Update app.py

app.py

@@ -1,50 +1,80 @@
-import gradio as gr
+import os
+import requests
 from transformers import pipeline
 
-# Initialize medical LLM (using smaller model for Spaces compatibility)
-med_llm = pipeline("text-generation", model="distilgpt2")
+# 🔐 Securely get your Hugging Face API token from environment variables
+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}"}
 
-def diagnose(symptoms):
-    prompt = f"""Act as an AI doctor. Analyze these symptoms:
-{symptoms}
+# Optionally load a local model (useful for fallback/testing)
+try:
+    classifier = pipeline("text-classification", model="bert-base-cased")
+except:
+    classifier = None
 
-Possible diagnoses from most to least likely:
-1."""
-    
-    diagnosis = med_llm(
-        prompt,
-        max_length=400,
-        num_return_sequences=1,
-        temperature=0.7
-    )[0]['generated_text']
-    
-    # Extract just the diagnosis part
-    return diagnosis.split("1.", 1)[-1].strip()
+# 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"]
+}
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🌍 Global Symptom Checker")
-    gr.Markdown("Enter symptoms to get possible diagnoses")
-    
-    with gr.Row():
-        symptoms = gr.Textbox(label="Describe your symptoms", lines=3)
-        submit = gr.Button("Diagnose")
-    
-    output = gr.Textbox(label="Possible Conditions", interactive=False)
-    
-    gr.Examples(
-        examples=[
-            ["fever, headache, muscle pain"],
-            ["cough, shortness of breath, fatigue"],
-            ["rash, joint pain, red eyes"]
-        ],
-        inputs=symptoms
-    )
-    
-    submit.click(diagnose, inputs=symptoms, outputs=output)
+# 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.",
+    "covid-19": "Isolate, monitor oxygen levels, and seek medical attention if necessary."
+}
+
+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
+        else:
+            return "unknown"
+    except Exception as e:
+        print("API Error:", e)
+        return "unknown"
+
+def get_symptoms(disease):
+    return disease_symptoms.get(disease.lower(), "No data available.")
+
+def provide_assistance(disease):
+    return medical_guidelines.get(disease.lower(), "Consult a healthcare professional.")
+
+if __name__ == "__main__":
+    print("🤖 Welcome to the AI Health Assistant!")
     
-    gr.Markdown("""
-    ⚠️ **Disclaimer**: This AI provides general information only. 
-    Always consult a real doctor for medical advice.
-    """)
+    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
+
+        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)
 
-demo.launch()
+            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}")