Update app.py

app.py

@@ -1,49 +1,28 @@
 import streamlit as st
-from PIL import Image
-import requests
-from transformers import BlipProcessor, BlipForConditionalGeneration
+from transformers import pipeline
 
-# Load the model and processor outside the main function to avoid reloading on every run
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+# Initialize the image captioning pipeline
+captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
 
-def generate_caption(img_url):
-    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+# Streamlit app title
+st.title("Image to Text Captioning")
 
-    # Conditional image captioning
-    text = "a photography of"
-    inputs = processor(raw_image, text, return_tensors="pt")
-    out = model.generate(**inputs)
-    conditional_caption = processor.decode(out[0], skip_special_tokens=True)
+# Input for image URL
+image_url = st.text_input("Enter the URL of the image:")
 
-    # Unconditional image captioning
-    inputs = processor(raw_image, return_tensors="pt")
-    out = model.generate(**inputs)
-    unconditional_caption = processor.decode(out[0], skip_special_tokens=True)
+# If an image URL is provided
+if image_url:
+    try:
+        # Display the image
+        st.image(image_url, caption="Provided Image", use_column_width=True)
+        
+        # Generate the caption
+        caption = captioner(image_url)
+        
+        # Display the caption
+        st.write("**Generated Caption:**")
+        st.write(caption[0]['generated_text'])
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
 
-    return conditional_caption, unconditional_caption
-
-def main():
-    st.title("Image Captioning App")
-    
-    img_url = st.text_input("Enter the image URL:")
-    if img_url:
-        try:
-            # Display the image
-            image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
-            st.image(image, caption='Input Image', use_column_width=True)
-            
-            # Generate captions
-            conditional_caption, unconditional_caption = generate_caption(img_url)
-            
-            # Display captions
-            st.subheader("Conditional Image Caption")
-            st.write(conditional_caption)
-            
-            st.subheader("Unconditional Image Caption")
-            st.write(unconditional_caption)
-        except Exception as e:
-            st.error(f"Error processing the image: {e}")
-
-if __name__ == "__main__":
-    main()
+# To run this app, save this code to a file (e.g., `app.py`) and run `streamlit run app.py` in your terminal.