Update app.py

app.py

@@ -1,49 +1,16 @@
 import streamlit as st
-from transformers import CLIPProcessor, CLIPModel
-import torch
-from PIL import Image
-import io
 
-# Load the CLIP model and processor
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+# Title
+st.title("Image Display App")
 
-# Labels for classification
-labels = ["dog", "cat", "bird", "flower", "car", "building", "person", "food"]
+# Input field for URL
+image_url = st.text_input("Enter the URL of the image:")
 
-def classify_image(image_bytes):
-    # Convert bytes to PIL Image
-    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-    
-    # Preprocess the image
-    inputs = clip_processor(images=image, return_tensors="pt")
-    
-    # Classify the image using the CLIP model
-    with torch.no_grad():
-        logits_per_image = clip_model(**inputs)[0]
-        probs = logits_per_image.softmax(dim=-1)
-        
-    # Get the top predicted label
-    top_prob, top_label = torch.max(probs, dim=-1)
-    top_label = labels[top_label.item()]
-    
-    return top_label
-
-def main():
-    st.title("Image Classification")
-    
-    # Upload image
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-    
-    if uploaded_file is not None:
-        # Display the uploaded image
-        image = st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-        
-        # Classify the image
-        if st.button("Classify Image"):
-            with st.spinner("Classifying image..."):
-                top_label = classify_image(uploaded_file.getvalue())
-                st.success(f"Top Predicted Label: {top_label}")
-
-if __name__ == "__main__":
-    main()
+# Display image if a valid URL is provided
+if image_url:
+    try:
+        st.image(image_url, caption="Uploaded Image")
+    except Exception as e:
+        st.error(f"Error: {e}")
+else:
+    st.warning("Please enter a valid image URL.")