Create app.py

app.py

@@ -1,10 +1,9 @@
 import streamlit as st
-from PIL import Image
 import os
-import torch
-from transformers import pipeline  # Assuming you use a Hugging Face pipeline
-pip install torch torchvision torchaudio
-# Define the path to the folder where images are stored
+from PIL import Image
+
+
+# Define the path to the folder
 FIRE_SHOT_FOLDER = os.path.join(os.getcwd(), 'FireShot')
 
 # Ensure the folder exists
@@ -13,46 +12,32 @@ if not os.path.exists(FIRE_SHOT_FOLDER):
 else:
     st.success(f"FireShot folder found at {FIRE_SHOT_FOLDER}")
 
-# 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'))]
+# Display a file uploader
+uploaded_file = st.file_uploader("Browse images from FireShot", type=["jpg", "png", "jpeg"])
 
-# 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:")
+# If the user uploads a file, save it in the FireShot folder
+if uploaded_file is not None:
 
-    # Display images as clickable thumbnails
-    selected_image = None
-    cols = st.columns(4)  # Adjust the number of columns based on your layout preference
+    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}")
 
-    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
-    
-    # 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}")
 
 
+
+# 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'))]
+    
+    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.")