Update app.py

app.py

@@ -1,28 +1,34 @@
 import streamlit as st
-import torch
-import cv2
-import numpy as np
+import kagglehub
+import tensorflow as tf
 from PIL import Image
+import numpy as np
+
+# Download model from Kaggle Hub
+model_path = kagglehub.model_download("sohiebalwedyan/seatbelt-detection/tensorFlow2/seatbelt-detection")
+print("Path to model files:", model_path)
 
-# Load YOLOv5 model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+# Load the TensorFlow model
+model = tf.saved_model.load(model_path)
 
 def predict(image):
-    # Convert PIL Image to numpy array
-    img_array = np.array(image)
+    # Preprocess image
+    img = image.resize((224, 224))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
     
-    # Detect objects
-    results = model(img_array)
+    # Run inference
+    predictions = model(tf.convert_to_tensor(img_array, dtype=tf.float32))
     
-    # Plot results
-    img_with_boxes = results.render()[0]
+    # Process predictions (modify based on actual model output)
+    # This is a placeholder and may need adjustment based on model specifics
+    st.write("Raw predictions:", predictions)
     
-    # Convert back to PIL Image
-    return Image.fromarray(img_with_boxes)
+    return image  # Placeholder return
 
 # Streamlit UI
-st.title("Object Detection with YOLOv5")
-st.markdown("Upload an image for detection.")
+st.title("Seatbelt Detection")
+st.markdown("Upload an image for seatbelt detection.")
 
 uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
@@ -31,5 +37,5 @@ if uploaded_image is not None:
     st.image(image, caption="Uploaded Image", use_column_width=True)
     
     st.subheader("Prediction Results:")
-    result_image = predict(image)
-    st.image(result_image, caption="Detected Image", use_column_width=True)
+    result = predict(image)
+    # Note: You may need to modify visualization based on model output