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README (2).md

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+---
+title: Image Upload Model
+emoji: 📚
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 5.5.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: '"Upload an image to get model-generated results via an API"'
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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):
+    try:
+        # 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
+    except Exception as e:
+        return f"Error generating caption: {e}"
+
+# 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(share=True)  # Set share=True to enable public link if needed
+