first commit

app.py

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+import gradio as gr
+import torch
+from transformers import AutoImageProcessor, TimesformerForVideoClassification
+import cv2
+from PIL import Image
+import numpy as np
+import time
+from collections import deque
+
+# --- Configuration ---
+# Your Hugging Face model repository ID
+HF_MODEL_REPO_ID = "owinymarvin/timesformer-crime-detection"
+
+# These must match the values used during your training
+NUM_FRAMES = 16
+TARGET_IMAGE_HEIGHT = 224
+TARGET_IMAGE_WIDTH = 224
+
+# --- Load Model and Processor ---
+print(f"Loading model and image processor from {HF_MODEL_REPO_ID}...")
+try:
+    processor = AutoImageProcessor.from_pretrained(HF_MODEL_REPO_ID)
+    model = TimesformerForVideoClassification.from_pretrained(HF_MODEL_REPO_ID)
+except Exception as e:
+    print(f"Error loading model from Hugging Face Hub: {e}")
+    # Handle error - exit or raise exception for Space to fail gracefully
+    exit()
+
+model.eval() # Set model to evaluation mode
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+print(f"Model loaded successfully on {device}.")
+print(f"Model's class labels: {model.config.id2label}")
+
+# Initialize a global buffer for frames for the session
+# Use a deque for efficient appending/popping from both ends
+frame_buffer = deque(maxlen=NUM_FRAMES)
+last_inference_time = time.time()
+inference_interval = 1.0 # Predict every 1 second (1.0 / INFERENCE_FPS)
+current_prediction_text = "Buffering frames..."
+
+def predict_video_frame(image_np_array):
+    global frame_buffer, last_inference_time, current_prediction_text
+
+    # Gradio sends frames as numpy arrays (RGB)
+    pil_image = Image.fromarray(image_np_array)
+    frame_buffer.append(pil_image)
+
+    current_time = time.time()
+
+    # Only perform inference if we have enough frames and it's time for a prediction
+    if len(frame_buffer) == NUM_FRAMES and (current_time - last_inference_time) >= inference_interval:
+        last_inference_time = current_time
+
+        # Preprocess the frames. processor expects a list of PIL Images or numpy arrays
+        processed_input = processor(images=list(frame_buffer), return_tensors="pt")
+        pixel_values = processed_input.pixel_values.to(device)
+
+        with torch.no_grad():
+            outputs = model(pixel_values)
+            logits = outputs.logits
+
+        predicted_class_id = logits.argmax(-1).item()
+        predicted_label = model.config.id2label[predicted_class_id]
+        confidence = torch.nn.functional.softmax(logits, dim=-1)[0][predicted_class_id].item()
+
+        current_prediction_text = f"Predicted: {predicted_label} ({confidence:.2f})"
+        print(current_prediction_text) # Print to Space logs
+    
+    # Return the current prediction text for display in the UI
+    return current_prediction_text
+
+# --- Gradio Interface ---
+# Create a streaming input for webcam
+webcam_input = gr.Image(
+    sources=["webcam"], # Allows webcam input
+    streaming=True,      # Enables continuous streaming of frames
+    shape=(TARGET_IMAGE_WIDTH, TARGET_IMAGE_HEIGHT), # Set expected input resolution
+    label="Live Webcam Feed"
+)
+
+# Output text box for predictions
+prediction_output = gr.Textbox(label="Real-time Prediction")
+
+
+# Define the Gradio Interface
+# We use Blocks for more control over layout if needed, but Interface works too.
+# For simplicity, we'll stick to a basic Interface
+# For streaming, gr.Interface.load() is more common, but let's define from scratch.
+
+demo = gr.Interface(
+    fn=predict_video_frame,
+    inputs=webcam_input,
+    outputs=prediction_output,
+    live=True, # Enable live updates
+    allow_flagging="never", # Disable flagging on public demo
+    title="TimesFormer Crime Detection Live Demo",
+    description=f"This demo uses a finetuned TimesFormer model ({HF_MODEL_REPO_ID}) to predict crime actions from a live webcam feed. The model processes {NUM_FRAMES} frames at a time and makes a prediction every {inference_interval} seconds. Please allow webcam access.",
+    # You might want to add examples for file uploads if you also want to support video files.
+    # examples=["path/to/your/test_video.mp4"] # If you add video upload input
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+transformers
+opencv-python-headless # Use headless for server environments without display
+Pillow
+gradio