Update app.py

app.py

@@ -1,15 +1,12 @@
 import streamlit as st
 import os
 from PIL import Image
+from transformers import pipeline  # Hugging Face pipeline for object detection
+import time  # To simulate a delay (for model processing)
 
 # Define the path to the folder where images are stored
 FIRE_SHOT_FOLDER = os.path.join(os.getcwd(), 'FireShot')
 
-# Ensure the folder exists
-if not os.path.exists(FIRE_SHOT_FOLDER):
-    st.error("FireShot folder does not exist!")
-else:
-    st.success(f"FireShot folder found at {FIRE_SHOT_FOLDER}")
 
 # Get list of images from the FireShot folder
 image_files = [f for f in os.listdir(FIRE_SHOT_FOLDER) if f.endswith(('jpg', 'jpeg', 'png'))]
@@ -24,5 +21,27 @@ if image_files:
         img = Image.open(img_path)
         st.image(img, caption=f"Selected Image: {selected_image}", use_column_width=True)
 
+        # Add a button to start the object detection
+        if st.button("Run Tree Detection"):
+            # Show loading spinner while processing the model
+            with st.spinner("Running YOLO model to detect trees..."):
+                # Simulate loading time (you can remove this when using an actual model)
+                time.sleep(3)  # Simulate delay (this line can be removed)
+                
+                # Load YOLO model from Hugging Face
+                model = pipeline('object-detection', model="blah-blah-treecounter")
+
+                # Perform object detection on the selected image
+                results = model(img_path)
+
+                # Simulate counting trees based on the detected objects
+                tree_count = sum(1 for obj in results if obj['label'].lower() == 'tree')
+
+            # Display the results in a button to be clicked
+            if st.button("Show Results"):
+                st.write(f"### Detected Trees: {tree_count}")
+                st.json(results)  # Display full detection results in JSON format if needed
+
 else:
     st.info("No images found in FireShot folder.")
+