Delete app.py

app.py

@@ -1,67 +0,0 @@
-import streamlit as st
-import cv2
-import numpy as np
-import tensorflow as tf
-import random
-import time
-
-url = st.text_area('enter streaming url')
-locations = ['Miami', 'Smouha', 'Mandara', 'Sporting', 'Montazah']
-model = tf.saved_model.load('my_model')
-
-classes_1 = ["RoadAccidents", "Fighting", "NormalVideos"]  # Define your classes here
-
-# Function to preprocess frames for action recognition
-def preprocess_frame(frame):
-    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    frame = cv2.resize(frame, (224, 224))
-    frame = frame.astype('float32') / 255.0
-    frame = np.expand_dims(frame, axis=0)
-    return frame
-
-def get_top_k(probs, k=1, label_map=classes_1):
-    """Outputs the top k model labels and probabilities on the given video."""
-    top_predictions = tf.argsort(probs, axis=-1, direction='DESCENDING')[:k]
-    top_labels = tf.gather(label_map, top_predictions, axis=-1)
-    top_labels = [label.decode('utf8') for label in top_labels.numpy()]
-    top_probs = tf.gather(probs, top_predictions, axis=-1).numpy()
-    return top_labels[0]
-
-# Function to perform action recognition on the HLS stream
-def perform_action_recognition(url, model, k=1, label_map=classes_1, locations=locations):
-    # Open the video stream
-    cap = cv2.VideoCapture(url)
-    start_time = time.time()  # Capture the start time
-
-    while True:
-        # Read the next frame
-        ret, frame = cap.read()
-
-        if not ret:
-            break
-
-        # Preprocess the frame
-        preprocessed_frame = preprocess_frame(frame)
-
-        # Perform action recognition on the preprocessed frame
-        outputs = model.signatures['serving_default'](image=preprocessed_frame[tf.newaxis])
-        probs = tf.nn.softmax(outputs['classifier_head_1'])
-        current_time = time.time() - start_time  # Calculate the elapsed time
-        m, s = divmod(current_time, 60)
-        h, m = divmod(m, 60)
-        ip_address = last_part = url.split("/")[-1]
-        output = [get_top_k(probs[0], k=k, label_map=label_map), f"{int(h):02d}:{int(m):02d}:{int(s):02d}", random.choice(locations), ip_address]
-        yield output
-
-    # Release the video stream
-    cap.release()
-
-# Call the function to perform action recognition on the HLS stream
-
-# Create a placeholder for the output
-output_placeholder = st.empty()
-
-# Call the function to perform action recognition on the HLS stream and update the placeholder
-if url:
-    for output in perform_action_recognition(url, model):
-        output_placeholder.json(output)