Implemented logging and try catch

api/main.py

@@ -11,6 +11,7 @@ app = FastAPI()
 app_logger = logging.getLogger("app_logger")
 error_logger = logging.getLogger("error_logger")
 access_logger = logging.getLogger("access_logger")
+warning_logger = logging.getLogger("warning_logger")
 
 
 @app.middleware("http")

api/routers/brainstorm.py

@@ -1,4 +1,5 @@
 
+import logging
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
 from api.stored_data import stored_data
@@ -9,30 +10,38 @@ from api.backup_prompts import brainstorm_backup_prompt
 router=APIRouter()
 agent = BrainstormingAgent()
 brainstorm_graph = agent.brainstorming_graph()
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
+
 
 @router.post("/brainstorm")
 def brainstorming_endpoint(request: BrainstormRequest):
-    idea = (
-        [stored_data['refined_ideation']]
-        if stored_data.get('refined_ideation')
-        else [str(stored_data['final_ideation'])]
-        if stored_data.get('final_ideation')
-        else [brainstorm_backup_prompt]
-    )
-
-    def event_stream():
-        for stream_mode, chunk in brainstorm_graph.stream(
-            {
-                "idea": idea,
-                "images": request.image_base64_list,
-                "latest_preferred_topics": request.preferred_topics,
-                "business_details": stored_data["business_details"],
-            },
-            config={"configurable": {"thread_id": request.thread_id}},
-            stream_mode=['messages','values'] 
-        ):
-            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
+    try:
+        idea = (
+            [stored_data['refined_ideation']]
+            if stored_data.get('refined_ideation')
+            else [str(stored_data['final_ideation'])]
+            if stored_data.get('final_ideation')
+            else [brainstorm_backup_prompt]
+        )
 
-    return StreamingResponse(event_stream(), media_type="text/event-stream")
+        def event_stream():
+            for stream_mode, chunk in brainstorm_graph.stream(
+                {
+                    "idea": idea,
+                    "images": request.image_base64_list,
+                    "latest_preferred_topics": request.preferred_topics,
+                    "business_details": stored_data["business_details"],
+                },
+                config={"configurable": {"thread_id": request.thread_id}},
+                stream_mode=['messages','values'] 
+            ):
+                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
+        app_logger.info('Executed brainstorming agent.')
+        return StreamingResponse(event_stream(), media_type="text/event-stream")
+    
+    except Exception as e:
+        error_logger.error('Unable to execute brainstorm agent.')
+        raise

api/routers/context_analysis.py

@@ -1,51 +1,64 @@
 import ast
 import json
+import logging
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
 from api.stored_data import stored_data
 from src.genai.context_analysis_agent.agent import IntroductionChatbot
 from api.schemas.context_analysis import UserMessage
 
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
+
 router = APIRouter()
 context_analysis_graph = IntroductionChatbot()   
 
 @router.post("/context-analysis")
 def context_analysis(msg: UserMessage):
     def event_generator():
-        accumulated_response = ""
-
-        # Stream tokens or partial chunks from your chat generator
-        for chunk in context_analysis_graph.chat(msg.message):
-            accumulated_response += chunk
-            payload = {
-                "streamed_response": chunk,
-            }
-            yield json.dumps(payload) + "\n"  # JSON per line
-
-        # After streaming finished, get completion info
-        last_response = context_analysis_graph.messages[-1]["content"]
-
-        if context_analysis_graph.is_complete(last_response):
-            details = context_analysis_graph.extract_details()
-            if type(details) != dict:
-                details = details.model_dump()
-            if isinstance(details, str):
-                details = ast.literal_eval(details)
-
-            stored_data["business_details"] = details
-            context_analysis_graph.reset()
-
-            final_payload = {
-                "response": accumulated_response,
-                "complete": True,
-                "business_details": details
-            }
-        else:
-            final_payload = {
-                "response": accumulated_response,
-                "complete": False
-            }
-
-        yield json.dumps(final_payload) + "\n"
+        try:
+            accumulated_response = ""
+            for chunk in context_analysis_graph.chat(msg.message):
+                accumulated_response += chunk
+                payload = {
+                    "streamed_response": chunk,
+                }
+                yield json.dumps(payload) + "\n"  
+            app_logger.info('Executed context analysis agent')
+
+            last_response = context_analysis_graph.messages[-1]["content"]
+
+            if context_analysis_graph.is_complete(last_response):
+                app_logger.info('Context analysis completed.')
+                try:
+                    details = context_analysis_graph.extract_details()
+                    app_logger.info('Details extracted after context analysis.')
+                except Exception as e:
+                    error_logger.error(f'Unable to extract details: {e}')
+
+                if type(details) != dict:details = details.model_dump()
+                if isinstance(details, str):details = ast.literal_eval(details)
+                    
+                stored_data["business_details"] = details
+                try:
+                    context_analysis_graph.reset()
+                    app_logger.info('Context analysis memory reset after details extraction.')
+                except Exception as e:
+                    error_logger.error('Unable to reset memory:', e)
+
+                final_payload = {
+                    "response": accumulated_response,
+                    "complete": True,
+                    "business_details": details
+                }
+            else:
+                final_payload = {
+                    "response": accumulated_response,
+                    "complete": False
+                }
+
+            yield json.dumps(final_payload) + "\n"
+        except Exception as e:
+            error_logger.error('Unable to run context analysis agent:', e)
 
     return StreamingResponse(event_generator(), media_type="text/event-stream")

api/routers/generate_final_story.py

@@ -1,14 +1,23 @@
+import logging
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
 from api.stored_data import stored_data
 from src.genai.brainstroming_agent.utils.utils import FinalStoryGenenrator
 
 router= APIRouter()
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
 
 @router.post("/generate-final-story")
 def generate_final_story_endpoint():
-    def event_stream():
-        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")
+    try:
+        def event_stream():
+            for chunk in FinalStoryGenenrator().generate_final_story(stored_data.get('brainstorming_response') or stored_data.get('business_details')):
+                yield chunk
+        app_logger.info('Executed final story generation.')
+        return StreamingResponse(event_stream(), media_type="text/event-stream")
+    
+    except Exception as e:
+        error_logger.error('Unable to generate final story.')
+        raise
+    

api/routers/generate_image.py

@@ -1,15 +1,24 @@
+import logging
 from fastapi import APIRouter
 from api.stored_data import stored_data
 from src.genai.brainstroming_agent.utils.utils import ImageGenerator
 from api.backup_prompts import image_gen_backup_prompt
+
 router = APIRouter()
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
 
 @router.post("/generate-image") 
 def generate_image_endpoint():
-    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',image_gen_backup_prompt)))
-    stored_data['generated_image']=image
-    return {
-        'response':image
-    }
+    try:
+        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',image_gen_backup_prompt)))
+        stored_data['generated_image']=image
+        app_logger.info('The image is Generated')
+        return {
+            'response':image
+        }
+    except Exception as e:
+        error_logger.error('Unable to generate image')
+        raise

api/routers/human_idea_refining.py

@@ -1,20 +1,30 @@
+import logging
 from fastapi import APIRouter
 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()
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
 
 @router.post("/human-idea-refining")
 def human_idea_refine_endpoint(request:RefineIdeationRequest):
-    stored_data['human_ideation_interactions'].append({"role": "user", "content": request.query})
-    response = human_refine_graph.invoke(
-        {
-        'query': stored_data['human_ideation_interactions'],
-        'business_details': stored_data["business_details"],
-        'final_ideation': stored_data.get('final_ideation',["","","",""]),
-        },config={"configurable": {"thread_id": request.thread_id}}
-    )
-    stored_data['human_ideation_interactions'].append({"role": "assistant", "content": response['result']})
-    stored_data['refined_ideation'] = stored_data['human_ideation_interactions'][-1]['content']
-    return {'response' : stored_data['human_ideation_interactions'][-1]['content'] }
+    try:
+        stored_data['human_ideation_interactions'].append({"role": "user", "content": request.query})
+        response = human_refine_graph.invoke(
+            {
+            'query': stored_data['human_ideation_interactions'],
+            'business_details': stored_data["business_details"],
+            'final_ideation': stored_data.get('final_ideation',["","","",""]),
+            },config={"configurable": {"thread_id": request.thread_id}}
+        )
+        stored_data['human_ideation_interactions'].append({"role": "assistant", "content": response['result']})
+        stored_data['refined_ideation'] = stored_data['human_ideation_interactions'][-1]['content']
+        app_logger.info('Executed human ideation refining.')
+        return {'response' : stored_data['human_ideation_interactions'][-1]['content'] }
+    
+    except Exception as e:
+        error_logger.error('Unable to execute human idea refining agent:', e)
+        raise

api/routers/ideation.py

@@ -1,9 +1,13 @@
 import ast
+import logging
 from fastapi import APIRouter
 from api.stored_data import stored_data
 from src.genai.ideation_agent.agent import IdeationAgent
 from langgraph.errors import GraphRecursionError
 
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
+
 router = APIRouter()
 agent = IdeationAgent()
 idea_graph = agent.ideation_graph()
@@ -16,15 +20,21 @@ def ideation_endpoint():
         {
             'business_details': [stored_data['business_details']],
             'image_caption': [stored_data['image_caption'] if 'image_caption' in stored_data else ""]
-
         },
-        config=config,
-        )
+        config=config)
+
         stored_data['final_ideation'] = result['formatted_response'][-1]
         stored_data['final_ideation']=ast.literal_eval(stored_data['final_ideation'])
-
+        app_logger.info('Executed the ideation pipeline.')
+        
         return {'response':ast.literal_eval(result['formatted_response'][-1])}
+    
     except GraphRecursionError:
+        error_logger.error('Ideation loop ran more than specified.')
         result = idea_graph.get_state({"configurable": {"thread_id": "ideation_thread123"}})
         return {'response': result[0]}
+    
+    except Exception as e:
+        error_logger.error('Unable to execute the ideation', e)
+        raise
 

api/routers/orchestration.py

@@ -1,30 +1,32 @@
+import logging
 from fastapi import APIRouter
-from pydantic import BaseModel
 from api.stored_data import stored_data
 from src.genai.orchestration_agent.agent import OrchestrationAgent
-from typing import Optional
+from api.schemas.orchestration import OrchestrationRequest
 
-class OrchestrationRequest(BaseModel):
-    message: str
-    image_base64 : Optional[list] = []
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
 
 router= APIRouter()
 agent = OrchestrationAgent()
+
 @router.post("/orchestration")
 def orchestration_endpoint(request:OrchestrationRequest):
-    print('Image:',request.image_base64)
-    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
-        
-    print('Idea:',stored_data['refined_ideation'])
-    print('Story:', stored_data['final_story'])
+    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
+        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}
+    except Exception as e:
+        error_logger.error('Unable to run orchestration:', e)
+        raise 
 
-    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}
 
     

api/routers/show_analytics.py

@@ -1,9 +1,18 @@
+import logging
 from fastapi import APIRouter
 from api.stored_data import stored_data
 from src.genai.orchestration_agent.utils.utils import AnalyticsViewer
 
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
+
 router=APIRouter()
 @router.post("/show-analytics")
 def show_analytics_endpoint():
-    response = AnalyticsViewer(stored_data['business_details']).show_analytics()
-    return {'response': response}
+    try:
+        response = AnalyticsViewer(stored_data['business_details']).show_analytics()
+        app_logger.info('Influencer Analytics returned by orchestrator.')
+        return {'response': response}
+    except Exception as e:
+        error_logger.error('Unable to extract influencer analytics:', e)
+        raise

api/schemas/orchestration.py

@@ -0,0 +1,6 @@
+from pydantic import BaseModel
+from typing import Optional
+
+class OrchestrationRequest(BaseModel):
+    message: str
+    image_base64 : Optional[list] = []

logger_config.py

@@ -2,11 +2,17 @@
 import logging
 import os
 
+class RelativePathFormatter(logging.Formatter):
+    def format(self, record):
+        record.relpath = os.path.relpath(record.pathname, os.getcwd())
+        return super().format(record)
+    
 def setup_loggers():
     os.makedirs("logs", exist_ok=True)
 
     # === Format ===
-    formatter = logging.Formatter("%(asctime)s | %(levelname)s | %(name)s | %(message)s")
+    # formatter = logging.Formatter("%(asctime)s | %(levelname)s | %(name)s | %(message)s")
+    formatter = RelativePathFormatter("%(asctime)s | %(levelname)s | %(name)s | %(relpath)s:%(lineno)d | %(message)s")
 
     # === App Logger ===
     app_logger = logging.getLogger("app_logger")
@@ -32,9 +38,21 @@ def setup_loggers():
     access_logger.setLevel(logging.INFO)
     access_logger.addHandler(access_handler)
 
+
+    # === Warning Logger ===
+    warning_logger = logging.getLogger("warning_logger")
+    warning_handler = logging.FileHandler("logs/warnings.log")
+    warning_handler.setLevel(logging.WARNING)  # WARNING, ERROR, CRITICAL
+    warning_handler.setFormatter(formatter)
+    warning_logger.setLevel(logging.WARNING)
+    warning_logger.addHandler(warning_handler)
+
+
     # Optional: also log to console
     console_handler = logging.StreamHandler()
     console_handler.setFormatter(formatter)
     app_logger.addHandler(console_handler)
     error_logger.addHandler(console_handler)
     access_logger.addHandler(console_handler)
+    warning_logger.addHandler(console_handler)
+

logs/access.log

@@ -331,3 +331,53 @@
 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
+2025-08-17 14:00:44,955 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-08-17 14:00:44,955 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/docs
+2025-08-17 14:00:44,988 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:00:44,988 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:00:45,317 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-17 14:00:45,317 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/openapi.json
+2025-08-17 14:00:45,332 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:00:45,332 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:01:37,707 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/
+2025-08-17 14:01:37,707 | INFO | access_logger | api\main.py:19 | Request: GET http://127.0.0.1:8000/
+2025-08-17 14:01:37,708 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:01:37,708 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:01:52,138 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-08-17 14:01:52,138 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/orchestration
+2025-08-17 14:02:00,653 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:02:00,653 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:02:51,400 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/show-analytics
+2025-08-17 14:02:51,400 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/show-analytics
+2025-08-17 14:02:52,614 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:02:52,614 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:03:31,051 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-17 14:03:31,051 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/context-analysis
+2025-08-17 14:03:31,055 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:03:31,055 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:03:54,120 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-17 14:03:54,120 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-17 14:04:55,699 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:04:55,699 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:06:10,799 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/human-idea-refining
+2025-08-17 14:06:10,799 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/human-idea-refining
+2025-08-17 14:06:14,163 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:06:14,163 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:06:31,241 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-17 14:06:31,241 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/brainstorm
+2025-08-17 14:06:31,245 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:06:31,245 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:07:16,274 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/generate-final-story
+2025-08-17 14:07:16,274 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/generate-final-story
+2025-08-17 14:07:16,283 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:07:16,283 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:07:28,222 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/generate-image
+2025-08-17 14:07:28,222 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/generate-image
+2025-08-17 14:07:40,034 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:07:40,034 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 14:13:44,422 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/show-analytics
+2025-08-17 14:13:45,525 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 15:22:50,869 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/show-analytics
+2025-08-17 15:22:52,619 | INFO | access_logger | api\main.py:21 | Response status: 200
+2025-08-17 15:23:25,844 | INFO | access_logger | api\main.py:19 | Request: POST http://127.0.0.1:8000/api/ideation
+2025-08-17 15:24:08,120 | INFO | access_logger | api\main.py:21 | Response status: 200

logs/app.log

@@ -1,6 +1,35 @@
 2025-07-30 16:53:23,605 | INFO | app_logger | Showing Analytics of the influencers after context analysis.
 2025-08-12 17:03:25,262 | INFO | app_logger | Showing Analytics of the influencers after context analysis.
-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.
+2025-08-17 12:01:31,453 | INFO | app_logger | Checking the logging
+2025-08-17 12:01:35,639 | INFO | app_logger | Checking the logging
+2025-08-17 12:02:44,510 | INFO | app_logger | logger_config.py:65 | Checking the logging
+2025-08-17 12:06:40,293 | INFO | app_logger | Context Analysis agent initialized.
+2025-08-17 12:06:40,294 | INFO | app_logger | Details are extracted.
+2025-08-17 12:06:42,154 | INFO | app_logger | Context Analysis agent initialized.
+2025-08-17 12:06:48,500 | INFO | app_logger | logger_config.py:65 | Checking the logging
+2025-08-17 12:07:11,398 | INFO | app_logger | src\genai\context_analysis_agent\agent.py:21 | Context Analysis agent initialized.
+2025-08-17 12:07:11,398 | INFO | app_logger | src\genai\context_analysis_agent\agent.py:46 | Details are extracted.
+2025-08-17 12:07:12,641 | INFO | app_logger | src\genai\context_analysis_agent\agent.py:21 | Context Analysis agent initialized.
+2025-08-17 13:59:22,347 | INFO | app_logger | logger_config.py:65 | Checking the logging
+2025-08-17 14:02:00,651 | INFO | app_logger | api\routers\orchestration.py:20 | Orchestrator executed
+2025-08-17 14:02:00,651 | INFO | app_logger | api\routers\orchestration.py:20 | Orchestrator executed
+2025-08-17 14:02:52,612 | INFO | app_logger | api\routers\show_analytics.py:14 | Influencer Analytics returned by orchestrator.
+2025-08-17 14:02:52,612 | INFO | app_logger | api\routers\show_analytics.py:14 | Influencer Analytics returned by orchestrator.
+2025-08-17 14:03:33,201 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-17 14:03:33,201 | INFO | app_logger | api\routers\context_analysis.py:27 | Executed context analysis agent
+2025-08-17 14:04:55,698 | INFO | app_logger | api\routers\ideation.py:28 | Executed the ideation pipeline.
+2025-08-17 14:04:55,698 | INFO | app_logger | api\routers\ideation.py:28 | Executed the ideation pipeline.
+2025-08-17 14:06:14,161 | INFO | app_logger | api\routers\human_idea_refining.py:25 | Executed human ideation refining.
+2025-08-17 14:06:14,161 | INFO | app_logger | api\routers\human_idea_refining.py:25 | Executed human ideation refining.
+2025-08-17 14:06:31,243 | INFO | app_logger | api\routers\brainstorm.py:42 | Executed brainstorming agent.
+2025-08-17 14:06:31,243 | INFO | app_logger | api\routers\brainstorm.py:42 | Executed brainstorming agent.
+2025-08-17 14:07:16,281 | INFO | app_logger | api\routers\generate_final_story.py:17 | Executed final story generation.
+2025-08-17 14:07:16,281 | INFO | app_logger | api\routers\generate_final_story.py:17 | Executed final story generation.
+2025-08-17 14:07:40,022 | INFO | app_logger | api\routers\generate_image.py:18 | The image is Generated
+2025-08-17 14:07:40,022 | INFO | app_logger | api\routers\generate_image.py:18 | The image is Generated
+2025-08-17 14:13:45,517 | INFO | app_logger | api\routers\show_analytics.py:14 | Influencer Analytics returned by orchestrator.
+2025-08-17 15:22:52,597 | INFO | app_logger | api\routers\show_analytics.py:14 | Influencer Analytics returned by orchestrator.
+2025-08-17 15:24:08,119 | INFO | app_logger | api\routers\ideation.py:28 | Executed the ideation pipeline.

src/genai/brainstroming_agent/utils/tools.py

@@ -5,7 +5,7 @@ from src.genai.utils.models_loader import  embedding_model , llm
 import numpy as np
 import faiss
 import tiktoken
-from src.genai.utils.load_embeddings import  caption_index , caption_df
+from src.genai.utils.data_loader import  caption_index , caption_df
 from src.genai.utils.utils import clean_text
 
 class Retrieval:

src/genai/context_analysis_agent/agent.py

@@ -1,4 +1,4 @@
-from langchain_groq import ChatGroq
+import logging
 from langgraph.graph import StateGraph, MessagesState, START, END
 from langgraph.checkpoint.memory import MemorySaver
 from .utils.state import State 
@@ -6,6 +6,7 @@ from .utils.nodes import IntroductionNode
 from .utils.utils import DetailsExtractor
 from src.genai.utils.models_loader import llm_gpt
 
+
 business_state = State()
 
 class IntroductionChatbot:
@@ -16,6 +17,7 @@ class IntroductionChatbot:
         self.interact_agent = self.workflow.compile(checkpointer=self.memory)
         self.messages = []
 
+
     def _initialize_workflow(self):
         workflow = StateGraph(MessagesState)
         workflow.add_node("chatbot", lambda state: IntroductionNode().run(state, self.llm))
@@ -38,11 +40,11 @@ class IntroductionChatbot:
         return "Thanks for providing all your required business details" in latest_response
 
     def extract_details(self):
-        response = DetailsExtractor().run(business_state.interactions)
-        print('Extracted details:', response)
+        response = DetailsExtractor(business_state.interactions).run()
         return response
     
     def reset(self):
         self.memory= MemorySaver()
         self.interact_agent = self.workflow.compile(checkpointer=self.memory) 
-        print('Memory cleared')
+
+

src/genai/context_analysis_agent/utils/nodes.py

@@ -1,8 +1,8 @@
-from .state import DetailsFormatter
 from langchain_core.messages import SystemMessage
 from src.genai.utils.models_loader import llm_gpt
 from .prompts import introduction_prompt
 
+
 class IntroductionNode:
     def __init__(self):
         self.llm = llm_gpt

src/genai/context_analysis_agent/utils/utils.py

@@ -1,16 +1,17 @@
 import pandas as pd
-from src.genai.utils.load_embeddings import caption_df
+from src.genai.utils.data_loader 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):
+    def __init__(self, interactions):
         self.llm = llm_gpt
+        self.interactions = interactions
 
-    def run(interactions):
-        template = details_extract_prompt(interactions)
+    def run(self):
+        template = details_extract_prompt(self.interactions)
         messages = [SystemMessage(content=template)]
         response=llm_gpt.with_structured_output(DetailsFormatter).invoke(messages)
         return response

src/genai/ideation_agent/utils/tools.py

@@ -7,7 +7,7 @@ import ast
 import faiss
 import tiktoken
 from src.genai.utils.models_loader import embedding_model
-from src.genai.utils.load_embeddings import  caption_index , caption_df, ideas_index , ideas_df
+from src.genai.utils.data_loader import  caption_index , caption_df, ideas_index , ideas_df
 from src.genai.utils.utils import clean_text
 
 

src/genai/orchestration_agent/utils/tools.py

@@ -2,7 +2,7 @@ import faiss
 import ast
 import pandas as pd
 import numpy as np
-from src.genai.utils.load_embeddings import caption_df, caption_embeddings , caption_index
+from src.genai.utils.data_loader import caption_df, caption_embeddings , caption_index
 from src.genai.utils.models_loader import embedding_model
 from src.genai.utils.utils import clean_text
 import tiktoken

src/genai/orchestration_agent/utils/utils.py

@@ -8,9 +8,6 @@ from src.genai.utils.models_loader import llm
 from langchain_core.messages import FunctionMessage , AIMessage
 from .tools import  InfluencerRetrievalTool
 import re
-import logging
-app_logger = logging.getLogger("app_logger")
-error_logger = logging.getLogger("error_logger")
 
 class ImageCaptioner:
     def __init__(self, api_key=os.environ.get('GROQ_API_KEY')):
@@ -48,13 +45,9 @@ class AnalyticsViewer:
         self.business_details = business_details
         
     def show_analytics(self):
-        try:
-            tool_response = InfluencerRetrievalTool().retrieve_for_analytics(str(self.business_details))
-            app_logger.info('Showing Analytics of the influencers after context analysis.')
-            return tool_response
-        except Exception as e:
-            error_logger.error(f'Error while showing analytics: {e}')
-            return e
+        tool_response = InfluencerRetrievalTool().retrieve_for_analytics(str(self.business_details))
+        return tool_response
+
 
 class ResponseBlockExtractor:
     def __init__(self, response):

src/genai/utils/data_loader.py

@@ -1,11 +1,47 @@
-
-print('Loading Dataset..................')
+import ast
+import faiss
+import logging
+import numpy as np
+import pandas as pd
 from datasets import load_dataset
-# dataset = load_dataset("subashdvorak/tiktok-formatted-story-v2", revision="embedded")
-# data = dataset['train'].add_faiss_index('embeddings')
 
-def load_influencer_data():
-    return 'Some error occouring'
-print('Dataset loaded.................')
+app_logger = logging.getLogger("app_logger")
+error_logger = logging.getLogger("error_logger")
+
+class DataLoader:
+    def __init__(self):
+        self.caption_dataset = load_dataset("DvorakInnovationAI/rt-genai-dataset-v1", revision="openai-embeddings")
+        self.ideas_dataset = load_dataset("DvorakInnovationAI/rt-genai-imdb-ideas-v1", revision='openai-embeddings')
+    
+    def _load_vector_index(self , dataset):
+        df = dataset["train"].to_pandas()
+        df['embeddings'] = df['embeddings'].apply(lambda x: ast.literal_eval(x) if isinstance(x, str) else x)
+        embeddings = np.vstack(df['embeddings'].values).astype('float32')
+        faiss.normalize_L2(embeddings)
+        index = faiss.IndexFlatIP(embeddings.shape[1])
+        index.add(embeddings)
+        return df, embeddings, index
+    
+    def load_caption(self):
+        return self._load_vector_index(self.caption_dataset)
+    
+    def load_imdb_ideas(self):
+        return self._load_vector_index(self.ideas_dataset)
+
+try:
+    data_loader = DataLoader()
+    app_logger.info('Dataset loaded from Hugging Face.')
+    
+except Exception as e:
+    error_logger.error('Unable to load dataset:', e)
+    raise
+
+try:
+    caption_df, caption_embeddings, caption_index = data_loader.load_caption()
+    ideas_df , ideas_embeddings , ideas_index = data_loader.load_imdb_ideas()
+    app_logger.info('Loaded the embeddings.')
+
+except Exception as e:
+    error_logger.error('Unable to load the embeddings:', e)
 
 

src/genai/utils/load_embeddings.py

@@ -1,33 +0,0 @@
-import numpy as np
-import ast
-import faiss
-import pandas as pd
-from datasets import load_dataset
-
-
-def load_caption_index():
-    dataset = load_dataset("DvorakInnovationAI/rt-genai-dataset-v1", revision="openai-embeddings")
-    df = dataset["train"]
-    df= df.to_pandas()
-    df['embeddings'] = df['embeddings'].apply(lambda x: ast.literal_eval(x) if isinstance(x, str) else x)
-    embeddings = np.vstack(df['embeddings'].values).astype('float32')
-    faiss.normalize_L2(embeddings)
-    index = faiss.IndexFlatIP(embeddings.shape[1])
-    index.add(embeddings)
-    return df, embeddings, index
-
-def load_imdb_ideas_index():
-    dataset = load_dataset("DvorakInnovationAI/rt-genai-imdb-ideas-v1", revision='openai-embeddings')
-    df = dataset['train']
-    df= df.to_pandas()
-    df['embeddings'] = df['embeddings'].apply(lambda x: ast.literal_eval(x) if isinstance(x,str) else x)
-    embeddings = np.vstack(df['embeddings'].values).astype('float32')
-    faiss.normalize_L2(embeddings)
-    index = faiss.IndexFlatIP(embeddings.shape[1])
-    index.add(embeddings)
-    return df , embeddings , index
-
-print('Loading Embeddings...........')
-caption_df, caption_embeddings, caption_index = load_caption_index()
-ideas_df , ideas_embeddings , ideas_index = load_imdb_ideas_index()
-print('Embeddings Loaded.................')

src/genai/utils/models_loader.py

@@ -1,5 +1,4 @@
 import os
-import requests
 import numpy as np
 from langchain_groq import ChatGroq
 from langchain_openai import ChatOpenAI
@@ -8,57 +7,34 @@ from langchain_anthropic import ChatAnthropic
 from langchain_openai import OpenAIEmbeddings
 from huggingface_hub import login
 from dotenv import load_dotenv
+
 load_dotenv()
 os.environ['HUGGINGFACEHUB_ACCESS_TOKEN']=os.getenv('HUGGINGFACEHUB_ACCESS_TOKEN')
 login(os.environ['HUGGINGFACEHUB_ACCESS_TOKEN'])
-os.environ['GROQ_API_KEY']=os.getenv('GROQ_API_KEY')
+
 
 embedding_model = OpenAIEmbeddings(model="text-embedding-3-small", dimensions=1536)
 llm_anthropic = ChatAnthropic(model='claude-3-5-sonnet-20241022', temperature=0.7, max_tokens=500)
 llm_gemini = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
+llm_groq = ChatGroq(model="llama-3.1-8b-instant",temperature=0.7)
+llm_gpt = ChatOpenAI(model="gpt-4o-mini",temperature=0.3)
 
-llm = ChatGroq(
-    model="llama-3.1-8b-instant",
-    temperature=0.7,
-)
-
-llm_gpt = ChatOpenAI(
-    model="gpt-4o-mini",
-    temperature=0.3,
-)
-
-
-class HFEmbeddingAPI:
-    def __init__(self, api_url, token):
-        self.api_url = api_url
-        self.headers = {
-            "Authorization": f"Bearer {os.environ.get('HUGGINGFACEHUB_ACCESS_TOKEN')}",
-        }
-
-    def encode(self, texts):
-        if isinstance(texts, str):
-            texts = [texts]
-        response = requests.post(
-            self.api_url,
-            headers=self.headers,
-            json={"inputs": texts}
-        )
-        response.raise_for_status()
-        embeddings=response.json()
-        return np.array(embeddings[0]) if len(embeddings) == 1 else np.array(embeddings)
-
-# Instantiate your API-backed "SentenceTransformer"
-ST = HFEmbeddingAPI(
-    api_url="https://router.huggingface.co/hf-inference/models/mixedbread-ai/mxbai-embed-large-v1/pipeline/feature-extraction",
-    token=os.environ.get('HUGGINGFACEHUB_ACCESS_TOKEN')
-)
+captioning_model = "meta-llama/llama-4-scout-17b-16e-instruct"
+image_generation_model = "black-forest-labs/FLUX.1-schnell"
 
 improver_llm = llm_anthropic
 ideator_llm = llm_anthropic
 critic_llm = llm_anthropic
-validator_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"