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app.py

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+# app.py — HealthSense AI
+# Deployed on Hugging Face Spaces (Gradio SDK)
+
+import json
+import os
+
+import gradio as gr
+import joblib
+from groq import Groq
+
+# --- Load trained ML artifacts ---
+MODEL_DIR = "models"
+model = joblib.load(os.path.join(MODEL_DIR, "disease_model.joblib"))
+encoder = joblib.load(os.path.join(MODEL_DIR, "label_encoder.joblib"))
+
+with open(os.path.join(MODEL_DIR, "symptoms.json")) as f:
+    symptoms_list = json.load(f)
+
+# --- Groq client (reads GROQ_API_KEY from Space secrets) ---
+groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+
+def predict_disease(selected_symptoms):
+    """Run the Random Forest classifier on the selected symptoms."""
+    input_vec = [[1 if s in selected_symptoms else 0 for s in symptoms_list]]
+    proba = model.predict_proba(input_vec)[0]
+    top_idx = int(proba.argmax())
+    disease = encoder.inverse_transform([top_idx])[0]
+    confidence = round(float(proba[top_idx]) * 100, 1)
+    return disease, confidence
+
+
+def get_ai_guidance(disease, symptoms, confidence):
+    """Ask the LLM for a structured explanation of the predicted disease."""
+    prompt = f"""Patient symptoms: {', '.join(symptoms)}.
+ML model predicted: {disease} ({confidence}% confidence).
+
+Provide:
+1) What this disease is
+2) Common causes
+3) Severity level
+4) Home care steps
+5) When to see a doctor
+
+Keep it clear, empathetic, and under 300 words."""
+
+    try:
+        response = groq_client.chat.completions.create(
+            model="llama3-8b-8192",
+            messages=[{"role": "user", "content": prompt}],
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return (
+            "⚠️ Couldn't reach the AI guidance service right now "
+            f"({e}). The ML prediction above is still valid."
+        )
+
+
+def analyse(selected_symptoms):
+    if not selected_symptoms:
+        return "⚠️ Please select at least one symptom.", ""
+
+    disease, confidence = predict_disease(selected_symptoms)
+    guidance = get_ai_guidance(disease, selected_symptoms, confidence)
+    result = f"### 🩺 Predicted: **{disease}** — {confidence}% confidence"
+    return result, guidance
+
+
+DISCLAIMER = (
+    "⚠️ **Disclaimer:** HealthSense AI is an educational demo, not a substitute "
+    "for professional medical advice. Always consult a qualified doctor for "
+    "diagnosis and treatment."
+)
+
+with gr.Blocks(title="HealthSense AI", theme=gr.themes.Soft()) as app:
+    gr.Markdown("# 🏥 HealthSense AI\nSelect your symptoms for AI-powered disease prediction.")
+
+    symptom_input = gr.CheckboxGroup(
+        choices=symptoms_list, label="Select Symptoms"
+    )
+    btn = gr.Button("🔍 Analyse Symptoms", variant="primary")
+
+    ml_output = gr.Markdown(label="ML Prediction")
+    ai_output = gr.Markdown(label="AI Medical Guidance")
+
+    gr.Markdown(DISCLAIMER)
+
+    btn.click(fn=analyse, inputs=symptom_input, outputs=[ml_output, ai_output])
+
+if __name__ == "__main__":
+    app.launch()

requirements.txt

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+gradio>=4.0.0
+scikit-learn>=1.3.0
+joblib>=1.3.0
+numpy>=1.24.0
+pandas>=2.0.0
+groq>=0.9.0