Create app.py

app.py

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+
+
+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()