Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+from PIL import Image
+import io
+
+# Load a pre-trained TensorFlow model (replace with your model path)
+model = tf.keras.applications.MobileNetV2(weights="imagenet")
+
+def preprocess_image(image):
+    img = np.array(image)
+    img = cv2.resize(img, (224, 224))
+    img = tf.keras.applications.mobilenet_v2.preprocess_input(img)
+    return np.expand_dims(img, axis=0)
+
+def classify_frame(frame):
+    processed_frame = preprocess_image(frame)
+    predictions = model.predict(processed_frame)
+    decoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions, top=1)[0]
+    return decoded_predictions[0][1]
+
+def process_video(video):
+    result = ""
+    cap = cv2.VideoCapture(video)
+    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    frame_interval = frame_count // 10  # Analyze 10 frames evenly spaced throughout the video
+
+    for i in range(0, frame_count, frame_interval):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, i)
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        image = Image.fromarray(frame_rgb)
+        label = classify_frame(image)
+        
+        if "baseball" in label.lower():
+            result = "The runner is out"
+            break
+
+    cap.release()
+    if result == "":
+        result = "The runner is safe"
+    
+    return result
+
+iface = gr.Interface(
+    fn=process_video,
+    inputs=gr.inputs.Video(type="mp4"),
+    outputs="text",
+    title="Baseball Runner Status",
+    description="Upload a baseball video to determine if the runner is out or safe."
+)
+
+if __name__ == "__main__":
+    iface.launch()