Update app.py

app.py

@@ -2,35 +2,49 @@
 MindScan — Flask Backend
 NCI H9DAI Research Project 2026
 
-Loads all 12 models (3 classical + XLM-RoBERTa per dataset)
-and serves predictions via a single /predict endpoint.
+Two modes — auto-detected at startup:
+
+  PROXY mode  (default, no local models needed)
+    Forwards /predict to the HuggingFace Space.
+    Set HF_SPACE_URL env var to override the target.
+    Run: python app.py
+
+  LOCAL mode  (models/ directory present)
+    Loads all 12 models from disk and runs inference locally.
+    Activated automatically when models/ exists.
+    Run: python app.py
 
-Run:  python app.py
 Open: http://localhost:5000
 """
 
 from flask import Flask, request, jsonify, render_template
-import os, time
-
-# Import our prediction module
-from predict import load_all_models, predict_all, models_loaded
+import os, time, requests as _requests
 
 app = Flask(__name__)
 
+HF_SPACE_URL   = os.environ.get('HF_SPACE_URL', 'https://esvanth-mindscan.hf.space')
+_LOCAL_MODELS  = os.path.join(os.path.dirname(__file__), 'models', 'classical')
+_use_local     = os.path.isdir(_LOCAL_MODELS)
+
 # ─────────────────────────────────────────────────────────────────
-# Load models once at startup — not per request
+# Startup
 # ─────────────────────────────────────────────────────────────────
 print("\n" + "="*55)
 print("  MindScan — Starting up")
 print("="*55)
-print("  Loading models... (XLM-RoBERTa takes ~30s on CPU)")
-
-start = time.time()
-load_all_models()
-elapsed = time.time() - start
 
-print(f"  ✅ All models loaded in {elapsed:.1f}s")
-print(f"  🌐 Open: http://localhost:5000")
+if _use_local:
+    print("  LOCAL mode — loading models from disk...")
+    from predict import load_all_models, predict_all, models_loaded
+    start = time.time()
+    load_all_models()
+    print(f"  ✅ Models loaded in {time.time()-start:.1f}s")
+else:
+    print("  PROXY mode — no local models found")
+    print(f"  → Forwarding requests to: {HF_SPACE_URL}")
+    print("  (Download models/ from Google Drive to switch to LOCAL mode)")
+
+print(f"  🌐 Open: http://localhost:{os.environ.get('PORT', 5001)}")
 print("="*55 + "\n")
 
 
@@ -40,54 +54,75 @@ print("="*55 + "\n")
 
 @app.route('/')
 def index():
-    """Serve the main UI."""
     return render_template('index.html')
 
 
+@app.route('/flow')
+def flow():
+    return render_template('flow_diagram.html')
+
+
 @app.route('/predict', methods=['POST'])
 def predict():
-    """
-    POST /predict
-    Body: { "text": "your text here" }
-    Returns: full prediction JSON from all 12 models
-    """
     data = request.get_json()
 
     if not data or 'text' not in data:
         return jsonify({'error': 'Missing "text" field in request body'}), 400
 
     text = data['text'].strip()
-
     if not text:
         return jsonify({'error': 'Text cannot be empty'}), 400
-
     if len(text) > 5000:
         return jsonify({'error': 'Text too long (max 5000 characters)'}), 400
 
-    if not models_loaded():
-        return jsonify({'error': 'Models not loaded yet — try again in a moment'}), 503
-
-    try:
-        t0 = time.time()
-        result = predict_all(text)
-        result['processing_time_ms'] = round((time.time() - t0) * 1000)
-        return jsonify(result)
-    except Exception as e:
-        print(f"Prediction error: {e}")
-        return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
+    if _use_local:
+        # ── Local inference ───────────────────────────────────────
+        if not models_loaded():
+            return jsonify({'error': 'Models not ready yet — try again in a moment'}), 503
+        try:
+            t0 = time.time()
+            result = predict_all(text)
+            result['processing_time_ms'] = round((time.time() - t0) * 1000)
+            return jsonify(result)
+        except Exception as e:
+            print(f"Prediction error: {e}")
+            return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
+    else:
+        # ── Proxy to HuggingFace Space ────────────────────────────
+        try:
+            r = _requests.post(
+                f'{HF_SPACE_URL}/predict',
+                json={'text': text},
+                timeout=120,
+            )
+            return r.content, r.status_code, {'Content-Type': 'application/json'}
+        except _requests.exceptions.Timeout:
+            return jsonify({'error': 'HuggingFace Space timed out — it may be waking up, try again in 30s'}), 504
+        except _requests.exceptions.ConnectionError:
+            return jsonify({'error': f'Cannot reach {HF_SPACE_URL} — check your internet connection'}), 503
 
 
 @app.route('/health')
 def health():
-    """Quick health check endpoint."""
-    return jsonify({
-        'status': 'ok',
-        'models_ready': models_loaded()
-    })
+    if _use_local:
+        from predict import models_loaded
+        return jsonify({'status': 'ok', 'mode': 'local', 'models_ready': models_loaded()})
+    else:
+        try:
+            r = _requests.get(f'{HF_SPACE_URL}/health', timeout=10)
+            data = r.json()
+            data['mode'] = 'proxy'
+            data['hf_space'] = HF_SPACE_URL
+            return jsonify(data)
+        except Exception as e:
+            return jsonify({'status': 'error', 'mode': 'proxy', 'message': str(e)}), 503
 
 
 # ─────────────────────────────────────────────────────────────────
 # START
 # ─────────────────────────────────────────────────────────────────
 if __name__ == '__main__':
-    app.run(debug=False, host='0.0.0.0', port=int(os.environ.get('PORT', 5000)))
+    import threading, webbrowser
+    port = int(os.environ.get('PORT', 5001))
+    threading.Timer(1.2, lambda: webbrowser.open(f'http://localhost:{port}')).start()
+    app.run(debug=False, host='0.0.0.0', port=port)