app.py

# import gradio as gr
# from huggingface_hub import InferenceClient
# import os

# """
# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# """

# token = os.getenv("HF_TOKEN")  
# client = InferenceClient("emilyalsentzer/Bio_ClinicalBERT", token=token)


# def respond(
#     message,
#     history: list[tuple[str, str]],
#     system_message,
#     max_tokens,
#     temperature,
#     top_p,
# ):
#     messages = [{"role": "system", "content": system_message}]

#     for val in history:
#         if val[0]:
#             messages.append({"role": "user", "content": val[0]})
#         if val[1]:
#             messages.append({"role": "assistant", "content": val[1]})

#     messages.append({"role": "user", "content": message})

#     response = ""

#     for message in client.chat_completion(
#         messages,
#         max_tokens=max_tokens,
#         stream=True,
#         temperature=temperature,
#         top_p=top_p,
#     ):
#         token = message.choices[0].delta.content

#         response += token
#         yield response


# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# demo = gr.ChatInterface(
#     respond,
#     additional_inputs=[
#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
#         gr.Slider(
#             minimum=0.1,
#             maximum=1.0,
#             value=0.95,
#             step=0.05,
#             label="Top-p (nucleus sampling)",
#         ),
#     ],
# )


# if __name__ == "__main__":
#     demo.launch()


# import gradio as gr
# from langdetect import detect
# from transformers import pipeline
# from qdrant_client import QdrantClient
# from qdrant_client.models import VectorParams, Distance
# from langchain.llms import HuggingFacePipeline
# from langchain.chains import RetrievalQA
# from langchain.vectorstores import Qdrant
# from transformers import GenerationConfig, AutoTokenizer, AutoModelForCausalLM
# from langchain.embeddings import HuggingFaceEmbeddings
# import os

# QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
# QDRANT_URL = os.getenv("QDRANT_URL")


# # Define model path
# model_name = "FreedomIntelligence/Apollo-7B"

# # Load model directly
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# model = AutoModelForCausalLM.from_pretrained(model_name)

# # Enable padding token if missing
# tokenizer.pad_token = tokenizer.eos_token

# # Set up Qdrant vector store
# qdrant_client = QdrantClient(url=QDRANT_URL, api_key = QDRANT_API_KEY)
# vector_size = 768
# embedding = HuggingFaceEmbeddings(model_name="Omartificial-Intelligence-Space/GATE-AraBert-v1")

# qdrant_vectorstore = Qdrant(
#     client=qdrant_client,
#     collection_name="arabic_rag_collection",
#     embeddings=embedding
# )

# # Generation config
# 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 HuggingFace Pipeline
# llm_pipeline = pipeline(
#     model=model,
#     tokenizer=tokenizer,
#     task="text-generation",
#     generation_config=generation_config,
# )

# llm = HuggingFacePipeline(pipeline=llm_pipeline)

# # Set up QA Chain
# qa_chain = RetrievalQA.from_chain_type(
#     llm=llm,
#     retriever=qdrant_vectorstore.as_retriever(search_kwargs={"k": 3}),
#     chain_type="stuff"
# )

# # Generate prompt based on language
# 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:"""

# # Define Gradio interface function
# def medical_chatbot(question):
#     formatted_question = generate_prompt(question)
#     answer = qa_chain.run(formatted_question)
#     return answer

# # Set up Gradio interface
# iface = gr.Interface(
#     fn=medical_chatbot,
#     inputs=gr.Textbox(label="Ask a Medical Question", placeholder="Type your question here..."),
#     outputs=gr.Textbox(label="Answer", interactive=False),
#     title="Medical Chatbot",
#     description="Ask medical questions and get detailed answers in Arabic or English.",
#     theme="compact"
# )

# # Launch Gradio interface
# if __name__ == "__main__":
#     iface.launch()


import gradio as gr
from langdetect import detect
from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline, GenerationConfig
import torch

# 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

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, 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.
 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
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

# 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()
)

if __name__ == "__main__":
    demo.launch(share=True)