Update app.py

app.py

@@ -1,8 +1,10 @@
 import streamlit as st
-import os
 from PIL import Image
+import os
+import torch
+from transformers import pipeline  # Assuming you use a Hugging Face pipeline
 
-# Define the path to the folder
+# Define the path to the folder where images are stored
 FIRE_SHOT_FOLDER = os.path.join(os.getcwd(), 'FireShot')
 
 # Ensure the folder exists
@@ -11,28 +13,46 @@ if not os.path.exists(FIRE_SHOT_FOLDER):
 else:
     st.success(f"FireShot folder found at {FIRE_SHOT_FOLDER}")
 
-# Display a file uploader
-uploaded_file = st.file_uploader("Browse images from FireShot", type=["jpg", "png", "jpeg"])
+# Display a list of image files in the folder
+image_files = [f for f in os.listdir(FIRE_SHOT_FOLDER) if f.endswith(('jpg', 'jpeg', 'png'))]
 
-# If the user uploads a file, save it in the FireShot folder
-if uploaded_file is not None:
-    img = Image.open(uploaded_file)
-    st.image(img, caption="Uploaded Image", use_column_width=True)
-    
-    # Save the image to FireShot folder
-    file_path = os.path.join(FIRE_SHOT_FOLDER, uploaded_file.name)
-    img.save(file_path)
-    st.success(f"Image saved to {file_path}")
-
-# Optionally, display all images from the FireShot folder
-if st.button("Show all images in FireShot folder"):
-    image_files = [f for f in os.listdir(FIRE_SHOT_FOLDER) if f.endswith(('jpg', 'jpeg', 'png'))]
+# Make sure there are images in the folder
+if not image_files:
+    st.info("No images found in FireShot folder.")
+else:
+    st.write("## Select an image to perform object detection:")
+
+    # Display images as clickable thumbnails
+    selected_image = None
+    cols = st.columns(4)  # Adjust the number of columns based on your layout preference
+
+    for i, image_file in enumerate(image_files):
+        img_path = os.path.join(FIRE_SHOT_FOLDER, image_file)
+        img = Image.open(img_path)
+
+        # Display thumbnail of the image
+        with cols[i % 4]:  # Display images in 4 columns
+            if st.button(image_file):
+                selected_image = img_path  # Set the selected image
     
-    if image_files:
-        for image_file in image_files:
-            img_path = os.path.join(FIRE_SHOT_FOLDER, image_file)
-            img = Image.open(img_path)
-            st.image(img, caption=image_file, use_column_width=True)
-    else:
-        st.info("No images found in FireShot folder.")
+    # Once an image is selected, run the object detection model on it
+    if selected_image:
+        st.write(f"### Running object detection on {os.path.basename(selected_image)}")
+        
+        img = Image.open(selected_image)
+        st.image(img, caption="Selected Image", use_column_width=True)
+        
+        # Load your object detection model from Hugging Face (replace with your model)
+        model = pipeline('object-detection', model='your-huggingface-model')  # Load your Hugging Face model
+        
+        # Convert the image to the format needed by the model (PIL Image)
+        results = model(selected_image)  # Pass image file to model
+        
+        # Display results (adjust this depending on the format of model's output)
+        st.write(f"### Detected Objects: {results}")
+        
+        # Assuming you're counting trees based on detection
+        tree_count = sum(1 for obj in results if obj['label'] == 'tree')
+        st.success(f"### Number of Trees: {tree_count}")
+