Update app.py

app.py

@@ -1,57 +1,32 @@
-import os
-import requests
 import gradio as gr
+from transformers import pipeline
 
-# Hugging Face Inference API
-API_URL = "https://api-inference.huggingface.co/models/sunnwei/ClinicalBERT-finetuned-disease"
-headers = {"Authorization": f"Bearer {os.environ['HF_API_TOKEN']}"}
+# Load the model from Hugging Face Hub
+generator = pipeline("text-generation", model="ibm-granite/granite-3.3-2b-instruct", device_map="auto")
 
-# Disease data
-disease_symptoms = {
-    "flu": ["fever", "cough", "sore throat", "fatigue"],
-    "diabetes": ["increased thirst", "frequent urination", "weight loss", "fatigue"],
-    "covid-19": ["fever", "dry cough", "loss of taste or smell", "difficulty breathing"]
-}
+def predict_disease(symptoms):
+    prompt = f"User has the following symptoms: {symptoms}. What is the most probable disease?"
+    result = generator(prompt, max_new_tokens=100, do_sample=True)[0]["generated_text"]
+    return result.split(prompt)[-1].strip()
 
-medical_guidelines = {
-    "flu": "Drink warm fluids, rest, and take antiviral medication if severe.",
-    "diabetes": "Monitor blood sugar, maintain a healthy diet, and exercise.",
-    "covid-19": "Isolate, monitor oxygen levels, and seek medical attention if necessary."
-}
+def home_remedy(disease):
+    prompt = f"Suggest a natural home remedy for the disease: {disease}."
+    result = generator(prompt, max_new_tokens=100, do_sample=True)[0]["generated_text"]
+    return result.split(prompt)[-1].strip()
 
-def identify_disease(symptoms):
-    try:
-        response = requests.post(API_URL, headers=headers, json={"inputs": symptoms})
-        response.raise_for_status()
-        prediction = response.json()
-        return prediction[0]["label"]
-    except Exception:
-        return "Error contacting API."
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 HealthAI")
 
-def get_symptoms(disease):
-    return disease_symptoms.get(disease.lower(), "No data available.")
+    with gr.Tab("🔍 Symptoms Identifier"):
+        symptoms_input = gr.Textbox(label="Enter your symptoms")
+        symptoms_output = gr.Textbox(label="Predicted Disease")
+        symptoms_btn = gr.Button("Predict Disease")
+        symptoms_btn.click(fn=predict_disease, inputs=symptoms_input, outputs=symptoms_output)
 
-def provide_assistance(disease):
-    return medical_guidelines.get(disease.lower(), "Consult a healthcare professional.")
+    with gr.Tab("🌿 Home Remedies"):
+        disease_input = gr.Textbox(label="Enter your disease")
+        remedy_output = gr.Textbox(label="Suggested Remedy")
+        remedy_btn = gr.Button("Get Remedy")
+        remedy_btn.click(fn=home_remedy, inputs=disease_input, outputs=remedy_output)
 
-def ai_health_assistant(user_input):
-    if not user_input.strip():
-        return "Please enter your symptoms."
-
-    if user_input.lower() in disease_symptoms:
-        symptoms = get_symptoms(user_input)
-        advice = provide_assistance(user_input)
-        return f"✅ Known Disease: **{user_input.title()}**\n\nSymptoms: {symptoms}\nAdvice: {advice}"
-    else:
-        disease = identify_disease(user_input)
-        symptoms = get_symptoms(disease)
-        advice = provide_assistance(disease)
-        return f"🧠 Predicted Disease: **{disease}**\n\nSymptoms: {symptoms}\nAdvice: {advice}"
-
-# Launch Gradio interface
-gr.Interface(fn=ai_health_assistant, 
-             inputs="text", 
-             outputs="markdown", 
-             title="🩺 AI Health Assistant",
-             description="Enter symptoms or disease name to get predictions and medical guidance.",
-             theme="soft").launch()
+demo.launch()