Create app.py

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+# Load your model and processor
+processor = BlipProcessor.from_pretrained("quadranttechnologies/Dileep_model")
+model = BlipForConditionalGeneration.from_pretrained("quadranttechnologies/Dileep_model")
+
+# Define a function to generate captions for the uploaded image
+def generate_caption(image):
+    # Convert the image into the required format for the model
+    inputs = processor(image, return_tensors="pt")
+
+    # Generate caption
+    outputs = model.generate(**inputs)
+    caption = processor.decode(outputs[0], skip_special_tokens=True)
+    return caption
+
+# Set up Gradio interface for image upload and caption generation
+interface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(type="pil"),  # Accepts uploaded images
+    outputs="text",               # Displays the caption as text
+    title="Image Captioning Model",
+    description="Upload an image to receive a caption generated by the model."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()