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app.py

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+import torch
+from transformers import pipeline, AutoProcessor, AutoModelForVision2Seq
+from PIL import Image
+import gradio as gr
+
+# Automatically use GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load processor and model
+processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model = AutoModelForVision2Seq.from_pretrained("Salesforce/blip-image-captioning-large").to(device)
+
+# Inference function
+def generate_caption(image):
+    image = image.convert("RGB")
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    output = model.generate(**inputs)
+    caption = processor.decode(output[0], skip_special_tokens=True)
+    return caption
+
+# Gradio interface
+interface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="🖼️ Image to Text Captioning",
+    description="Upload an image and get a caption using BLIP (Salesforce/blip-image-captioning-large)."
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+torch
+transformers
+gradio
+Pillow