Update app.py

app.py

@@ -1,145 +1,81 @@
 import gradio as gr
 from ultralytics import YOLO
 from PIL import Image
-import tensorflow as tf
-import numpy as np
-
-# Global variables
-loaded_model = None
-selected_model = None
-
-# Load model on demand
-def load_model(choice):
-    global loaded_model, selected_model
-    selected_model = choice
-
-    try:
-        if choice == "YOLO":
-            loaded_model = YOLO("Suspicious_Activities_nano.pt")
-            return "✅ YOLO model loaded successfully!"
-
-        elif choice == "SlowFast":
-            loaded_model = tf.keras.models.load_model("slowfast_finalmodel.h5")
-            return "✅ SlowFast model loaded successfully!"
-
-        else:
-            return "⚠️ Please select a valid model."
-    except Exception as e:
-        return f"❌ Error loading model: {str(e)}"
-
-
-# Prediction function
-def predict_suspicious_activity(image):
-    global loaded_model, selected_model
-
-    if loaded_model is None:
-        return Image.new("RGB", (400, 200), (30, 30, 30))  # placeholder
-
-    if selected_model == "YOLO":
-        results = loaded_model.predict(source=image, show=False, conf=0.6)
-        results_img = results[0].plot()
-        return Image.fromarray(results_img)
-
-    elif selected_model == "SlowFast":
-        img = image.resize((224, 224))
-        arr = np.expand_dims(np.array(img) / 255.0, axis=0)
-        preds = loaded_model.predict(arr)
-        class_id = int(np.argmax(preds))
-        # For now, just return the same image (you can overlay text later)
-        return image
-
-    return None
-
-
-# 🌟 Attractive CSS
-custom_css = """
-#main-card {
-    background: linear-gradient(135deg, rgba(20,20,20,0.8), rgba(60,60,60,0.6));
-    backdrop-filter: blur(15px);
-    border-radius: 22px;
-    box-shadow: 0 10px 40px rgba(0,0,0,0.5);
-    padding: 35px;
-    border: 1px solid rgba(255,255,255,0.15);
-    transition: all 0.4s ease-in-out;
-}
-#main-card:hover {
-    transform: translateY(-4px);
-    box-shadow: 0 15px 45px rgba(0,0,0,0.65);
-}
-h1 {
-    font-size: 2.8rem !important;
-    font-weight: bold;
-    background: linear-gradient(90deg, #ff4b1f, #ff9068, #1fddff);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    text-align: center;
-    margin-bottom: 15px;
-}
-.description {
-    text-align: center;
-    font-size: 1.15rem;
-    margin-bottom: 28px;
-    color: #f0f0f0;
-}
-.gr-button {
-    border-radius: 14px !important;
-    padding: 12px 24px !important;
-    font-weight: 600 !important;
-    letter-spacing: 0.5px;
-    background: linear-gradient(135deg, #ff4b1f, #1fddff) !important;
-    color: white !important;
-    border: none !important;
-    transition: all 0.3s ease-in-out;
-}
-.gr-button:hover {
-    transform: scale(1.08) rotate(-1deg);
-    box-shadow: 0 6px 20px rgba(0,0,0,0.4);
-}
-.gr-button-secondary {
-    background: linear-gradient(135deg, #555, #333) !important;
-}
-label {
-    color: #ddd !important;
-    font-weight: 600;
-}
-"""
-
-# 🌟 Build UI
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    with gr.Column(elem_id="main-card"):
-        gr.Markdown("<h1>🚨 Suspicious Activity Detection</h1>")
-        gr.Markdown(
-            "<p class='description'>Choose a model (YOLO or SlowFast), upload your image, and let AI detect activities instantly ⚡</p>"
-        )
-
-        model_choice = gr.Dropdown(
-            choices=["YOLO", "SlowFast"], label="Select Model", value=None
-        )
-        load_status = gr.Label(label="Model Load Status")
-        load_btn = gr.Button("📥 Load Model")
-
+import tempfile
+import cv2
+import os
+
+# ---------------------------
+# Load models EARLY
+# ---------------------------
+yolo_model = YOLO("Suspicious_Activities_nano.pt")
+
+# ---------------------------
+# Prediction for IMAGE
+# ---------------------------
+def predict_image(image):
+    results = yolo_model.predict(source=image, show=False, conf=0.6)
+    results_img = results[0].plot()
+    return Image.fromarray(results_img)
+
+# ---------------------------
+# Prediction for VIDEO
+# ---------------------------
+def predict_video(video):
+    temp_out = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    cap = cv2.VideoCapture(video)
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    width, height = int(cap.get(3)), int(cap.get(4))
+    out = cv2.VideoWriter(temp_out.name, fourcc, fps, (width, height))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        results = yolo_model.predict(source=frame, conf=0.6, verbose=False)
+        annotated_frame = results[0].plot()
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+    return temp_out.name
+
+# ---------------------------
+# Gradio UI
+# ---------------------------
+with gr.Blocks(css="""
+    body {background: linear-gradient(135deg, #1f1c2c, #928DAB);}
+    .gradio-container {font-family: 'Segoe UI', sans-serif;}
+    h1 {text-align: center; color: white; padding: 20px; font-size: 2.5em;}
+    .tabs {margin-top: 20px;}
+    .footer {text-align:center; color:#eee; font-size:14px; margin-top:25px;}
+    .gr-button {border-radius:12px; font-weight:bold; padding:10px 18px;}
+""") as demo:
+
+    gr.HTML("<h1>🚨 Suspicious Activity Detection</h1>")
+
+    with gr.Tab("📷 Image Detection"):
         with gr.Row():
-            input_image = gr.Image(type="pil", label="Upload Image", height=350)
-            output_image = gr.Image(type="pil", label="Detection Result", height=350)
-
+            with gr.Column(scale=1):
+                img_input = gr.Image(type="pil", label="Upload Image")
+                img_btn = gr.Button("🔍 Detect Suspicious Activity")
+            with gr.Column(scale=1):
+                img_output = gr.Image(type="pil", label="Detection Result")
+        img_btn.click(predict_image, inputs=img_input, outputs=img_output)
+
+    with gr.Tab("🎥 Video Detection"):
         with gr.Row():
-            detect_btn = gr.Button("🔍 Detect")
-            clear_btn = gr.Button("🗑️ Clear", elem_classes="gr-button-secondary")
-
-        load_btn.click(fn=load_model, inputs=model_choice, outputs=load_status)
-        detect_btn.click(fn=predict_suspicious_activity, inputs=input_image, outputs=output_image)
-        clear_btn.click(fn=lambda: (None, None), inputs=None, outputs=[input_image, output_image])
-
-        gr.Markdown(
-            """
-            ---
-            📂 **Dataset Reference:**  
-            [DCSASS Dataset on Kaggle](https://www.kaggle.com/mateohervas/dcsass-dataset)  
-            *(Used for training the SlowFast model)*
-            """
-        )
-
-
-# ✅ Hugging Face needs this
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+            with gr.Column(scale=1):
+                vid_input = gr.Video(label="Upload Video")
+                vid_btn = gr.Button("🎬 Detect in Video")
+            with gr.Column(scale=1):
+                vid_output = gr.Video(label="Processed Video")
+        vid_btn.click(predict_video, inputs=vid_input, outputs=vid_output)
+
+    gr.HTML("<div class='footer'>Made with ❤️ using YOLO + Gradio</div>")
+
+# ---------------------------
+# Launch App
+# ---------------------------
+demo.launch(share=True)