Update app.py

app.py

@@ -1,45 +1,242 @@
+# 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)
+
+
+
+import gradio as gr
+from langdetect import detect
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline, GenerationConfig
 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"
+# 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
 
 
-# FastAPI setup
 app = FastAPI(title="Apollo RAG Medical Chatbot")
 
-
-# Generation settings
 generation_config = GenerationConfig(
     max_new_tokens=150,
     temperature=0.2,
@@ -49,168 +246,53 @@ generation_config = GenerationConfig(
     repetition_penalty=1.3,
 )
 
-# Text generation pipeline
-llm_pipeline = pipeline(
+# Create generation pipeline
+pipe = TextGenerationPipeline(
     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"
+    device=model.device.index if torch.cuda.is_available() else "cpu"
 )
 
-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)
+# Prompt formatter based on language
+def generate_prompt(message, history):
+    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. 
+        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:
-"""
-        
+- 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, 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}
+        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:"""
 
-# === ROUTES === #
-@app.get("/")
-async def root():
-    return {"message": "Medical QA API is running!"}
-
+# Chat function
 @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()
+def chat_fn(message, history):
+    prompt = generate_prompt(message, history)
+    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}
 
-        
-        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):
+# Gradio ChatInterface
+# demo = gr.ChatInterface(
+#     fn=chat_fn,
+#     title="🩺 Apollo Medical Chatbot",
+#     description="Multilingual (Arabic & English) medical Q&A chatbot powered by Apollo-7B model inference.",
+#     theme=gr.themes.Soft()
+# )
 
-    prompt = generate_prompt(query.question)
 
-    answer = qa_chain.run(prompt)
+# if __name__ == "__main__":
+#     demo.launch(share=True)
 
-    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)
-