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app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import model_from_json
+import os
+from PIL import Image
+import tempfile
+
+# ✅ Ensure this is the first Streamlit command
+st.set_page_config(page_title="Emotion Detection App", layout="wide")
+
+# Constants
+TARGET_SIZE = (48, 48)
+EMOTION_DICT = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# Load Model
+@st.cache_resource
+def load_emotion_model():
+    with open('model/emotion_model.json', 'r') as json_file:
+        loaded_model_json = json_file.read()
+    emotion_model = model_from_json(loaded_model_json, custom_objects={'Sequential': tf.keras.models.Sequential})
+    emotion_model.load_weights("model/emotion_model.h5")
+    return emotion_model
+
+emotion_model = load_emotion_model()
+
+
+# Load Haar Cascade for face detection
+face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Function to process image
+def process_image(image):
+    gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_detector.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5)
+
+    for (x, y, w, h) in faces:
+        cv2.rectangle(image, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
+        roi_gray_frame = gray_img[y:y + h, x:x + w]
+        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, TARGET_SIZE), -1), 0)
+
+        emotion_prediction = emotion_model.predict(cropped_img)
+        maxindex = int(np.argmax(emotion_prediction))
+        cv2.putText(image, EMOTION_DICT[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+    return image
+
+# Streamlit UI
+st.title("😀 Emotion Detection App")
+st.write("Upload an image or video, or use live camera to detect emotions in real-time.")
+
+# Sidebar navigation
+option = st.sidebar.radio("Choose an option", ["📷 Upload Image", "🎥 Upload Video", "📡 Live Camera"])
+
+# Image Upload
+if option == "📷 Upload Image":
+    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+    if uploaded_image is not None:
+        file_bytes = np.asarray(bytearray(uploaded_image.read()), dtype=np.uint8)
+        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        if st.button("🔍 Detect Emotion"):
+            processed_img = process_image(image)
+            st.image(processed_img, caption="Processed Image", use_column_width=True)
+
+# Video Upload
+elif option == "🎥 Upload Video":
+    uploaded_video = st.file_uploader("Upload a video", type=["mp4", "avi", "mov", "mkv"])
+
+    if uploaded_video is not None:
+        tfile = tempfile.NamedTemporaryFile(delete=False)
+        tfile.write(uploaded_video.read())
+
+        cap = cv2.VideoCapture(tfile.name)
+        stframe = st.empty()
+
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            processed_frame = process_image(frame)
+            stframe.image(processed_frame, channels="BGR")
+
+        cap.release()
+
+# Live Camera
+elif option == "📡 Live Camera":
+    stframe = st.empty()
+    cap = cv2.VideoCapture(0)
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        processed_frame = process_image(frame)
+        stframe.image(processed_frame, channels="BGR")
+
+    cap.release()

emotion_detection_using_face.py

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+from flask import Flask, request, redirect, url_for, render_template, send_file, Response
+import os
+import cv2
+import numpy as np
+from tensorflow.keras.models import model_from_json
+import tensorflow as tf
+
+# Constants
+TARGET_SIZE = (48, 48)
+BATCH_SIZE = 64
+EMOTION_DICT = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'uploads'
+app.config['PROCESSED_FOLDER'] = 'processed'
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+os.makedirs(app.config['PROCESSED_FOLDER'], exist_ok=True)
+
+def load_model():
+    with open('model/emotion_model.json', 'r') as json_file:
+        loaded_model_json = json_file.read()
+    try:
+        emotion_model = model_from_json(loaded_model_json, custom_objects={'Sequential': tf.keras.models.Sequential})
+    except TypeError:
+        emotion_model = model_from_json(loaded_model_json, custom_objects={'Sequential': tf.keras.models.Sequential})
+    emotion_model.load_weights("model/emotion_model.h5")
+    print("Loaded model from disk")
+    return emotion_model
+
+emotion_model = load_model()
+
+def process_image(file_path, output_path):
+    img = cv2.imread(file_path)
+    if img is None:
+        print(f"Error loading image {file_path}")
+        return
+
+    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    face_detector = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
+    faces = face_detector.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5)
+    
+    for (x, y, w, h) in faces:
+        cv2.rectangle(img, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
+        roi_gray_frame = gray_img[y:y + h, x:x + w]
+        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, TARGET_SIZE), -1), 0)
+        
+        emotion_prediction = emotion_model.predict(cropped_img)
+        maxindex = int(np.argmax(emotion_prediction))
+        cv2.putText(img, EMOTION_DICT[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+    
+    cv2.imwrite(output_path, img)
+
+def process_video(file_path, output_path):
+    cap = cv2.VideoCapture(file_path)
+    face_detector = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
+    fourcc = cv2.VideoWriter_fourcc(*'XVID')
+    out = cv2.VideoWriter(output_path, fourcc, 20.0, (int(cap.get(3)), int(cap.get(4))))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+        for (x, y, w, h) in num_faces:
+            cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
+            roi_gray_frame = gray_frame[y:y + h, x:x + w]
+            cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, TARGET_SIZE), -1), 0)
+
+            emotion_prediction = emotion_model.predict(cropped_img)
+            maxindex = int(np.argmax(emotion_prediction))
+            cv2.putText(frame, EMOTION_DICT[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+        out.write(frame)
+
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+
+def gen_frames():
+    cap = cv2.VideoCapture(0)
+    face_detector = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
+    
+    while True:
+        success, frame = cap.read()
+        if not success:
+            break
+        else:
+            gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+            for (x, y, w, h) in faces:
+                cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
+                roi_gray_frame = gray_frame[y:y + h, x:x + w]
+                cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, TARGET_SIZE), -1), 0)
+
+                emotion_prediction = emotion_model.predict(cropped_img)
+                maxindex = int(np.argmax(emotion_prediction))
+                cv2.putText(frame, EMOTION_DICT[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+            ret, buffer = cv2.imencode('.jpg', frame)
+            frame = buffer.tobytes()
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
+
+    cap.release()
+
+@app.route('/')
+def index():
+    return render_template('camera.html')
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'file' not in request.files:
+        return redirect(request.url)
+    file = request.files['file']
+    if file.filename == '':
+        return redirect(request.url)
+    if file:
+        file_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
+        file.save(file_path)
+        output_path = os.path.join(app.config['PROCESSED_FOLDER'], file.filename)
+        
+        if file.filename.lower().endswith(('png', 'jpg', 'jpeg')):
+            process_image(file_path, output_path)
+        elif file.filename.lower().endswith(('mp4', 'avi', 'mov', 'mkv')):
+            process_video(file_path, output_path)
+        else:
+            return 'Unsupported file type'
+        
+        return redirect(url_for('download_file', filename=file.filename))
+    return 'File upload failed'
+
+@app.route('/download/<filename>')
+def download_file(filename):
+    return send_file(os.path.join(app.config['PROCESSED_FOLDER'], filename), as_attachment=True)
+
+@app.route('/video_feed')
+def video_feed():
+    return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
+
+@app.route('/camera')
+def camera():
+    return render_template('camera.html')
+
+if __name__ == "__main__":
+    app.run(debug=True)

model/emotion_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b3f0b5e2305190d32260bf2cad3907d4f305bcff0600096a6cbc814476dfb697
+size 9412808

model/emotion_model.json

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+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1024, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.4.0", "backend": "tensorflow"}