Update app.py

app.py

@@ -1,26 +1,17 @@
 import os
 import requests
-from transformers import pipeline
+import gradio as gr
 
-# 🔐 Securely get your Hugging Face API token from environment variables
+API_URL = "https://api-inference.huggingface.co/models/your-medical-model"
 HF_API_TOKEN = os.environ.get("HF_API_TOKEN")
-API_URL = "https://api-inference.huggingface.co/models/your-medical-model"  # Replace with actual model URL
 headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
 
-# Optionally load a local model (useful for fallback/testing)
-try:
-    classifier = pipeline("text-classification", model="bert-base-cased")
-except:
-    classifier = None
-
-# Disease-to-symptoms mapping
 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"]
 }
 
-# Medical advice & precautions
 medical_guidelines = {
     "flu": "Drink warm fluids, rest, and take antiviral medication if severe.",
     "diabetes": "Monitor blood sugar, maintain a healthy diet, and exercise regularly.",
@@ -28,20 +19,16 @@ medical_guidelines = {
 }
 
 def identify_disease(symptoms):
-    """Identifies disease based on symptoms using Hugging Face Inference API."""
     payload = {"inputs": symptoms}
     try:
         response = requests.post(API_URL, headers=headers, json=payload, timeout=10)
         results = response.json()
-        
         if isinstance(results, list) and "label" in results[0]:
-            predicted_disease = results[0]["label"].lower()
-            return predicted_disease
+            return results[0]["label"].lower()
         else:
             return "unknown"
     except Exception as e:
-        print("API Error:", e)
-        return "unknown"
+        return "Error contacting API."
 
 def get_symptoms(disease):
     return disease_symptoms.get(disease.lower(), "No data available.")
@@ -49,32 +36,26 @@ def get_symptoms(disease):
 def provide_assistance(disease):
     return medical_guidelines.get(disease.lower(), "Consult a healthcare professional.")
 
-if __name__ == "__main__":
-    print("🤖 Welcome to the AI Health Assistant!")
-    
-    while True:
-        user_input = input("\nEnter your symptoms or a disease name (or type 'exit' to quit): ").strip()
-        
-        if user_input.lower() == "exit":
-            print("👋 Thank you for using the AI Health Assistant. Stay safe!")
-            break
+def assistant(user_input):
+    user_input = user_input.lower()
+    if user_input in disease_symptoms:
+        symptoms = get_symptoms(user_input)
+        advice = provide_assistance(user_input)
+        return f"Disease: {user_input.capitalize()}\nSymptoms: {', '.join(symptoms)}\nAdvice: {advice}"
+    else:
+        disease = identify_disease(user_input)
+        if disease == "unknown":
+            return "Sorry, could not identify the disease. Please consult a healthcare provider."
+        symptoms = get_symptoms(disease)
+        advice = provide_assistance(disease)
+        return f"Predicted Disease: {disease.capitalize()}\nSymptoms: {', '.join(symptoms) if isinstance(symptoms, list) else symptoms}\nAdvice: {advice}"
+
+demo = gr.Interface(
+    fn=assistant,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your symptoms or disease name here..."),
+    outputs="text",
+    title="AI Health Assistant"
+)
 
-        if user_input.lower() in disease_symptoms:
-            disease = user_input.lower()
-            symptoms_list = get_symptoms(disease)
-            advice = provide_assistance(disease)
-            print(f"\n🦠 Disease: {disease.capitalize()}")
-            print(f"📋 Symptoms: {', '.join(symptoms_list)}")
-            print(f"💡 Advice: {advice}")
-        else:
-            print("\n🔍 Analyzing symptoms, please wait...")
-            predicted_disease = identify_disease(user_input)
-
-            if predicted_disease == "unknown":
-                print("❌ Sorry, we couldn't identify the disease. Please consult a healthcare provider.")
-            else:
-                symptoms_list = get_symptoms(predicted_disease)
-                advice = provide_assistance(predicted_disease)
-                print(f"\n🧠 Predicted Disease: {predicted_disease.capitalize()}")
-                print(f"📋 Associated Symptoms: {', '.join(symptoms_list) if isinstance(symptoms_list, list) else symptoms_list}")
-                print(f"💡 Medical Assistance: {advice}")
+if __name__ == "__main__":
+    demo.launch()