app.py

# -*- coding: utf-8 -*-
"""heai.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1CPgKNfxzP9sPf9nsHmsct1wlUuZL3XpL
"""

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

# Load model and tokenizer
model_name = "ibm-granite/granite-3.2-2b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto" if torch.cuda.is_available() else None
)

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# Function to generate LLM response
def generate_response(prompt, max_length=1024):
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
    if torch.cuda.is_available():
        inputs = {k: v.to(model.device) for k, v in inputs.items()}
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=max_length,
            temperature=0.7,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    response = response.replace(prompt, "").strip()
    return response

# Function for disease prediction
def disease_prediction(symptoms):
    prompt = f"""Based on the following symptoms, provide possible medical conditions and general medication suggestions.
Always emphasize the importance of consulting a doctor for proper diagnosis.

Symptoms: {symptoms}

Possible conditions and recommendations:

**IMPORTANT: This is for informational purposes only. Please consult a healthcare professional for proper diagnosis and treatment.**

Analysis:"""
    return generate_response(prompt, max_length=1200)

# Function for treatment plan
def treatment_plan(condition, age, gender, medical_history):
    prompt = f"""Generate personalized treatment suggestions for the following patient information. Include home remedies and general medication guidelines.

Medical Condition: {condition}
Age: {age}
Gender: {gender}
Medical History: {medical_history}

Personalized treatment plan including home remedies and medication guidelines:

**IMPORTANT: This is for informational purposes only. Please consult a healthcare professional for proper treatment.**

Treatment Plan:"""
    return generate_response(prompt, max_length=1200)

# Function for chat with patient
def patient_chat(chat_history, user_input):
    conversation = chat_history + f"\nPatient: {user_input}\nAI:"
    response = generate_response(conversation, max_length=800)
    chat_history += f"\nPatient: {user_input}\nAI: {response}"
    return chat_history, chat_history

# Build Gradio app
with gr.Blocks() as app:
    gr.Markdown("# Medical AI Assistant")
    gr.Markdown("**Disclaimer: This is for informational purposes only. Always consult healthcare professionals for medical advice.**")

    with gr.Tabs():
        with gr.TabItem("Patient Chat"):
            chat_history = gr.Textbox(label="Conversation", lines=15, value="", interactive=False)
            user_input = gr.Textbox(label="Your Message", placeholder="Describe your symptoms or ask questions...", lines=2)
            send_btn = gr.Button("Send")
            send_btn.click(patient_chat, inputs=[chat_history, user_input], outputs=[chat_history, chat_history])

        with gr.TabItem("Disease Prediction"):
            with gr.Row():
                with gr.Column():
                    symptoms_input = gr.Textbox(
                        label="Enter Symptoms",
                        placeholder="e.g., fever, headache, cough, fatigue...",
                        lines=4
                    )
                    predict_btn = gr.Button("Analyze Symptoms")
                with gr.Column():
                    prediction_output = gr.Textbox(label="Possible Conditions & Recommendations", lines=20)
            predict_btn.click(disease_prediction, inputs=symptoms_input, outputs=prediction_output)

        with gr.TabItem("Treatment Plans"):
            with gr.Row():
                with gr.Column():
                    condition_input = gr.Textbox(
                        label="Medical Condition",
                        placeholder="e.g., diabetes, hypertension, migraine...",
                        lines=2
                    )
                    age_input = gr.Number(label="Age", value=30)
                    gender_input = gr.Dropdown(
                        choices=["Male", "Female", "Other"],
                        label="Gender",
                        value="Male"
                    )
                    history_input = gr.Textbox(
                        label="Medical History",
                        placeholder="Previous conditions, allergies, medications or None",
                        lines=3
                    )
                    plan_btn = gr.Button("Generate Treatment Plan")
                with gr.Column():
                    plan_output = gr.Textbox(label="Personalized Treatment Plan", lines=20)
            plan_btn.click(
                treatment_plan,
                inputs=[condition_input, age_input, gender_input, history_input],
                outputs=plan_output
            )

    app.launch(server_name="0.0.0.0", server_port=8080, share=True)