Update app.py

app.py

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+import gradio as gr
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+import pandas as pd
+import cv2
+
+# Define constants
+IMG_SIZE = 224
+MAX_SEQ_LENGTH = 30
+NUM_FEATURES = 2048
+
+# Load the trained model
+model_filepath = "lstm_model.h5"  # Replace with the actual path
+loaded_model = keras.models.load_model(model_filepath)
+train_df = pd.DataFrame({
+    'tag': ['BabyCrawling', 'CricketShot']
+})
+label_processor = keras.layers.StringLookup(num_oov_indices=0, vocabulary=np.unique(train_df["tag"]))
+def crop_center_square(frame):
+    y, x = frame.shape[0:2]
+    min_dim = min(y, x)
+    start_x = (x // 2) - (min_dim // 2)
+    start_y = (y // 2) - (min_dim // 2)
+    return frame[start_y : start_y + min_dim, start_x : start_x + min_dim]
+    
+def load_video(path, max_frames=0, resize=(IMG_SIZE, IMG_SIZE)):
+    cap = cv2.VideoCapture(path)
+    frames = []
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frame = crop_center_square(frame)
+            frame = cv2.resize(frame, resize)
+            frame = frame[:, :, [2, 1, 0]]
+            frames.append(frame)
+
+            if len(frames) == max_frames:
+                break
+    finally:
+        cap.release()
+    return np.array(frames)
+# Load the feature extractor
+def build_feature_extractor():
+    feature_extractor = keras.applications.InceptionV3(
+        weights="imagenet",
+        include_top=False,
+        pooling="avg",
+        input_shape=(IMG_SIZE, IMG_SIZE, 3),
+    )
+    preprocess_input = keras.applications.inception_v3.preprocess_input
+
+    inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3))
+    preprocessed = preprocess_input(inputs)
+
+    outputs = feature_extractor(preprocessed)
+    return keras.Model(inputs, outputs, name="feature_extractor")
+
+feature_extractor = build_feature_extractor()
+
+# Function for preparing a single video for prediction
+def prepare_single_video(frames):
+    frames = frames[None, ...]
+    frame_mask = np.zeros(shape=(1, MAX_SEQ_LENGTH,), dtype="bool")
+    frame_features = np.zeros(shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype="float32")
+
+    for i, batch in enumerate(frames):
+        video_length = batch.shape[0]
+        length = min(MAX_SEQ_LENGTH, video_length)
+        for j in range(length):
+            frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])
+        frame_mask[i, :length] = 1  # 1 = not masked, 0 = masked
+
+    return frame_features, frame_mask
+
+# Function for making predictions
+def sequence_prediction(video_file):
+    class_vocab = label_processor.get_vocabulary()
+
+    # Load the video frames
+    frames = load_video(video_file)
+
+    # Prepare the frames for prediction
+    frame_features, frame_mask = prepare_single_video(frames)
+
+    # Make predictions using the loaded model
+    probabilities = loaded_model.predict([frame_features, frame_mask])[0]
+
+    # Get the predicted label
+    predicted_label = class_vocab[np.argmax(probabilities)]
+
+    return predicted_label
+example_list=[
+        ["video1.mp4"],
+        ["video2.mp4"],
+    ]
+# Gradio interface
+iface = gr.Interface(
+    fn=sequence_prediction,
+    inputs=gr.Video(label="Upload a video file"),
+    outputs="text",
+    examples=example_list,
+)
+
+# Launch the Gradio app
+iface.launch()