Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+
+# Load model and processor
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Captioning function
+def generate_caption(upload_img, webcam_img):
+    # Choose image from upload or webcam
+    image = webcam_img if webcam_img is not None else upload_img
+    if image is None:
+        return "No image provided."
+    # Preprocess
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
+    # Generate
+    output_ids = model.generate(pixel_values, max_length=16, num_beams=4)
+    caption = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
+    return caption
+
+# Build Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Image Captioning with Gradio")
+    with gr.Row():
+        upload_input = gr.Image(source="upload", type="pil", label="Upload Image")
+        webcam_input = gr.Image(source="webcam", type="pil", label="Use Camera")
+    output_text = gr.Textbox(label="Caption", interactive=False)
+    generate_btn = gr.Button("Generate Caption")
+    generate_btn.click(
+        fn=generate_caption,
+        inputs=[upload_input, webcam_input],
+        outputs=output_text
+    )
+
+demo.launch()