app.py

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

# Load Hugging Face token (paste yours below if needed)
os.environ['HF_TOKEN'] = 'HF_TOKEN'

model_id = "ibm-granite/granite-3.3-2b-instruct"
token = os.getenv("HF_TOKEN")

tokenizer = AutoTokenizer.from_pretrained(model_id, token=token)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, token=token)

# Core generation function
def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Four feature functions
def disease_prediction(symptoms):
    prompt = f"Patient has symptoms: {symptoms}. What could be the possible conditions?"
    return generate_response(prompt)

def treatment_plan(condition):
    prompt = f"What is the treatment plan for {condition}? Include medications, lifestyle changes, and follow-up."
    return generate_response(prompt)

def health_analytics(vitals):
    prompt = f"Analyze this health data and give insights: {vitals}"
    return generate_response(prompt)

def patient_chat(query):
    prompt = f"Medical Question: {query}"
    return generate_response(prompt)

# Custom CSS
custom_css = """
body {
    font-family: 'Segoe UI', sans-serif;
    background-color: #f8f9fa;
}
h1, h2 {
    color: #114B5F;
    font-weight: bold;
}
.gradio-container {
    padding: 20px !important;
}
textarea {
    border-radius: 10px !important;
    border: 1px solid #ccc !important;
}
button {
    background-color: #114B5F !important;
    color: white !important;
    border-radius: 8px !important;
    padding: 10px 16px !important;
}
.tabitem {
    background-color: #d6ecf3 !important;
    padding: 10px;
    border-radius: 10px;
}
"""

# Gradio Interface
with gr.Blocks(css=custom_css) as demo:
    gr.Markdown("# 🏥 HealthAI - Generative Healthcare Assistant")

    with gr.Tab("🧠 Disease Prediction"):
        with gr.Column():
            symptom_input = gr.Textbox(label="Enter your symptoms")
            disease_output = gr.Textbox(label="Predicted Conditions")
            predict_btn = gr.Button("Predict")
            predict_btn.click(disease_prediction, inputs=symptom_input, outputs=disease_output)

    with gr.Tab("💊 Treatment Plans"):
        with gr.Column():
            condition_input = gr.Textbox(label="Enter diagnosed condition")
            treatment_output = gr.Textbox(label="Recommended Treatment")
            treatment_btn = gr.Button("Get Treatment Plan")
            treatment_btn.click(treatment_plan, inputs=condition_input, outputs=treatment_output)

    with gr.Tab("📊 Health Analytics"):
        with gr.Column():
            vitals_input = gr.Textbox(label="Enter vitals (e.g., heart rate: 80, BP: 120/80...)")
            analytics_output = gr.Textbox(label="AI Insights")
            analytics_btn = gr.Button("Analyze")
            analytics_btn.click(health_analytics, inputs=vitals_input, outputs=analytics_output)

    with gr.Tab("💬 Patient Chat"):
        with gr.Column():
            query_input = gr.Textbox(label="Ask a health-related question")
            chat_output = gr.Textbox(label="Response")
            chat_btn = gr.Button("Ask")
            chat_btn.click(patient_chat, inputs=query_input, outputs=chat_output)

demo.launch()