Update app.py

app.py

@@ -1,80 +1,80 @@
-from flask import Flask, request, jsonify
-import cv2
-import numpy as np
-import tensorflow as tf
-
-# Load the model
-MODEL_PATH = "engagement_model_89.tflite"
-interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
-interpreter.allocate_tensors()
-input_details = interpreter.get_input_details()
-output_details = interpreter.get_output_details()
-
-def predict_tflite(input_data):
-    interpreter.set_tensor(input_details[0]['index'], input_data)
-    interpreter.invoke()
-    return interpreter.get_tensor(output_details[0]['index'])
-
-# Labels
-labels = ["Engaged", "Frustrated", "Bored", "Confused"]
-
-# Haar Cascade for face detection
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
-
-# Initialize Flask app
-app = Flask(__name__)
-
-@app.route('/')
-def home():
-    return jsonify({
-        'message': 'Engagement Detection API',
-        'endpoints': {
-            '/predict': 'POST image for emotion prediction'
-        }
-    })
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    file = request.files['image']
-    img = np.frombuffer(file.read(), np.uint8)
-    frame = cv2.imdecode(img, cv2.IMREAD_COLOR)
-
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
-
-    predictions = {"Engaged": 0, "Frustrated": 0, "Bored": 0, "Confused": 0}
-
-    if len(faces) > 0:
-        for (x, y, w, h) in faces:
-            face = frame[y:y+h, x:x+w]
-            img_resized = cv2.resize(face, (224, 224))
-            img_resized = img_resized.astype("float32") / 255.0
-            img_resized = np.expand_dims(img_resized, axis=0)
-
-            # Get model predictions
-            preds = predict_tflite(img_resized)[0]
-            
-            # Normalize to sum to 1 (100%)
-            preds = preds / np.sum(preds)
-            
-            # Get dominant emotion
-            dominant_idx = np.argmax(preds)
-            dominant_emotion = labels[dominant_idx]
-            
-            # Debug output
-            print(f"Model predictions: {dict(zip(labels, preds))}")
-            print(f"Dominant emotion: {dominant_emotion}")
-            
-            # Store results
-            for i, label in enumerate(labels):
-                predictions[label] = float(preds[i])
-            result = dominant_emotion
-
-    if len(faces) == 0:
-        return jsonify({"error": "No face detected", "predictions": None, "result": None})
-    return jsonify({"predictions": predictions, "result": dominant_emotion})
-
-if __name__ == "__main__":
-    import os
-    port = int(os.environ.get("PORT", 5000))  # Default to 5000 if not set
-    app.run(host='0.0.0.0', port=port, debug=True)
+from flask import Flask, request, jsonify
+import cv2
+import numpy as np
+import tensorflow as tf
+
+# Load the model
+MODEL_PATH = "engagement_model_89.tflite"
+interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+def predict_tflite(input_data):
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+    return interpreter.get_tensor(output_details[0]['index'])
+
+# Labels
+labels = ["Engaged", "Frustrated", "Bored", "Confused"]
+
+# Haar Cascade for face detection
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+# Initialize Flask app
+app = Flask(__name__)
+
+@app.route('/')
+def home():
+    return jsonify({
+        'message': 'Engagement Detection API',
+        'endpoints': {
+            '/predict': 'POST image for emotion prediction'
+        }
+    })
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    file = request.files['image']
+    img = np.frombuffer(file.read(), np.uint8)
+    frame = cv2.imdecode(img, cv2.IMREAD_COLOR)
+
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+
+    predictions = {"Engaged": 0, "Frustrated": 0, "Bored": 0, "Confused": 0}
+
+    if len(faces) > 0:
+        for (x, y, w, h) in faces:
+            face = frame[y:y+h, x:x+w]
+            img_resized = cv2.resize(face, (224, 224))
+            img_resized = img_resized.astype("float32") / 255.0
+            img_resized = np.expand_dims(img_resized, axis=0)
+
+            # Get model predictions
+            preds = predict_tflite(img_resized)[0]
+            
+            # Normalize to sum to 1 (100%)
+            preds = preds / np.sum(preds)
+            
+            # Get dominant emotion
+            dominant_idx = np.argmax(preds)
+            dominant_emotion = labels[dominant_idx]
+            
+            # Debug output
+            print(f"Model predictions: {dict(zip(labels, preds))}")
+            print(f"Dominant emotion: {dominant_emotion}")
+            
+            # Store results
+            for i, label in enumerate(labels):
+                predictions[label] = float(preds[i])
+            result = dominant_emotion
+
+    if len(faces) == 0:
+        return jsonify({"error": "No face detected", "predictions": None, "result": None})
+    return jsonify({"predictions": predictions, "result": dominant_emotion})
+
+if __name__ == "__main__":
+    import os
+    port = int(os.environ.get("PORT", 7860))  # Default to 5000 if not set
+    app.run(host='0.0.0.0', port=port, debug=True)