Update app.py

app.py

@@ -1,71 +1,58 @@
+# ==============================
+# 🩺 Pediatric Respiratory Triage Assistant (Gradio App)
+# ==============================
+
 import gradio as gr
-from PIL import Image, ImageDraw
-import os
-import uuid
-import logging
 import joblib
+import os
 
-# ───── Configuration ───── #
-OUTPUT_DIR = "outputs"
-MODEL_PATH = "model_assets/model.pkl"
-os.makedirs(OUTPUT_DIR, exist_ok=True)
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# ───── Dummy Generator ───── #
-def generate_image_from_text(prompt, model=None):
-    image = Image.new("RGB", (512, 512), color="white")
-    draw = ImageDraw.Draw(image)
-    draw.text((10, 250), prompt, fill="#ff00cc")
-    return image
-
-# ───── Load Model ───── #
-def load_model():
-    try:
-        return joblib.load(MODEL_PATH) if os.path.exists(MODEL_PATH) else None
-    except Exception as e:
-        logger.error(f"Model load error: {e}")
-        return None
-
-model = load_model()
-
-# ───── Prediction Logic ───── #
-def predict(prompt):
-    try:
-        image = generate_image_from_text(prompt, model)
-        image_id = str(uuid.uuid4())
-        output_path = os.path.join(OUTPUT_DIR, f"{image_id}.png")
-        image.save(output_path)
-        return image, output_path
-    except Exception as e:
-        logger.error(f"Prediction error: {e}")
-        return None, None
-
-# ───── CSS ───── #
-cyber_light_css = """
-.gradio-container {
-    background: linear-gradient(135deg, #f0f0f0, #fafafa);
-    color: #ff00cc;
-    font-family: 'Orbitron', sans-serif;
-}
-button, input, .gr-button {
-    background-color: #ffffff;
-    color: #ff00cc;
-    border: 2px solid #ff00cc;
-    font-weight: bold;
-    border-radius: 5px;
-}
-"""
-
-# ───── UI ───── #
-with gr.Blocks(css=cyber_light_css) as demo:
-    gr.Markdown("## 🌐 Cyberpunk T2I Generator (Light Mode)")
-    prompt = gr.Textbox(label="Describe your neon dream 💭")
-    generate = gr.Button("⚡ Generate")
-    output_img = gr.Image()
-    download = gr.File(label="Download")
-
-    generate.click(fn=predict, inputs=prompt, outputs=[output_img, download])
-
+# ------------------------------
+# 📦 Load Trained Model
+# ------------------------------
+MODEL_PATH = "best_model_decision_tree.joblib"
+
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError(f"Model file not found: {MODEL_PATH}")
+
+model = joblib.load(MODEL_PATH)
+
+# ------------------------------
+# 💬 Triage Prediction Function
+# ------------------------------
+def triage_chatbot(symptom_text):
+    if not symptom_text or len(symptom_text.strip()) < 5:
+        return "⚠️ Please provide more detailed symptoms."
+
+    prediction = model.predict([symptom_text])[0]  # already returns string label
+
+    # Friendly message mapping
+    messages = {
+        "Monitor at Home": "🟢 Symptoms appear mild. It's okay to monitor at home unless they worsen.",
+        "Consult GP": "🟡 Please consult your GP. These symptoms may require medical evaluation.",
+        "Use Inhaler": "🫁 Consider using a prescribed inhaler if applicable. Monitor response closely.",
+        "Visit Emergency": "🔴 Seek emergency care immediately. These symptoms may be serious."
+    }
+
+    return messages.get(prediction, f"Triage Result: {prediction}")
+
+# ------------------------------
+# 🎨 Gradio Interface
+# ------------------------------
+interface = gr.Interface(
+    fn=triage_chatbot,
+    inputs=gr.Textbox(
+        label="Describe your child's symptoms",
+        placeholder="e.g. My child is wheezing after running outside...",
+        lines=4
+    ),
+    outputs=gr.Textbox(label="Triage Recommendation"),
+    title="🩺 Pediatric Respiratory Triage Assistant",
+    description="This AI assistant offers non-diagnostic guidance based on described respiratory symptoms. Always consult a doctor for professional care.",
+    allow_flagging="never"
+)
+
+# ------------------------------
+# 🚀 Launch App
+# ------------------------------
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()