Update app.py

app.py

@@ -1,31 +1,29 @@
-# 🖼️ BLIP Image Captioning - Fast, Accurate, CPU-Friendly
 import torch
 from transformers import BlipProcessor, BlipForConditionalGeneration
 from PIL import Image
 import gradio as gr
 
 # Load model and processor
-device = "cpu"
+device = torch.device("cpu")
 processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
 
-# Captioning function
+# Captioning function with fallback logic
 def caption_image(image):
     try:
         inputs = processor(images=image, return_tensors="pt").to(device)
-        out = model.generate(**inputs, max_length=30)
-        caption = processor.tokenizer.decode(out[0], skip_special_tokens=True)
+        output = model.generate(**inputs, max_length=30)
+        caption = processor.tokenizer.decode(output[0], skip_special_tokens=True)
         return caption.capitalize()
-    except Exception:
-        return "Could not generate caption. Try a different image."
+    except Exception as e:
+        return f"⚠️ Error: {str(e)[:100]}"
 
-# Launch Gradio interface
+# Gradio UI
 gr.Interface(
     fn=caption_image,
     inputs=gr.Image(type="pil", label="Upload Image"),
     outputs=gr.Textbox(label="Generated Caption"),
-    title="🖼️ Image Caption Generator (BLIP)",
-    description="Accurate, fast image captioning using BLIP. No API keys. CPU-friendly. Instant output.",
-    examples=["example.jpg"],  # Optional: preload sample image
-    cache_examples=True        # Optional: speeds up UX
+    title="🖼️ BLIP Image Caption Generator",
+    description="Fast, accurate image captioning using BLIP. No API keys. CPU-friendly. Instant output.",
+    allow_flagging="never"
 ).launch()