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app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import pandas as pd
+import tempfile
+import os
+import time
+from keras.models import model_from_json
+from keras_preprocessing.image import img_to_array
+import plotly.express as px
+
+
+# Load Emotion Detection Model
+@st.cache_resource
+def load_model():
+    model_path = "emotion_model.json"
+    weights_path = "emotion_model.h5"
+    if not os.path.exists(model_path) or not os.path.exists(weights_path):
+        st.error("Model files not found.")
+        return None
+    with open(model_path, "r") as json_file:
+        loaded_model_json = json_file.read()
+    emotion_model = model_from_json(loaded_model_json)
+    emotion_model.load_weights(weights_path)
+    return emotion_model
+
+
+# Load Haar Cascade for Face Detection
+@st.cache_resource
+def load_face_detector():
+    cascade_path = "haarcascade_frontalface_default.xml"
+    if not os.path.exists(cascade_path):
+        st.error("Haar Cascade file not found.")
+        return None
+    return cv2.CascadeClassifier(cascade_path)
+
+
+# Convert to H.264 for Better Browser Playback
+def convert_to_h264(input_path, output_path):
+    cap = cv2.VideoCapture(input_path)
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+
+    fourcc = cv2.VideoWriter_fourcc(*'avc1')  # H.264 codec
+
+    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        out.write(frame)
+
+    cap.release()
+    out.release()
+    time.sleep(2)  # Delay to prevent file locking
+
+
+# Process Video and Collect Emotion Data
+def process_video(video_path, output_path, skip_frames=5):
+    cap = cv2.VideoCapture(video_path)
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+
+    fourcc = cv2.VideoWriter_fourcc(*'avc1')  # Use H.264 for better support
+    temp_output_path = os.path.join(tempfile.gettempdir(), "temp_video.mp4")
+    out = cv2.VideoWriter(temp_output_path, fourcc, fps, (frame_width, frame_height))
+
+    emotion_model = load_model()
+    face_detector = load_face_detector()
+
+    emotion_labels = {0: "Angry", 1: "Disgust", 2: "Fear",
+                      3: "Happy", 4: "Sad", 5: "Surprise", 6: "Neutral"}
+
+    frame_number = 0
+    emotions_over_time = []
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        if frame_number % skip_frames != 0:
+            frame_number += 1
+            continue
+
+        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:
+            roi_gray = gray_frame[y:y + h, x:x + w]
+            roi_gray = cv2.resize(roi_gray, (48, 48))
+            roi = roi_gray.astype("float") / 255.0
+            roi = img_to_array(roi)
+            roi = np.expand_dims(roi, axis=0)
+
+            preds = emotion_model.predict(roi, verbose=0)[0]
+            label = emotion_labels[np.argmax(preds)]
+
+            emotions_over_time.append(label)
+
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
+            cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,
+                        0.9, (36, 255, 12), 2)
+
+        out.write(frame)
+        frame_number += 1
+
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+
+    convert_to_h264(temp_output_path, output_path)
+
+    try:
+        os.remove(temp_output_path)
+    except PermissionError:
+        st.warning(f"Could not delete temporary file: {temp_output_path}. Please remove it manually.")
+
+    return pd.DataFrame({"Emotion": emotions_over_time})
+
+# Generate Scatter Plot for Timeline
+def generate_emotion_scatterplot(data):
+    data["Frame"] = data.index  # Add frame numbers
+    fig = px.scatter(data, x="Frame", y="Emotion", title="Emotion Timeline Across Frames",
+                     labels={"Frame": "Frame Number", "Emotion": "Detected Emotion"},
+                     color="Emotion", opacity=0.7)
+    return fig
+
+# Generate Pie Chart
+def generate_emotion_distribution(data):
+    emotion_counts = data["Emotion"].value_counts().reset_index()
+    emotion_counts.columns = ["Emotion", "Count"]
+    fig = px.pie(emotion_counts, names="Emotion", values="Count", title="Emotion Distribution")
+    return fig
+
+
+# Streamlit App
+st.title("🎭 EmoNet: We Empower Machines With Emotion ")
+
+uploaded_file = st.file_uploader("📤 Upload a Video for Emotion Detection", type=["mp4", "avi", "mov"])
+
+if uploaded_file is not None:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tfile:
+        tfile.write(uploaded_file.read())
+        video_path = tfile.name
+
+    output_video_path = os.path.join(tempfile.gettempdir(), "analyzed_video.mp4")
+
+    loading_gif_path = "loading.gif"  # Replace with your GIF file path
+    if os.path.exists(loading_gif_path):
+        st.image(loading_gif_path, caption="⏳ Processing your video... Please wait.", use_column_width=True)
+
+    emotion_data = process_video(video_path, output_video_path, skip_frames=2)
+
+    if os.path.exists(output_video_path) and os.path.getsize(output_video_path) > 0:
+        st.success("✅ Video processing complete! View your results below:")
+
+        tab1, tab2, tab3 = st.tabs(["🎬 Processed Video", "📊 Emotion Distribution", "📉 Emotion Timeline"])
+
+        with tab1:
+            st.subheader("Analyzed Video")
+            st.video(output_video_path)
+
+        with tab2:
+            st.subheader("Emotion Distribution")
+            st.plotly_chart(generate_emotion_distribution(emotion_data), use_container_width=True)
+
+        with tab3:
+            st.subheader("Emotion Timeline")
+            st.plotly_chart(generate_emotion_scatterplot(emotion_data), use_container_width=True)
+
+    else:
+        st.error("⚠️ Error: Processed video file is missing or empty.")
+
+    os.remove(video_path)

emotion_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3d42bef256364d206ddb84d4e7b09844b7a1d5b6a2a75d521da658c810c7570c
+size 9413600

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, "dtype": "float32", "batch_input_shape": [null, 48, 48, 1], "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": "2.12.0", "backend": "tensorflow"}