Create app.py

app.py

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+import torch
+from transformers import pipeline
+from PIL import Image
+import gradio as gr
+import os
+
+# Specify the device (CPU or GPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the image-to-text pipeline
+caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
+
+# List of local image paths
+example_images = ["image1.jpeg"]
+
+# Function to process the image
+def process_image(image):
+    caption = caption_image(image)[0]['generated_text']
+    return caption
+
+# Create Gradio interface with example images
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Generated Caption"),
+    examples=example_images  # Use local images as examples
+)
+
+# Launch the interface
+iface.launch()