app.py

import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_name = "mistralai/Mistral-7B-Instruct-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    load_in_4bit=True,
    torch_dtype=torch.float16
)

generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=200,
    temperature=0.3,
    repetition_penalty=1.1
)

def is_unsafe(query):
    blocked = [
        "dose", "dosage", "how much",
        "diagnose", "prescribe",
        "medicine for", "treatment", "cure"
    ]
    return any(word in query.lower() for word in blocked)

def health_chatbot(user_input):
    if is_unsafe(user_input):
        return (
            "I can’t provide diagnosis or medication instructions. "
            "Please consult a qualified healthcare professional."
        )

    prompt = f"""
You are a general health information assistant.

Rules:
- Do NOT diagnose diseases.
- Do NOT prescribe medicines or give dosages.
- Provide general causes, symptoms, and prevention only.
- Keep answers simple and clear.
- End with: 'If symptoms persist, consult a healthcare professional.'

Question:
{user_input}

Answer:
"""

    output = generator(prompt)[0]["generated_text"]
    return output.split("Answer:")[-1].strip()

demo = gr.Interface(
    fn=health_chatbot,
    inputs=gr.Textbox(lines=2),
    outputs="text",
    title="🩺 General Health Query Chatbot"
)

demo.launch()