Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 from transformers import AutoProcessor, AutoModelForImageTextToText
 from PIL import Image
 import torch
+import time  # To simulate progress bar updates
 
 # Load model and processor
 @st.cache_resource
@@ -12,16 +13,30 @@ def load_model():
 
 # Extract text from image
 def extract_text(image, processor, model):
+    # Initialize progress bar
+    progress_bar = st.progress(0)
+    
+    # Simulate steps for progress
+    progress_bar.progress(20)  # Step 1: Starting processing
+    time.sleep(0.5)
+
     # Preprocess image (extract pixel values only)
     inputs = processor(images=image, return_tensors="pt").to("cpu")
-    pixel_values = inputs.get("pixel_values")  # Only pass necessary inputs
+    pixel_values = inputs.get("pixel_values")
+    progress_bar.progress(50)  # Step 2: Preprocessed image
+    time.sleep(0.5)
 
     # Perform generation
     with torch.no_grad():
-        outputs = model.generate(pixel_values=pixel_values)  # Use pixel_values here
+        outputs = model.generate(pixel_values=pixel_values)
+        progress_bar.progress(80)  # Step 3: Model processing
+        time.sleep(0.5)
 
     # Decode outputs
     result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+    progress_bar.progress(100)  # Step 4: Completed
+    time.sleep(0.5)
+
     return result
 
 # Streamlit UI
@@ -38,10 +53,10 @@ def main():
     if uploaded_file is not None:
         # Open image
         image = Image.open(uploaded_file).convert("RGB")
-        st.image(image, caption="Uploaded Image", use_column_width=True)
+        st.image(image, caption="Uploaded Image", use_container_width=True)
 
-        # Extract text
-        with st.spinner("Extracting text..."):
+        # Extract text with progress bar
+        with st.spinner("Extracting text... Please wait!"):
             try:
                 extracted_text = extract_text(image, processor, model)
                 st.subheader("📝 Extracted Text:")