Update app.py

app.py

@@ -1,344 +1,344 @@
-import torch
-import asyncio
-import logging
-import signal
-import uvicorn
-import os 
-
-from fastapi import FastAPI, Request, HTTPException, status
-from pydantic import BaseModel, Field
-from langdetect import detect
-
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, GenerationConfig
-from langchain.vectorstores import Qdrant
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.chains import RetrievalQA
-from langchain.llms import HuggingFacePipeline
-from qdrant_client import QdrantClient
-from langchain.callbacks.base import BaseCallbackHandler
-from huggingface_hub import hf_hub_download
-from contextlib import asynccontextmanager
-
-# Get environment variables
-COLLECTION_NAME = "arabic_rag_collection"
-QDRANT_URL = os.getenv("QDRANT_URL", "https://12efeef2-9f10-4402-9deb-f070977ddfc8.eu-central-1-0.aws.cloud.qdrant.io:6333")
-QDRANT_API_KEY = os.getenv("QDRANT_API_KEY", "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.Jb39rYQW2rSE9RdXrjdzKY6T1RF44XjdQzCvzFkjat4")
-
-# === LOGGING === #
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
-
-# Load model and tokenizer
-model_name = "FreedomIntelligence/Apollo-2B"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer.pad_token = tokenizer.eos_token
-
-
-# FastAPI setup
-app = FastAPI(title="Apollo RAG Medical Chatbot")
+# import torch
+# import asyncio
+# import logging
+# import signal
+# import uvicorn
+# import os 
 
+# from fastapi import FastAPI, Request, HTTPException, status
+# from pydantic import BaseModel, Field
+# from langdetect import detect
 
-# Generation settings
-generation_config = GenerationConfig(
-    max_new_tokens=150,
-    temperature=0.2,
-    top_k=20,
-    do_sample=True,
-    top_p=0.7,
-    repetition_penalty=1.3,
-)
+# from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, GenerationConfig
+# from langchain.vectorstores import Qdrant
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.chains import RetrievalQA
+# from langchain.llms import HuggingFacePipeline
+# from qdrant_client import QdrantClient
+# from langchain.callbacks.base import BaseCallbackHandler
+# from huggingface_hub import hf_hub_download
+# from contextlib import asynccontextmanager
+
+# # Get environment variables
+# COLLECTION_NAME = "arabic_rag_collection"
+# QDRANT_URL = os.getenv("QDRANT_URL", "https://12efeef2-9f10-4402-9deb-f070977ddfc8.eu-central-1-0.aws.cloud.qdrant.io:6333")
+# QDRANT_API_KEY = os.getenv("QDRANT_API_KEY", "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.Jb39rYQW2rSE9RdXrjdzKY6T1RF44XjdQzCvzFkjat4")
+
+# # === LOGGING === #
+# logging.basicConfig(level=logging.DEBUG)
+# logger = logging.getLogger(__name__)
 
-# Text generation pipeline
-llm_pipeline = pipeline(
-    model=model,
-    tokenizer=tokenizer,
-    task="text-generation",
-    generation_config=generation_config,
-    device=model.device.index if model.device.type == "cuda" else -1
-)
+# # Load model and tokenizer
+# model_name = "FreedomIntelligence/Apollo-2B"
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# model = AutoModelForCausalLM.from_pretrained(model_name)
+# tokenizer.pad_token = tokenizer.eos_token
 
-llm = HuggingFacePipeline(pipeline=llm_pipeline)
 
-# Connect to Qdrant + embedding
-embedding = HuggingFaceEmbeddings(model_name="Omartificial-Intelligence-Space/GATE-AraBert-v1")
-qdrant_client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
+# # FastAPI setup
+# app = FastAPI(title="Apollo RAG Medical Chatbot")
 
-vector_store = Qdrant(
-    client=qdrant_client,
-    collection_name=COLLECTION_NAME,
-    embeddings=embedding
-)
 
-retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+# # Generation settings
+# generation_config = GenerationConfig(
+#     max_new_tokens=150,
+#     temperature=0.2,
+#     top_k=20,
+#     do_sample=True,
+#     top_p=0.7,
+#     repetition_penalty=1.3,
+# )
 
-# Set up RAG QA chain
-qa_chain = RetrievalQA.from_chain_type(
-    llm=llm,
-    retriever=retriever,
-    chain_type="stuff"
-)
+# # Text generation pipeline
+# llm_pipeline = pipeline(
+#     model=model,
+#     tokenizer=tokenizer,
+#     task="text-generation",
+#     generation_config=generation_config,
+#     device=model.device.index if model.device.type == "cuda" else -1
+# )
 
-class Query(BaseModel):
-    question: str = Field(..., example="ما هي اسباب تساقط الشعر ؟", min_length=3)
+# llm = HuggingFacePipeline(pipeline=llm_pipeline)
 
-class TimeoutCallback(BaseCallbackHandler):
-    def __init__(self, timeout_seconds: int = 60):
-        self.timeout_seconds = timeout_seconds
-        self.start_time = None
+# # Connect to Qdrant + embedding
+# embedding = HuggingFaceEmbeddings(model_name="Omartificial-Intelligence-Space/GATE-AraBert-v1")
+# qdrant_client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
 
-    async def on_llm_start(self, *args, **kwargs):
-        self.start_time = asyncio.get_event_loop().time()
+# vector_store = Qdrant(
+#     client=qdrant_client,
+#     collection_name=COLLECTION_NAME,
+#     embeddings=embedding
+# )
 
-    async def on_llm_new_token(self, *args, **kwargs):
-        if asyncio.get_event_loop().time() - self.start_time > self.timeout_seconds:
-            raise TimeoutError("LLM processing timeout")
+# retriever = vector_store.as_retriever(search_kwargs={"k": 3})
 
+# # Set up RAG QA chain
+# qa_chain = RetrievalQA.from_chain_type(
+#     llm=llm,
+#     retriever=retriever,
+#     chain_type="stuff"
+# )
 
-# def generate_prompt(question: str) -> str:
+# class Query(BaseModel):
+#     question: str = Field(..., example="ما هي اسباب تساقط الشعر ؟", min_length=3)
+
+# class TimeoutCallback(BaseCallbackHandler):
+#     def __init__(self, timeout_seconds: int = 60):
+#         self.timeout_seconds = timeout_seconds
+#         self.start_time = None
+
+#     async def on_llm_start(self, *args, **kwargs):
+#         self.start_time = asyncio.get_event_loop().time()
+
+#     async def on_llm_new_token(self, *args, **kwargs):
+#         if asyncio.get_event_loop().time() - self.start_time > self.timeout_seconds:
+#             raise TimeoutError("LLM processing timeout")
+
+
+# # def generate_prompt(question: str) -> str:
+# #     lang = detect(question)
+# #     if lang == "ar":
+# #         return (
+# #             "أجب على السؤال الطبي التالي بلغة عربية فصحى، بإجابة دقيقة ومفصلة. إذا لم تجد معلومات كافية في السياق، استخدم معرفتك الطبية السابقة. \n"
+# #             "- عدم تكرار أي نقطة أو عبارة أو كلمة\n"
+# #             "- وضوح وسلاسة كل نقطة\n"
+# #             "- تجنب الحشو والعبارات الزائدة\n"
+# #             f"\nالسؤال: {question}\nالإجابة:"
+# #         )
+# #     else:
+# #         return (
+# #             "Answer the following medical question in clear English with a detailed, non-redundant response. "
+# #             "Do not repeat ideas, phrases, or restate the question in the answer. If the context lacks relevant "
+# #             "information, rely on your prior medical knowledge. If the answer involves multiple points, list them "
+# #             "in concise and distinct bullet points:\n"
+# #             f"Question: {question}\nAnswer:"
+# #         )
+
+# def generate_prompt(question):
 #     lang = detect(question)
 #     if lang == "ar":
-#         return (
-#             "أجب على السؤال الطبي التالي بلغة عربية فصحى، بإجابة دقيقة ومفصلة. إذا لم تجد معلومات كافية في السياق، استخدم معرفتك الطبية السابقة. \n"
-#             "- عدم تكرار أي نقطة أو عبارة أو كلمة\n"
-#             "- وضوح وسلاسة كل نقطة\n"
-#             "- تجنب الحشو والعبارات الزائدة\n"
-#             f"\nالسؤال: {question}\nالإجابة:"
-#         )
-#     else:
-#         return (
-#             "Answer the following medical question in clear English with a detailed, non-redundant response. "
-#             "Do not repeat ideas, phrases, or restate the question in the answer. If the context lacks relevant "
-#             "information, rely on your prior medical knowledge. If the answer involves multiple points, list them "
-#             "in concise and distinct bullet points:\n"
-#             f"Question: {question}\nAnswer:"
-#         )
-
-def generate_prompt(question):
-    lang = detect(question)
-    if lang == "ar":
-        return f"""أجب على السؤال الطبي التالي بلغة عربية فصحى، بإجابة دقيقة ومفصلة. إذا لم تجد معلومات كافية في السياق، استخدم معرفتك الطبية السابقة. 
- وتأكد من ان:
-- عدم تكرار أي نقطة أو عبارة أو كلمة
-- وضوح وسلاسة كل نقطة
-- تجنب الحشو والعبارات الزائدة-
+#         return f"""أجب على السؤال الطبي التالي بلغة عربية فصحى، بإجابة دقيقة ومفصلة. إذا لم تجد معلومات كافية في السياق، استخدم معرفتك الطبية السابقة. 
+#  وتأكد من ان:
+# - عدم تكرار أي نقطة أو عبارة أو كلمة
+# - وضوح وسلاسة كل نقطة
+# - تجنب الحشو والعبارات الزائدة-
 
-السؤال: {question}
-الإجابة:
-"""
+# السؤال: {question}
+# الإجابة:
+# """
         
-    else:
-        return f"""Answer the following medical question in clear English with a detailed, non-redundant response. Do not repeat ideas, phrases, or restate the question in the answer. If the context lacks relevant information, rely on your prior medical knowledge. If the answer involves multiple points, list them in concise and distinct bullet points:
-Question: {question}
-Answer:"""
+#     else:
+#         return f"""Answer the following medical question in clear English with a detailed, non-redundant response. Do not repeat ideas, phrases, or restate the question in the answer. If the context lacks relevant information, rely on your prior medical knowledge. If the answer involves multiple points, list them in concise and distinct bullet points:
+# Question: {question}
+# Answer:"""
 
-# === ROUTES === #
-@app.get("/")
-async def root():
-    return {"message": "Medical QA API is running!"}
+# # === ROUTES === #
+# @app.get("/")
+# async def root():
+#     return {"message": "Medical QA API is running!"}
 
-@app.post("/ask")
-async def ask(query: Query):
-    try:
-        logger.debug(f"Received question: {query.question}")
-        prompt = generate_prompt(query.question)
-        timeout_callback = TimeoutCallback(timeout_seconds=360)
-        loop = asyncio.get_event_loop()
+# @app.post("/ask")
+# async def ask(query: Query):
+#     try:
+#         logger.debug(f"Received question: {query.question}")
+#         prompt = generate_prompt(query.question)
+#         timeout_callback = TimeoutCallback(timeout_seconds=360)
+#         loop = asyncio.get_event_loop()
         
-        response = await asyncio.wait_for(
-            # qa_chain.run(prompt, callbacks=[timeout_callback]),
-            loop.run_in_executor(None, qa_chain.run, prompt),
-            timeout=360
-        )
+#         response = await asyncio.wait_for(
+#             # qa_chain.run(prompt, callbacks=[timeout_callback]),
+#             loop.run_in_executor(None, qa_chain.run, prompt),
+#             timeout=360
+#         )
 
-        if not response:
-            raise ValueError("Empty answer returned from model")
+#         if not response:
+#             raise ValueError("Empty answer returned from model")
 
-        answer = response.split("Answer:")[-1].strip() if "Answer:" in response else response.split("الإجابة:")[-1].strip()
+#         answer = response.split("Answer:")[-1].strip() if "Answer:" in response else response.split("الإجابة:")[-1].strip()
         
-        return {
-            "status": "success",
-            "response": response,
-            "answer": answer,
-            "language": detect(query.question)
-        }
-
-    except TimeoutError as te:
-        logger.error("Request timed out", exc_info=True)
-        raise HTTPException(
-            status_code=status.HTTP_504_GATEWAY_TIMEOUT,
-            detail={"status": "error", "message": "Request timed out", "error": str(te)}
-        )
+#         return {
+#             "status": "success",
+#             "response": response,
+#             "answer": answer,
+#             "language": detect(query.question)
+#         }
+
+#     except TimeoutError as te:
+#         logger.error("Request timed out", exc_info=True)
+#         raise HTTPException(
+#             status_code=status.HTTP_504_GATEWAY_TIMEOUT,
+#             detail={"status": "error", "message": "Request timed out", "error": str(te)}
+#         )
 
-    except Exception as e:
-        logger.error(f"Unexpected error: {e}", exc_info=True)
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail={"status": "error", "message": "Internal server error", "error": str(e)}
-        )
+#     except Exception as e:
+#         logger.error(f"Unexpected error: {e}", exc_info=True)
+#         raise HTTPException(
+#             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+#             detail={"status": "error", "message": "Internal server error", "error": str(e)}
+#         )
 
-@app.post("/chat")
-def chat(query: Query):
+# @app.post("/chat")
+# def chat(query: Query):
 
-    logger.debug(f"Received question: {query.question}")
+#     logger.debug(f"Received question: {query.question}")
 
-    prompt = generate_prompt(query.question)
+#     prompt = generate_prompt(query.question)
 
-    response = qa_chain.run(prompt)
+#     response = qa_chain.run(prompt)
 
-    answer = response.split("Answer:")[-1].strip() if "Answer:" in response else response.split("الإجابة:")[-1].strip()
+#     answer = response.split("Answer:")[-1].strip() if "Answer:" in response else response.split("الإجابة:")[-1].strip()
     
 
-    return {
-        "response": response,
-        "answer": answer
-    }
+#     return {
+#         "response": response,
+#         "answer": answer
+#     }
 
     
 
-# === ENTRYPOINT === #
-if __name__ == "__main__":
-    def handle_exit(signum, frame):
-        print("Shutting down gracefully...")
-        exit(0)
+# # === ENTRYPOINT === #
+# if __name__ == "__main__":
+#     def handle_exit(signum, frame):
+#         print("Shutting down gracefully...")
+#         exit(0)
 
-    signal.signal(signal.SIGINT, handle_exit)
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+#     signal.signal(signal.SIGINT, handle_exit)
+#     import uvicorn
+#     uvicorn.run(app, host="0.0.0.0", port=8000)
 
 
 
-# from langdetect import detect
-# from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline, GenerationConfig
-# import torch
-# import logging
-# from fastapi import FastAPI, Request, HTTPException, status
-# from pydantic import BaseModel, Field
-# import time
-# import asyncio
-# from concurrent.futures import ThreadPoolExecutor
-# from fastapi.middleware.cors import CORSMiddleware
-
-# logging.basicConfig(level=logging.INFO)
-# logger = logging.getLogger(__name__)
+from langdetect import detect
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline, GenerationConfig
+import torch
+import logging
+from fastapi import FastAPI, Request, HTTPException, status
+from pydantic import BaseModel, Field
+import time
+import asyncio
+from concurrent.futures import ThreadPoolExecutor
+from fastapi.middleware.cors import CORSMiddleware
 
-# # Load model and tokenizer
-# model_name = "FreedomIntelligence/Apollo-7B"
-# # model_name = "emilyalsentzer/Bio_ClinicalBERT"
-# # model_name = "FreedomIntelligence/Apollo-2B"
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-# tokenizer = AutoTokenizer.from_pretrained(model_name)
-# model = AutoModelForCausalLM.from_pretrained(model_name)
+# Load model and tokenizer
+model_name = "FreedomIntelligence/Apollo-7B"
+# model_name = "emilyalsentzer/Bio_ClinicalBERT"
+# model_name = "FreedomIntelligence/Apollo-2B"
 
-# tokenizer.pad_token = tokenizer.eos_token
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# app = FastAPI(title="Apollo RAG Medical Chatbot")
+tokenizer.pad_token = tokenizer.eos_token
 
-# # Add this after creating the `app`
-# app.add_middleware(
-#     CORSMiddleware,
-#     allow_origins=["*"],  # Allow all origins
-#     allow_credentials=True,
-#     allow_methods=["*"],
-#     allow_headers=["*"],
-# )
+app = FastAPI(title="Apollo RAG Medical Chatbot")
 
+# Add this after creating the `app`
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allow all origins
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 
-# generation_config = GenerationConfig(
-#     max_new_tokens=150,
-#     temperature=0.2,
-#     top_k=20,
-#     do_sample=True,
-#     top_p=0.7,
-#     repetition_penalty=1.3,
-# )
 
-# # Create generation pipeline
-# pipe = TextGenerationPipeline(
-#     model=model,
-#     tokenizer=tokenizer,
-#     device=model.device.index if torch.cuda.is_available() else "cpu"
-# )
+generation_config = GenerationConfig(
+    max_new_tokens=150,
+    temperature=0.2,
+    top_k=20,
+    do_sample=True,
+    top_p=0.7,
+    repetition_penalty=1.3,
+)
 
-# # Prompt formatter based on language
-# def generate_prompt(message):
-#     lang = detect(message)
-#     if lang == "ar":
-#         return f"""أجب على السؤال الطبي التالي بلغة عربية فصحى، بإجابة دقيقة ومفصلة. إذا لم تجد معلومات كافية في السياق، استخدم معرفتك الطبية السابقة.
-#  وتأكد من ان:
-# - عدم تكرار أي نقطة أو عبارة أو كلمة
-# - وضوح وسلاسة كل نقطة
-# - تجنب الحشو والعبارات الزائدة
-# السؤال: {message}
-# الإجابة:"""
-#     else:
-#         return f"""Answer the following medical question in clear English with a detailed, non-redundant response. Do not repeat ideas or restate the question. If information is missing, rely on your prior medical knowledge:
-# Question: {message}
-# Answer:"""
+# Create generation pipeline
+pipe = TextGenerationPipeline(
+    model=model,
+    tokenizer=tokenizer,
+    device=model.device.index if torch.cuda.is_available() else "cpu"
+)
 
-# # Chat function
-# # @app.post("/ask")
-# # def chat_fn(message):
-# #     prompt = generate_prompt(message)
-# #     response = pipe(prompt,
-# #                     max_new_tokens=512,
-# #                     temperature=0.7,
-# #                     do_sample = True,
-# #                     top_p=0.9)[0]['generated_text']
-# #     answer = response.split("Answer:")[-1].strip() if "Answer:" in response else response.split("الإجابة:")[-1].strip()
-# #     return {"Answer": answer}
-
-# executor = ThreadPoolExecutor()
-
-# # Define request model
-# class Query(BaseModel):
-#     message: str
+# Prompt formatter based on language
+def generate_prompt(message):
+    lang = detect(message)
+    if lang == "ar":
+        return f"""أجب على السؤال الطبي التالي بلغة عربية فصحى، بإجابة دقيقة ومفصلة. إذا لم تجد معلومات كافية في السياق، استخدم معرفتك الطبية السابقة.
+ وتأكد من ان:
+- عدم تكرار أي نقطة أو عبارة أو كلمة
+- وضوح وسلاسة كل نقطة
+- تجنب الحشو والعبارات الزائدة
+السؤال: {message}
+الإجابة:"""
+    else:
+        return f"""Answer the following medical question in clear English with a detailed, non-redundant response. Do not repeat ideas or restate the question. If information is missing, rely on your prior medical knowledge:
+Question: {message}
+Answer:"""
 
-# @app.get("/")
-# def read_root():
-#     return {"message": "Apollo Medical Chatbot API is running"}
+# Chat function
+# @app.post("/ask")
+# def chat_fn(message):
+#     prompt = generate_prompt(message)
+#     response = pipe(prompt,
+#                     max_new_tokens=512,
+#                     temperature=0.7,
+#                     do_sample = True,
+#                     top_p=0.9)[0]['generated_text']
+#     answer = response.split("Answer:")[-1].strip() if "Answer:" in response else response.split("الإجابة:")[-1].strip()
+#     return {"Answer": answer}
 
+executor = ThreadPoolExecutor()
 
-# # @app.post("/ask")
-# # async def chat_fn(query: Query):
-    
-# #     message = query.message
-# #     logger.info(f"Received message: {message}")
-    
-# #     prompt = generate_prompt(message)
+# Define request model
+class Query(BaseModel):
+    message: str
 
-# #     # Run blocking inference in thread
-# #     loop = asyncio.get_event_loop()
-# #     response = await loop.run_in_executor(executor,
-# #                                           lambda: pipe(prompt, max_new_tokens=512, temperature=0.7, do_sample=True, top_p=0.9)[0]['generated_text'])
+@app.get("/")
+def read_root():
+    return {"message": "Apollo Medical Chatbot API is running"}
 
-# #     # Parse answer
-# #     answer = response.split("Answer:")[-1].strip() if "Answer:" in response else response.split("الإجابة:")[-1].strip()
-# #     return {"Answer": answer}
 
 # @app.post("/ask")
 # async def chat_fn(query: Query):
+    
 #     message = query.message
 #     logger.info(f"Received message: {message}")
     
 #     prompt = generate_prompt(message)
 
-#     try:
-#         start_time = time.time()
+#     # Run blocking inference in thread
+#     loop = asyncio.get_event_loop()
+#     response = await loop.run_in_executor(executor,
+#                                           lambda: pipe(prompt, max_new_tokens=512, temperature=0.7, do_sample=True, top_p=0.9)[0]['generated_text'])
+
+#     # Parse answer
+#     answer = response.split("Answer:")[-1].strip() if "Answer:" in response else response.split("الإجابة:")[-1].strip()
+#     return {"Answer": answer}
+
+@app.post("/ask")
+async def chat_fn(query: Query):
+    message = query.message
+    logger.info(f"Received message: {message}")
+    
+    prompt = generate_prompt(message)
+
+    try:
+        start_time = time.time()
         
-#         loop = asyncio.get_event_loop()
-#         response = await loop.run_in_executor(
-#             executor,
-#             lambda: pipe(prompt, max_new_tokens=150, temperature=0.7, do_sample=True, top_p=0.9)[0]['generated_text']
-#         )
+        loop = asyncio.get_event_loop()
+        response = await loop.run_in_executor(
+            executor,
+            lambda: pipe(prompt, max_new_tokens=150, temperature=0.6, do_sample=True, top_p=0.8)[0]['generated_text']
+        )
         
-#         duration = time.time() - start_time
-#         logger.info(f"Model inference completed in {duration:.2f} seconds")
+        duration = time.time() - start_time
+        logger.info(f"Model inference completed in {duration:.2f} seconds")
 
-#         logger.info(f"Generated answer: {answer}")
-#         return {"Answer": answer}
+        logger.info(f"Generated answer: {answer}")
+        return {"Answer": answer}
 
-#     except Exception as e:
-#         logger.error(f"Inference failed: {str(e)}")
-#         raise HTTPException(status_code=500, detail="Model inference TimeOut failed.")
+    except Exception as e:
+        logger.error(f"Inference failed: {str(e)}")
+        raise HTTPException(status_code=500, detail="Model inference TimeOut failed.")