app.py

# 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-7B"
# 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")


# # 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,
# )

# # 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
# )

# 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)

# vector_store = Qdrant(
#     client=qdrant_client,
#     collection_name=COLLECTION_NAME,
#     embeddings=embedding
# )

# 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"
# )

# 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 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:
# """
        
#     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!"}

# @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=60)

#         # docs = retriever.get_relevant_documents(query.question)
#         # if not docs:
#         #     logger.warning("No documents retrieved from Qdrant for the question.")
#         # else:
#         #     logger.debug(f"Retrieved documents: {[doc.page_content for doc in docs[:1]]}")

#         loop = asyncio.get_event_loop()
        
#         answer = await asyncio.wait_for(
#             # qa_chain.run(prompt, callbacks=[timeout_callback]),
#             loop.run_in_executor(None, qa_chain.run, prompt),
#             timeout=360
#         )

#         if not answer:
#             raise ValueError("Empty answer returned from model")

#         if 'Answer:' in answer:
#             response_text = answer.split('Answer:')[-1].strip()
#         elif 'الإجابة:' in answer:
#             response_text = answer.split('الإجابة:')[-1].strip()
#         else:
#             response_text = answer.strip()

        
#         return {
#             "status": "success",
#             "answer": answer,
#             "response": response_text,
#             "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)}
#         )

# @app.post("/chat")
# def chat(query: Query):

#     prompt = generate_prompt(query.question)

#     answer = qa_chain.run(prompt)

#     return {

#         "answer": answer
#     }

    

# # === 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)



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__)

# Load model and tokenizer
model_name = "FreedomIntelligence/Apollo-7B"
# model_name = "emilyalsentzer/Bio_ClinicalBERT"
# model_name = "FreedomIntelligence/Apollo-2B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

tokenizer.pad_token = tokenizer.eos_token

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"
)

# 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:"""

# 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

@app.get("/")
def read_root():
    return {"message": "Apollo Medical Chatbot API is running"}


# @app.post("/ask")
# async def chat_fn(query: Query):
    
#     message = query.message
#     logger.info(f"Received message: {message}")
    
#     prompt = generate_prompt(message)

#     # 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']
        )
        
        duration = time.time() - start_time
        logger.info(f"Model inference completed in {duration:.2f} seconds")

        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.")