Model Deployed

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+# Load model and processor
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+
+# Define the function to generate caption
+def generate_caption(image):
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=25)
+    caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return caption
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Image Caption Generator",
+    description="Upload an image to generate a caption."
+)
+
+# Launch
+iface.launch()

requirements.txt

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+torch
+transformers
+gradio
+Pillow