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app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import requests
+from io import BytesIO
+from transformers import AutoProcessor, BlipForConditionalGeneration
+
+# Load the pretrained processor and model
+processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+def fetch_image(url: str) -> np.ndarray:
+    """Fetch an image from a given URL and return it as a numpy array."""
+    try:
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+        image = Image.open(response.raw).convert('RGB')
+        return np.array(image)
+    except Exception as e:
+        raise ValueError(f"Failed to fetch image: {str(e)}")
+
+def caption_image(input_image=None, image_url=None):
+    """Generate captions for the input image or image fetched from a URL."""
+    try:
+        if image_url:
+            image_array = fetch_image(image_url)
+        elif input_image is not None:
+            image_array = input_image
+        else:
+            raise ValueError("Please provide either an image or an image URL.")
+
+        # Ensure the image is in RGB format
+        pil_image = Image.fromarray(image_array).convert('RGB')
+
+        # Process the image and generate caption
+        inputs = processor(pil_image, return_tensors="pt")
+        out = model.generate(**inputs, max_length=50)
+        caption = processor.decode(out[0], skip_special_tokens=True)
+
+        # Save caption as a downloadable .txt file
+        caption_path = "caption.txt"
+        with open(caption_path, "w") as f:
+            f.write(caption)
+
+        return caption, caption_path
+    except Exception as e:
+        return f"Error: {str(e)}", None
+
+iface = gr.Interface(
+    fn=caption_image, 
+    inputs=[
+        gr.Image(type="numpy", label="Upload Image"),
+        gr.Textbox(label="Image URL (Optional)", placeholder="Enter an image URL here")
+    ], 
+    outputs=[
+        gr.Textbox(label="Generated Caption"),
+        gr.File(label="Download Caption")
+    ],
+    examples = [
+        ["model.jpg"],
+        ["horse.jpeg"],
+        ["panda.jpg"]
+    ],
+    title="Advanced Image Captioning with the BLIP model",
+    description="Upload an image or provide a URL to generate a caption. Download the generated caption as a .txt file.",
+    live=True,
+    theme="compact"
+)
+
+iface.launch()

requirements.txt

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+gradio==3.29.0
+transformers
+torch
+Pillow
+requests