main

app.py

@@ -1,4 +1,3 @@
-
 import streamlit as st
 import tensorflow as tf
 from tensorflow.keras.models import load_model
@@ -6,7 +5,6 @@ from PIL import Image
 import numpy as np
 import requests
 import os
-from tqdm import tqdm
 
 # Set page config
 st.set_page_config(
@@ -37,33 +35,33 @@ st.markdown(
 )
 
 # --- Model Downloading and Loading ---
-def download_file_from_google_drive(id, destination):
+def download_file_from_google_drive(id, destination, progress_bar):
     URL = f'https://drive.google.com/uc?export=download&id={id}'
     session = requests.Session()
+
     response = session.get(URL, stream=True)
-    
     token = None
     for key, value in response.cookies.items():
         if key.startswith('download_warning'):
             token = value
-            break
-    
+
     if token:
         params = {'id': id, 'confirm': token}
         response = session.get(URL, params=params, stream=True)
-    
+
     total_size = int(response.headers.get('content-length', 0))
     block_size = 1024  # 1 Kibibyte
-    
-    progress_bar = tqdm(total=total_size, unit='iB', unit_scale=True)
+    current_size = 0
+
     with open(destination, 'wb') as f:
         for data in response.iter_content(block_size):
-            progress_bar.update(len(data))
+            current_size += len(data)
             f.write(data)
-    progress_bar.close()
-    
-    if total_size != 0 and progress_bar.n != total_size:
-        st.error("An error occurred during file download.")
+            # Update Streamlit progress bar
+            progress_percentage = min(int((current_size / total_size) * 100), 100)
+            progress_bar.progress(progress_percentage, text=f"Downloading... {current_size // (1024*1024)}MB / {total_size // (1024*1024)}MB")
+
+    if total_size != 0 and current_size != total_size:
         return False
     return True
 
@@ -74,19 +72,26 @@ def load_keras_model():
     The `st.cache_resource` decorator ensures the model is loaded only once.
     """
     MODEL_PATH = "my_model.keras"
-    FILE_ID = "1M-HNEJqbz6PzjhX6WHHKLPbjZpPRWLjP" # Replace with your file ID
+    FILE_ID = "1M-HNEJqbz6PzjhX6WHHKLPbjZpPRWLjP"
 
     if not os.path.exists(MODEL_PATH):
         st.info("Model not found locally. Downloading from Google Drive... (this may take a moment)")
-        download_file_from_google_drive(FILE_ID, MODEL_PATH)
-        st.success("Model downloaded successfully!")
+        progress_bar = st.progress(0, text="Starting download...")
+        
+        download_successful = download_file_from_google_drive(FILE_ID, MODEL_PATH, progress_bar)
+        
+        progress_bar.empty() # Clear the progress bar after completion
+
+        if not download_successful:
+            st.error("Failed to download the model. Please check the file ID and permissions on Google Drive.")
+            st.stop()
 
     try:
         model = load_model(MODEL_PATH)
         return model
     except Exception as e:
         st.error(f"Error loading model: {e}")
-        st.info("Please ensure the Google Drive File ID is correct and the file is accessible.")
+        st.info("The downloaded file might be corrupted. Try deleting 'my_model.keras' and restarting the app.")
         return None
 
 model = load_keras_model()
@@ -95,15 +100,11 @@ model = load_keras_model()
 def preprocess_image(image):
     """
     Preprocesses the uploaded image to fit the model's input requirements.
-    - Resizes to (256, 256)
-    - Converts to a NumPy array
-    - Normalizes pixel values
-    - Expands dimensions for the model
     """
     img = image.resize((256, 256))
     img_array = np.array(img)
     img_array = img_array / 255.0
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = np.expand_dims(img_array, axis=0)
     return img_array
 
 # --- UI Layout ---
@@ -120,33 +121,31 @@ with col1:
         "Choose an image...", type=["jpg", "jpeg", "png"]
     )
 
-if uploaded_file is not None and model is not None:
-    # Display the uploaded image
+if uploaded_file is not None:
     image = Image.open(uploaded_file)
-    
     with col1:
         st.image(image, caption="Uploaded Image", use_column_width=True)
-
-    # Preprocess the image and make a prediction
-    processed_image = preprocess_image(image)
-    prediction = model.predict(processed_image)
-    confidence = prediction[0][0]
-
-    with col2:
-        st.header("Prediction")
-        if confidence > 0.5:
-            st.markdown(
-                f"## This is a Dog! 🐶"
-            )
-            st.progress(confidence)
-            st.write(f"**Confidence:** {confidence:.2f}")
-        else:
-            st.markdown(
-                f"## This is a Cat! 🐱"
-            )
-            st.progress(1-confidence)
-            st.write(f"**Confidence:** {1-confidence:.2f}")
+    
+    if model is not None:
+        # Preprocess the image and make a prediction
+        processed_image = preprocess_image(image)
+        prediction = model.predict(processed_image)
+        confidence = prediction[0][0]
+
+        with col2:
+            st.header("Prediction")
+            if confidence > 0.5:
+                st.markdown(f"## This is a Dog! 🐶")
+                st.progress(float(confidence))
+                st.write(f"**Confidence:** {confidence:.2f}")
+            else:
+                st.markdown(f"## This is a Cat! 🐱")
+                st.progress(float(1-confidence))
+                st.write(f"**Confidence:** {1-confidence:.2f}")
+    else:
+        with col2:
+            st.error("Model could not be loaded. Cannot make a prediction.")
 else:
-    with col2:
-        st.info("Please upload an image to see the prediction.")
-
+    if model is not None:
+        with col2:
+            st.info("Please upload an image to see the prediction.")

requirements.txt

@@ -3,4 +3,3 @@ tensorflow
 numpy
 Pillow
 requests
-tqdm