+  ],
+  "rabindra_dhant1": [
+    "rabindra dhant",
+    "dhant rabindra",
+    "rabindra"
+  ],
+  "razee.maharjan": [
+    "razee maharjan",
+    "maharjan razee",
+    "razee"
+  ],
+  "siddinathapa": [
+    "siddina thapa",
+    "siddina",
+    "thapa siddina"
+  ],
+  "sjpoon_official": [
+    "sjpoon",
+    "sj poon",
+    "poon sj"
+  ],
+  "tashyilha_g": [
+    "tashyil ha g",
+    "tashyil",
+    "tashyil g"
   ]
 }

src/genai/analytics_chatbot/utils/nodes.py

@@ -1,8 +1,8 @@
 import requests
 from langchain_core.messages import SystemMessage , HumanMessage , FunctionMessage
 from .state import State
-from .schemas import ResponseFormatter
-from .prompts import chatbot_prompt
+from .schemas import ResponseFormatter , InfluencerNames
+from .prompts import chatbot_prompt , get_inf_name_prompt
 from .utils import generate_api_knowledge
 from src.genai.utils.models_loader import llm_groq
 
@@ -27,6 +27,7 @@ class ChatbotNode:
         return {
         "messages": [{"role": "assistant", "content": f'''The endpoint is: {result.endpoint}. The parameters are: {result.parameters}'''}],
         "endpoint": result.endpoint,
+        "method": result.method,
         "parameters": result.parameters,
     }
 
@@ -40,8 +41,29 @@ class FetchDataNode:
             }
 
     def run(self, state:State):
+        print('Entered to fetch data')
         url = f'''{self.base_url}{state['endpoint']}'''
-        response = requests.get(url, params=state['parameters'],headers=self.headers)
+        if state['method'] == 'GET':
+            response = requests.get(url, params=state['parameters'],headers=self.headers)
+        elif state['endpoint'] == '/api/v1/compare/':
+            print('Condition satisfied')
+            messages = [SystemMessage(content=get_inf_name_prompt()), 
+            HumanMessage(content=f'''The dictionary of parameters is: {state['parameters']}''')]
+            response=llm_groq.with_structured_output(InfluencerNames).invoke(messages)
+            payload = {
+            "usernames": response.names,
+            "freq": state['parameters']['frequency']          
+             }
+            
+            print('The payload is:',payload)
+            
+            headers = {
+            "Content-Type": "application/json"
+             }
+
+            response = requests.post(url, json=payload, headers=headers)
+
         return {'response':response.json()}
 
 
+

src/genai/analytics_chatbot/utils/prompts.py

@@ -3,6 +3,7 @@ def chatbot_prompt():
 You are an intelligent assistant whose task is to route user queries to the correct API endpoint.
 You have access to the API knowledge base, which contains information about each endpoint:
 - The endpoint path
+-The method 'GET' or 'POST'
 - Its required parameters
 - A description of what the endpoint does
 
@@ -14,6 +15,7 @@ Your job is to:
 5. Return the result in a **strict JSON format** exactly like this:
 
   "endpoint": "<chosen endpoint path>",
+  "method": GET or POST
   "parameters":
     "<param1>": "<value1>",
     "<param2>": "<value2>"
@@ -21,7 +23,7 @@ Your job is to:
 Important instructions:
 - Only return endpoints that exist in the API knowledge base.
 - Include all required parameters for the endpoint.
-- If a parameter is not specified in the user's query, return it as null.
+- If the parameter or method  is not specified in the user's query, return it as null.
 - Do not add any extra explanation or text; return **only the JSON**.
 - The API knowledge base will be provided as a separate function message.
 
@@ -31,6 +33,7 @@ API knowledge: contains endpoint "/overview/buzz_trend" with parameters ["period
 Expected output:
 
   "endpoint": "/api/v1/overview/buzz_trend",
+  "method": GET
   "parameters": 
     "period": "monthly",
     "influencer_username": "John"
@@ -38,3 +41,8 @@ Expected output:
 Your response must always follow this exact JSON format.
 """
 
+def get_inf_name_prompt():
+    return'''You are given with a dictionary called parameters.
+Your task is to just extract the names of influencers from as it is from the values of that dictionary.
+Extract the names in the form of list. 
+'''

src/genai/analytics_chatbot/utils/schemas.py

@@ -2,4 +2,8 @@ from pydantic import BaseModel, Field
 
 class ResponseFormatter(BaseModel):
     endpoint: str = Field(description='Return the exact endpoint from the knowledge base of endpoints.')
-    parameters: dict = Field(description='Return the dictionary of parameters to pass to the endpoint.')
+    method: str = Field(description='Return the exact request type from the knowledge base of endpoints. GET or POST ')
+    parameters: dict = Field(description='Return the dictionary of parameters to pass to the endpoint.')
+
+class InfluencerNames(BaseModel):
+    names: list

src/genai/analytics_chatbot/utils/state.py

@@ -5,6 +5,7 @@ from langgraph.graph.message import add_messages
 class State(TypedDict):
     messages: Annotated[list, add_messages]
     endpoint: str
+    method: str
     parameters: dict
     response:dict
     error_message:str

src/genai/analytics_chatbot/utils/streamlit_app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+import requests
+
+# Your FastAPI endpoint
+API_URL = "http://localhost:8000/analytics-chatbot"   # change if deployed
+
+st.set_page_config(page_title="Analytics Chatbot", page_icon="🤖", layout="centered")
+
+st.title("📊 Analytics Chatbot")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+# Display previous messages
+for chat in st.session_state["messages"]:
+    role = "🧑 You" if chat["role"] == "user" else "🤖 Bot"
+    with st.chat_message(chat["role"]):
+        st.markdown(f"**{role}:** {chat['content']}")
+
+# User input box
+if prompt := st.chat_input("Ask something about analytics..."):
+    # Store user message
+    st.session_state["messages"].append({"role": "user", "content": prompt})
+
+    # Show user message
+    with st.chat_message("user"):
+        st.markdown(f"**🧑 You:** {prompt}")
+
+    try:
+        # Call FastAPI backend
+        response = requests.post(API_URL, json={"message": prompt})
+
+        if response.status_code == 200:
+            data = response.json()
+            bot_reply = f"**Endpoint:** {data['endpoint']}\n\n" \
+                        f"**Parameters:** `{data['parameters']}`\n\n" \
+                        f"**Response:** {data['response']}"
+        else:
+            bot_reply = f"⚠️ Error {response.status_code}: {response.text}"
+
+    except Exception as e:
+        bot_reply = f"❌ Failed to connect to API: {e}"
+
+    # Store bot message
+    st.session_state["messages"].append({"role": "assistant", "content": bot_reply})
+
+    # Show bot reply
+    with st.chat_message("assistant"):
+        st.markdown(f"**🤖 Bot:**\n\n{bot_reply}")

src/genai/analytics_chatbot/utils/utils.py

@@ -4,7 +4,7 @@ import re
 
 def generate_api_knowledge(base_url: str):
     """
-    Fetches FastAPI OpenAPI docs and summarizes endpoints, parameters, and descriptions.
+    Fetches FastAPI OpenAPI docs and summarizes endpoints, methods, parameters, and descriptions.
     Returns a JSON-friendly Python list of dicts, excluding the root endpoint.
     """
     resp = requests.get(f"{base_url}/openapi.json")
@@ -39,6 +39,7 @@ def generate_api_knowledge(base_url: str):
 
             api_knowledge.append({
                 "endpoint": endpoint,
+                "method": method.upper(),   
                 "parameters": params,
                 "description": description
             })

src/genai/orchestration_agent/agent.py

@@ -1,8 +1,8 @@
 from langgraph.graph import StateGraph, MessagesState, START, END
 from langgraph.checkpoint.memory import MemorySaver
-from .utils.nodes import ToolReturnNode, ExtractUserReferenceNode
+from .utils.nodes import ToolReturnNode, ExtractUserReferenceNode , ImageCaptionNode , QueryResponsenNode
 from src.genai.utils.models_loader import llm_gpt
-from .utils.state import ValidationFormatter
+from .utils.state import State
 from .utils.utils import ImageCaptioner, ResponseBlockExtractor
 from .utils.tools import InfluencerRetrievalTool
 
@@ -16,49 +16,24 @@ class OrchestrationAgent:
         self.user_input_history=[]
 
     def orchestration_graph(self):
-        workflow = StateGraph(MessagesState)
-        workflow.add_node("chatbot1", ToolReturnNode().run)
-        workflow.add_node("chatbot2", ExtractUserReferenceNode().run)
+        workflow = StateGraph(State)
+        workflow.add_node("image_caption", ImageCaptionNode().run)
+        workflow.add_node("tool_return", ToolReturnNode().run)
+        workflow.add_node("query_response", QueryResponsenNode().run)
+        workflow.add_node("extract_reference", ExtractUserReferenceNode().run)
+        
+        workflow.add_edge(START,"image_caption")
+        workflow.add_edge(START, "tool_return")
+        workflow.add_edge(START, "extract_reference")
+
+        workflow.add_edge('image_caption', "query_response")
+        workflow.add_edge('tool_return', "query_response")
+        workflow.add_edge('extract_reference', 'query_response')
+        workflow.add_edge('query_response', END)
 
-        workflow.add_edge(START, "chatbot1")
-        workflow.add_edge('chatbot1', "chatbot2")
-        workflow.add_edge('chatbot2', END)
         return workflow.compile(checkpointer=self.memory)
     
-    def trim_history(self):
-        if len(self.user_input_history)>4:
-            self.user_input_history=self.user_input_history[-2:]
-        print('Length of history', len(self.user_input_history))
-        query_for_retrieval = ' '.join(
-        [msg['content'] for msg in self.user_input_history if msg['role'] in ('human', 'image_caption')])
-        return query_for_retrieval
-    
-    def caption_image(self,image_base64,user_input):
-        if len(image_base64)>0:
-            caption_response = ImageCaptioner().caption_image(image_base64,user_input)
-            self.user_input_history.append({'role': 'image_caption', 'content': caption_response})
-
-            print('Caption Response:', caption_response)
-        else:
-            caption_response =''
-        return caption_response
-
-
-    def chat(self,user_input: str, image_base64=[]):
-        print('Message Chunk:')
-        self.user_input_history.append({'role': 'human', 'content': user_input})
-        caption_response = self.caption_image(image_base64,user_input)
-        query_for_retrieval= self.trim_history()
-
-        influencers_data = InfluencerRetrievalTool().retrieve_for_orchestration(query_for_retrieval)
 
-        config = {"configurable": {"thread_id": "orchestration-thread"}}
-        response = self.agent.invoke({"messages": [{'role':'human','content':user_input},
-                                            {'role': 'function', 'name': 'data_of_influencers', 'content': influencers_data},
-                                            {'role':'function','name':'information_of_image','content':caption_response}]}, config)['messages']
-        print('Orchestrator Response', response)
-        response=llm_gpt.with_structured_output(ValidationFormatter).invoke(ResponseBlockExtractor(response).extract_latest())
-        return response
 
     
 

src/genai/orchestration_agent/utils/nodes.py

@@ -1,7 +1,47 @@
-from .prompts import tool_return_prompt , extract_user_reference_prompt
-from langchain_core.messages import SystemMessage, HumanMessage
+from .prompts import tool_return_prompt , extract_user_reference_prompt , query_response_prompt,captioning_prompt
+from langchain_core.messages import SystemMessage, HumanMessage, FunctionMessage
 from src.genai.utils.models_loader import llm_gpt
-from .state import  ToolResponseFormatter, UserReferenceResponseFormatter
+from src.genai.utils.base_endpoint import base_url
+from .state import State
+from .tools import InfluencerRetrievalTool
+from .schemas import ToolResponseFormatter , UserReferenceResponseFormatter
+import os
+import requests
+from groq import Groq
+
+retriever=InfluencerRetrievalTool()
+
+class ImageCaptionNode:
+    def __init__(self, api_key=os.environ.get('GROQ_API_KEY')):
+        self.client = Groq(api_key=api_key)
+
+    def run(self,state:State):
+        if len(state['image_base64'])>0:
+            print('Captioning image')
+            chat_completion = self.client.chat.completions.create(
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": captioning_prompt(state['messages'])},
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/jpg;base64,{state['image_base64[-1]']}",
+                                },
+                            },
+                        ],
+                    }
+                ],
+                model="meta-llama/llama-4-scout-17b-16e-instruct",
+                max_completion_tokens=50,
+                temperature = 1
+            )
+            response=chat_completion.choices[0].message.content
+            return {'image_caption': response}
+        else:
+            return {'image_caption':None}
+
 
 class ToolReturnNode:
     """Node for determining which tools to use based on user messages."""
@@ -9,12 +49,32 @@ class ToolReturnNode:
     def __init__(self, llm=llm_gpt):
         self.llm = llm
 
-    def run(self, state):
+    def run(self, state:State):
         if len(state["messages"]) > 23:
             state["messages"] = state["messages"][-18:]
         template = [SystemMessage(content=tool_return_prompt)] + state["messages"]
         response = self.llm.with_structured_output(ToolResponseFormatter).invoke(template)
-        return {"messages": [{'role': 'assistant', 'content': f"The exact name of the tool is: {response}"}]}
+        print('The response is:', response)
+        return {"messages": [{'role': 'assistant', 'content': f"Tool invoked: {response.tools}"}],
+                "tools":response.tools}
+                
+
+class QueryResponsenNode:
+    def __init__(self):
+        self.llm = llm_gpt
+    
+    def run(self,state:State):
+        if len(state['tools'])<1:
+            retrieved_data=retriever.retrieve_for_orchestration(state['messages'])
+            template = [SystemMessage(content=query_response_prompt),
+                        FunctionMessage(name='inf-data-retrieval',content=retrieved_data)] + state["messages"]
+            response = self.llm.invoke(template)
+            return {"messages": [{'role': 'assistant', 'content': response.content}],
+                    "query_response":response.content}
+        else:
+            return{
+                "query_response": f'''Okay i will perform {" ".join(state['tools'])} for you.'''
+            }
 
 
 class ExtractUserReferenceNode:
@@ -28,13 +88,52 @@ class ExtractUserReferenceNode:
             (msg for msg in reversed(state['messages']) if isinstance(msg, HumanMessage)),
             None
         )
+        print('Latest human message:', latest_human_message)
         template = [SystemMessage(content=extract_user_reference_prompt),
                     HumanMessage(content=latest_human_message.content)]
         response = self.llm.with_structured_output(UserReferenceResponseFormatter).invoke(template)
-        return {'messages': [{
-            'role': 'assistant',
-            'content': f"The video idea is: {response.video_idea} and the video story is: {response.video_story}"
-        }]}
+        return{
+            'video_idea': response.video_idea,
+            'video_story': response.video_story
+        }
+
+
+class InvokeToolNode:
+    def __init__(self):
+        self.base_url = base_url
+        self.headers = {
+            "Authorization": "Bearer YOUR_API_KEY",  # replace with your API key if needed
+            "Content-Type": "application/json"
+            }
+
+    def run(self,state:State):
+        latest_human_message = next(
+            (msg for msg in reversed(state['messages']) if isinstance(msg, HumanMessage)),
+            None
+        )
+        data_to_return=[]
+        for tool in state['tools']:
+            if 'analytics' in tool:
+                url = f'''{self.base_url}{tool}'''
+                response = requests.get(url, params=latest_human_message.content,headers=self.headers)
+                return {
+                    'analytics_response':response.json()
+                }
+
+
+
+
+
+
+
+                
+
+
+            
+
+
+
+
 
 
 

src/genai/orchestration_agent/utils/prompts.py

@@ -1,19 +1,64 @@
+tool_return_prompt= """
+You are an perfect orchestration agent that
+- grabs the intention of the user and determines which of the tools are they trying to access.
+
+For every user query, return:
+   tools: A Python list of tool names (from the ordered list below). 
+
+Names of tools:
+1. ideation → Generate fresh video ideas, or if user asks again for new ideas.  
+2. human-idea-refining → If user likes/locks ideas, gives feedback, wants to improve/merge/edit, or refers to a previous idea. Trigger until user confirms final choice. Undo/going back also counts as refining. Never follow this with ideation.  
+3. generate-story → If user wants to create a story, brainstorm the story , or plot. Based on refined idea.  
+4. generate-ultimate-story → Only if user requests a final/polished version AND `generate-story` has been used.  
+5. generate-image → If user wants visuals/images/scenes from final story.  
+6. analytics → If user asks for influencer analytics, metrics, stats, comparisons, or insights. Examples:  
+   - “Info about influencer X”  
+   - “Sentiment of influencer X’s comments”  
+   - “Top collaborators of X”  
+   - “Compare X vs Y weekly analytics”  
+   - “Engagement of influencer X”  
+   - “Buzz of influencer X”  
+
+Rules
+- Never return the name of tool that doesn't exists in the tool names.
+- Always output tools as a Python list (even if one).  
+- Only include needed tools with their exact names.
+- If multiple tools, respect sequence (e.g., `["human-idea-refining", "generate-story"]`).  
+- If query is not about any tool calling (like normal questions) then return empty list in tools. → `"tools": []`.  
+- If about influencer analytics → `"tools": ["analytics"]`.  
+
+Clarifications
+- “I liked idea 4. Generate story based on idea 4” → `['human-idea-refining',"generate-story"]`  
+- “Create image of final script” → `["generate-image"]`  
+- “Final version of story” → `["generate-ultimate-story"]` (only if story already generated)  
+- “Improve this idea” → `["human-idea-refining"]`  
+- Analytics requests → `["analytics"]`  
+
+Output Format
+"tools": ["tool_1", "tool_2"],  
+"""
+
+query_response_prompt="""
+You are an AI orchestration agent and a friendly assistant built to help businesses and brands find influencers and generate content ideas, stories, and visuals.
+Respond to the user's query in polite way.
+You are passed with the data of influencers from the function message. Use that data to give the response.
+"""
 
-tool_return_prompt = """
+tool_return_prompt_old = """
 You are an AI orchestration agent and a friendly assistant built to help businesses and brands find influencers and generate content ideas, stories, and visuals.
 
 Your job is to:
 1. **Read the user's message carefully** and identify their intent.
 2. **Return exactly two things**:
-   - `tools`: a Python-style list of tool names (from the ordered list below) that match the user’s intent.
-   - `query_response`: a short, friendly, and helpful reply that aligns with the tools you've selected. Also return the relevant data of influencers aligning with the user query only if the user asks about the influencers.
+   - tools: a Python-style list of tool names (from the ordered list below) that match the user’s intent.
+   - query_response: a short, friendly, and helpful reply that aligns with the tools you've selected. Also return the relevant data of influencers aligning with the user query only if the user asks about the influencers.
 
-Your `tools` output must:
+Your tools output must:
 - Always be a **Python-style list**, even if only one tool is selected.
 - **Respect the sequence** of tools listed below. If more than one tool is needed, always list them in this order (regardless of how the user wrote them).
 - Only include tools that are strictly necessary.
 
-Your `query_response` must:
+Your query_response must:
 - Always match and acknowledge the user's request.
 - Be aligned with the selected tools.
 - Never say something is impossible if a tool exists for it.
@@ -44,7 +89,7 @@ Your `query_response` must:
 4. **generate-ultimate-story** → Trigger only if:
    - The user is asking for the **final**, polished, or complete story
    - And has already done previous story brainstorming
-   - Never trigger this tool alone unless `generate-story` has been used before
+   - Never trigger this tool alone unless generate-story has been used before
 
 5. **generate-image** → Trigger if the user:
    - Wants a visual, image, or scene created from the final story
@@ -56,19 +101,19 @@ Your `query_response` must:
 - If a user says:
   > “Generate story based on idea 4”  
   This means they selected/liked idea 4 → so return:
-  `"tools": ["human-idea-refining", "generate-story"]`
+  "tools": ["human-idea-refining", "generate-story"]
   
 - If a user says:
   > “Create an image of the final script”  
-  → Return: `["generate-image"]`
+  → Return: ["generate-image"]
 
 - If a user says:
   > “I want a final version of this story”  
-  → Return: `["generate-ultimate-story"]` (but only if `generate-story` was used before)
+  → Return: ["generate-ultimate-story"] (but only if generate-story was used before)
 
-- If a user gives feedback or says “improve this idea” → Return `["human-idea-refining"]`
+- If a user gives feedback or says “improve this idea” → Return ["human-idea-refining"]
 
-- If a user just wants help with finding influencers or advice → Return `"tools": []` but still respond helpfully via `query_response`.
+- If a user just wants help with finding influencers or advice → Return "tools": [] but still respond helpfully via query_response.
 - Again reminding you the most important part. If you generate more than one tools, **Please** generate them in the sequence above provided. For eg: Don't give **human-idea-refining** after **ideation**. The list of tools have to be in the proper order provided above.
 ---
 
@@ -85,8 +130,6 @@ You're also an intelligent assistant for brands looking to collaborate with infl
 
 """
 
-
-
 extract_user_reference_prompt = """
 You are an information extractor, NOT a creative assistant. Your ONLY job is to extract video ideas and video stories from user queries **if and only if** they are explicitly written by the user. Do not create, generate, or imagine anything on your own.
 

src/genai/orchestration_agent/utils/schemas.py

@@ -0,0 +1,16 @@
+from pydantic import BaseModel, Field 
+from typing import List
+
+class ToolResponseFormatter(BaseModel):
+  tools: List[str] = Field(description="Returns the name of the tool, tools, or an empty list.")
+
+class UserReferenceResponseFormatter(BaseModel):
+  video_idea: str = Field(description="Returns the video idea as it is, otherwise return null")
+  video_story: str = Field(description="Returns the video story as it is, otherwise return null")
+
+class ValidationFormatter(BaseModel):
+    tool: List[str] = Field(description="Returns the name of the tool or tools as it is.")
+    query_response: str = Field(description="Returns the reply of query as it is.")
+    image_caption: str = Field(description="Returns the information of image as it is.")
+    video_idea: str = Field(description="Returns the video idea as it is.")
+    video_story: str = Field(description="Returns the video story as it is.")

src/genai/orchestration_agent/utils/state.py

@@ -1,20 +1,15 @@
-from pydantic import BaseModel, Field
-from typing import List
+from typing import List , TypedDict , Annotated
+from langgraph.graph.message import add_messages
 
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+    tools: List[str]
+    query_response: str
+    video_idea: str
+    video_story: str
+    image_caption: str
+    image_base64: list
+    analytics_response: dict
+    final_story:str
 
-class ToolResponseFormatter(BaseModel):
-  tool: List[str] = Field(description="Returns the name of the tool, tools, or an empty list.")
-  query_response: str = Field(description="Returns the response of the user query.")
 
-
-
-class UserReferenceResponseFormatter(BaseModel):
-  video_idea: str = Field(description="Returns the video idea as it is, otherwise return null")
-  video_story: str = Field(description="Returns the video story as it is, otherwise return null")
-
-class ValidationFormatter(BaseModel):
-    tool: List[str] = Field(description="Returns the name of the tool or tools as it is.")
-    query_response: str = Field(description="Returns the reply of query as it is.")
-    image_caption: str = Field(description="Returns the information of image as it is.")
-    video_idea: str = Field(description="Returns the video idea as it is.")
-    video_story: str = Field(description="Returns the video story as it is.")

src/genai/orchestration_agent/utils/tools.py

@@ -28,8 +28,11 @@ class InfluencerRetrievalTool:
                 'commentCount': int(row['commentCount']) if pd.notnull(row['commentCount']) else None
             })
         return results
-
+    
     def retrieve_for_orchestration(self, query):
+        return 'Influencer Divya dhakal has got 66 likes.'
+    
+    def retrieve_for_orchestration_old(self, query):
         query_embedding = np.array(embedding_model.embed_query(str(query))).reshape(1, -1).astype('float32')
         faiss.normalize_L2(query_embedding)
         distances, indices = self.index.search(query_embedding, len(self.df))

src/genai/orchestration_agent/utils/utils.py

@@ -72,3 +72,19 @@ class ResponseBlockExtractor:
         return latest_block
 
 
+def handle_tools(tools_list,stored_data):
+    tools_order = [
+    "analytics",
+    "ideation",
+    "human-idea-refining",
+    "generate-story",
+    "generate-ultimate-story",
+    "generate-image",
+    ]
+    if 'generate-story' in tools_list and len(stored_data['human_ideation_interactions'])<1:
+        tools_list.append('human-idea-refining')
+    if 'generate-ultimate-story' in tools_list and len(stored_data['brainstorming_response'])<1:
+        tools_list.append('generate-story')
+    
+    tools_list = [tool for tool in tools_order if tool in tools_list]
+    return tools_list

src/genai/utils/base_endpoint.py

@@ -0,0 +1 @@
+base_url = 'http://127.0.0.1:8000/api/'

src/genai/utils/models_loader.py

@@ -17,22 +17,23 @@ embedding_model = OpenAIEmbeddings(model="text-embedding-3-small", dimensions=15
 llm_anthropic = ChatAnthropic(model='claude-3-7-sonnet-latest', temperature=1)
 llm_gemini = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
 llm_groq_openai = ChatGroq(model="openai/gpt-oss-120b",temperature=0.7)
-llm_groq = ChatGroq(model="llama-3.1-8b-instant",temperature=0.7)
+llm_groq = ChatGroq(model="llama-3.3-70b-versatile",temperature=0)
 
-llm_gpt = ChatOpenAI(model="gpt-4o-mini",temperature=0.3)
-llm_gpt_high = ChatOpenAI(model="gpt-4o-mini",temperature=0.5)
+llm_gpt_small = ChatOpenAI(model="gpt-3.5-turbo",temperature=0.3)
+llm_gpt = ChatOpenAI(model="gpt-3.5-turbo",temperature=0.3)
+llm_gpt_high = ChatOpenAI(model="gpt-3.5-turbo",temperature=0.5)
 
 captioning_model = "meta-llama/llama-4-scout-17b-16e-instruct"
 image_generation_model = "black-forest-labs/FLUX.1-schnell"
 
-ideator_llm = llm_groq_openai
-moderator_llm = llm_groq_openai
-critic_llm = llm_groq
-simplifier_llm = llm_groq
-normalizer_llm = llm_groq
-validator_llm = llm_groq
-judge1_llm = llm_groq
-judge2_llm = llm_groq
+ideator_llm = llm_gpt_small
+moderator_llm = llm_gpt_small
+critic_llm = llm_gpt_small
+simplifier_llm = llm_gpt_small
+normalizer_llm = llm_gpt_small
+validator_llm = llm_gpt_small
+judge1_llm = llm_gpt_small
+judge2_llm = llm_gpt_small
 
 
 

streamlit_app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+import requests
+
+# Your FastAPI endpoint
+API_URL = "http://localhost:8000/api/analytics-chatbot"   # change if deployed
+
+st.set_page_config(page_title="Analytics Chatbot", page_icon="🤖", layout="centered")
+
+st.title("📊 Analytics Chatbot")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+# Display previous messages
+for chat in st.session_state["messages"]:
+    role = "🧑 You" if chat["role"] == "user" else "🤖 Bot"
+    with st.chat_message(chat["role"]):
+        st.markdown(f"**{role}:** {chat['content']}")
+
+# User input box
+if prompt := st.chat_input("Ask something about analytics..."):
+    # Store user message
+    st.session_state["messages"].append({"role": "user", "content": prompt})
+
+    # Show user message
+    with st.chat_message("user"):
+        st.markdown(f"**🧑 You:** {prompt}")
+
+    try:
+        # Call FastAPI backend
+        response = requests.post(API_URL, json={"message": prompt})
+
+        if response.status_code == 200:
+            bot_reply = response.json()
+            # bot_reply = f"**Endpoint:** {data['endpoint']}\n\n" \
+            #             f"**Parameters:** `{data['parameters']}`\n\n" \
+            #             f"**Response:** {data['response']}"
+        else:
+            bot_reply = f"⚠️ Error {response.status_code}: {response.text}"
+
+    except Exception as e:
+        bot_reply = f"❌ Failed to connect to API: {e}"
+
+    # Store bot message
+    st.session_state["messages"].append({"role": "assistant", "content": bot_reply['response']})
+
+    # Show bot reply
+    with st.chat_message("assistant"):
+        st.json(bot_reply['response'])

tests/test_analytics.py

@@ -4,6 +4,6 @@ from api.main import app
 client = TestClient(app)
 
 def test_analytics():
-    response = client.post("/api/show-analytics", json={})
+    response = client.get("/api/show-analytics")
     print(response.json())
     assert response.status_code == 200

tests/test_analytics_chatbot.py

@@ -0,0 +1,10 @@
+from fastapi.testclient import TestClient
+from api.main import app
+
+client = TestClient(app)
+
+def test_context_analysis():
+    message = "I want to see the sentiment distribution of divya dhakal."
+
+    response = client.get("/api/analytics-chatbot",params=message)
+    assert response.status_code == 200

tests/test_final_story.py

@@ -4,5 +4,5 @@ from api.main import app
 client = TestClient(app)
 
 def test_final_story_generation():
-    response = client.post("/api/generate-final-story", json={})
+    response = client.get("/api/generate-final-story")
     assert response.status_code == 200

tests/test_image_generation.py

@@ -4,6 +4,6 @@ from api.main import app
 client = TestClient(app)
 
 def test_image_generation():
-    response = client.post("/api/generate-image", json={})
+    response = client.get("/api/generate-image")
     print(response.json())
     assert response.status_code == 200

tests/test_orchestrator.py

@@ -6,7 +6,7 @@ client = TestClient(app)
 
 def test_orchestrator():
     payload = {
-        "message":"Mention some popular influencers of nepal"
+        "message":"Hello"
     }
     response = client.post("/api/orchestration", json=payload)
     print(response.json())