added openai react

controller.py

@@ -27,7 +27,6 @@ import matplotlib
 import seaborn as sns
 from gemini_report_generator import generate_csv_report
 from intitial_q_handler import if_initial_chart_question, if_initial_chat_question
-from openai_react_agent_service import openai_react_chat
 from orchestrator_agent import csv_orchestrator_chat
 from supabase_service import upload_file_to_supabase
 from util_service import _prompt_generator, process_answer
@@ -345,10 +344,9 @@ async def csv_chat(request: Dict, authorization: str = Header(None)):
         query = request.get("query")
         csv_url = request.get("csv_url")
         decoded_url = unquote(csv_url)
-        detailed_answer = request.get("detailed_answer", False)
+        detailed_answer = request.get("detailed_answer")
         conversation_history = request.get("conversation_history", [])
-        generate_report = request.get("generate_report", False)
-        is_pro = request.get("is_pro", False)
+        generate_report = request.get("generate_report")
         
         if generate_report is True:
             report_files = await generate_csv_report(csv_url, query)
@@ -370,31 +368,22 @@ async def csv_chat(request: Dict, authorization: str = Header(None)):
            if orchestrator_answer is not None:
               return {"answer": jsonable_encoder(orchestrator_answer)}
 
-       # if the user is pro, then we use the openai_react_agent first
-        if is_pro is True:
-            openai_answer = await asyncio.to_thread(
-                openai_react_chat, decoded_url, query, False
-            )
-            logger.info("openai_answer:", openai_answer)
-            if openai_answer is not None:
-                return {"answer": jsonable_encoder(openai_answer)}
-
         # Process with groq_chat first
-        # groq_answer = await asyncio.to_thread(groq_chat, decoded_url, query)
-        # logger.info("groq_answer:", groq_answer)
+        groq_answer = await asyncio.to_thread(groq_chat, decoded_url, query)
+        logger.info("groq_answer:", groq_answer)
         
-        # if process_answer(groq_answer) == "Empty response received.":
-        #     return {"answer": "Sorry, I couldn't find relevant data..."}
+        if process_answer(groq_answer) == "Empty response received.":
+            return {"answer": "Sorry, I couldn't find relevant data..."}
 
-        # if process_answer(groq_answer):
-        #     lang_answer = await asyncio.to_thread(
-        #         langchain_csv_chat, decoded_url, query, False
-        #     )
-        #     if process_answer(lang_answer):
-        #         return {"answer": "error"}
-        #     return {"answer": jsonable_encoder(lang_answer)}
+        if process_answer(groq_answer):
+            lang_answer = await asyncio.to_thread(
+                langchain_csv_chat, decoded_url, query, False
+            )
+            if process_answer(lang_answer):
+                return {"answer": "error"}
+            return {"answer": jsonable_encoder(lang_answer)}
 
-        # return {"answer": jsonable_encoder(groq_answer)}
+        return {"answer": jsonable_encoder(groq_answer)}
 
     except Exception as e:
         logger.error(f"Error processing request: {str(e)}")
@@ -862,7 +851,6 @@ async def csv_chart(request: dict, authorization: str = Header(None)):
         detailed_answer = request.get("detailed_answer", False)
         conversation_history = request.get("conversation_history", [])
         generate_report = request.get("generate_report", False)
-        is_pro = request.get("is_pro", False)
         
         if generate_report is True:
             report_files = await generate_csv_report(csv_url, query)
@@ -893,52 +881,38 @@ async def csv_chart(request: dict, authorization: str = Header(None)):
         
            if orchestrator_answer is not None:
              return {"orchestrator_response": jsonable_encoder(orchestrator_answer)}
-         
-        # If user have a pro subscription start with openai-reAct agent
-        if is_pro is True:
-            openai_react_answer = await asyncio.to_thread(
-                process_executor, openai_react_chat, csv_url, query, True
-            )
-            if openai_react_answer is not None:
-               chart_path = openai_react_answer
-               logger.info("Uploading the chart to supabase...")
-               unique_file_name =f'{str(uuid.uuid4())}.png'
-               image_public_url = await upload_file_to_supabase(f"{chart_path}", unique_file_name)
-               logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
-               os.remove(chart_path)
-               return {"image_url": image_public_url}
-           
+        
         # Next, try the groq-based method
-        # groq_result = await loop.run_in_executor(
-        #     process_executor, groq_chart, csv_url, query
-        # )
-        # logger.info(f"Groq chart result: {groq_result}")
-        # if isinstance(groq_result, str) and groq_result != "Chart not generated":
-        #     unique_file_name =f'{str(uuid.uuid4())}.png'
-        #     logger.info("Uploading the chart to supabase...")
-        #     image_public_url = await upload_file_to_supabase(f"{groq_result}", unique_file_name)
-        #     logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
-        #     os.remove(groq_result)
-        #     return {"image_url": image_public_url}
-        #     # return FileResponse(groq_result, media_type="image/png")
+        groq_result = await loop.run_in_executor(
+            process_executor, groq_chart, csv_url, query
+        )
+        logger.info(f"Groq chart result: {groq_result}")
+        if isinstance(groq_result, str) and groq_result != "Chart not generated":
+            unique_file_name =f'{str(uuid.uuid4())}.png'
+            logger.info("Uploading the chart to supabase...")
+            image_public_url = await upload_file_to_supabase(f"{groq_result}", unique_file_name)
+            logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
+            os.remove(groq_result)
+            return {"image_url": image_public_url}
+            # return FileResponse(groq_result, media_type="image/png")
           
-        # # Fallback: try langchain-based again
-        # logger.error("Groq chart generation failed, trying langchain....")
-        # langchain_paths = await loop.run_in_executor(
-        #     process_executor, langchain_csv_chart, csv_url, query, True
-        # )
-        # logger.info("Fallback langchain chart result:", langchain_paths)
-        # if isinstance(langchain_paths, list) and len(langchain_paths) > 0:
-        #    unique_file_name =f'{str(uuid.uuid4())}.png'
-        #    logger.info("Uploading the chart to supabase...")
-        #    image_public_url = await upload_file_to_supabase(f"{langchain_paths[0]}", unique_file_name)
-        #    logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
-        #    os.remove(langchain_paths[0])
-        #    return {"image_url": image_public_url}
-        #    # return FileResponse(langchain_paths[0], media_type="image/png")
-        # else:
-        #    logger.error("All chart generation methods failed")
-        #    return {"answer": "error"}
+        # Fallback: try langchain-based again
+        logger.error("Groq chart generation failed, trying langchain....")
+        langchain_paths = await loop.run_in_executor(
+            process_executor, langchain_csv_chart, csv_url, query, True
+        )
+        logger.info("Fallback langchain chart result:", langchain_paths)
+        if isinstance(langchain_paths, list) and len(langchain_paths) > 0:
+           unique_file_name =f'{str(uuid.uuid4())}.png'
+           logger.info("Uploading the chart to supabase...")
+           image_public_url = await upload_file_to_supabase(f"{langchain_paths[0]}", unique_file_name)
+           logger.info("Image uploaded to Supabase and Image URL is... ", {image_public_url})
+           os.remove(langchain_paths[0])
+           return {"image_url": image_public_url}
+           # return FileResponse(langchain_paths[0], media_type="image/png")
+        else:
+           logger.error("All chart generation methods failed")
+           return {"answer": "error"}
 
     except Exception as e:
         logger.error(f"Critical chart error: {str(e)}")