Create app.py

app.py

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+from PIL import Image
+import torch
+from transformers import BlipProcessor, BlipForConditionalGeneration
+import gradio as gr
+# Load the processor and model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+def generate_caption(image):
+    # Preprocess the image
+    inputs = processor(image, return_tensors="pt")
+
+    # Generate text
+    with torch.no_grad():
+        output = model.generate(**inputs)
+
+    # Decode the generated text
+    caption = processor.decode(output[0], skip_special_tokens=True)
+    return caption
+def interface(image):
+    try:
+        # Ensure image is a PIL Image
+        image = image.convert("RGB")  # Convert to RGB to ensure compatibility
+        # Generate caption
+        caption = generate_caption(image)
+        return caption  # Return only the caption
+    except Exception as e:
+        return str(e)  # Return error message if any issue occurs
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=interface,
+    inputs=gr.Image(type="pil", label="Upload an Image"),  # Input for uploading an image
+    outputs=gr.Textbox(label="What image tells???"),  # Output will be the caption
+    title="Image Captioning with BLIP",
+    description="Upload an image to generate a caption using the BLIP model."
+)
+
+# Launch the interface
+iface.launch(inline = False)