Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import pipeline
+
+# Load the ViT+GPT2 image-to-text pipeline with bfloat16 precision
+captioner = pipeline(
+    "image-to-text",
+    model="nlpconnect/vit-gpt2-image-captioning",
+    torch_dtype=torch.bfloat16
+)
+
+def generate_caption(image):
+    """
+    Takes a PIL image and returns a generated caption.
+    """
+    outputs = captioner(image)
+    return outputs[0]["generated_text"]
+
+# Build the Gradio interface
+with gr.Blocks(theme=gr.themes.Default()) as demo:
+    gr.Markdown(
+        """
+        # 🖼️ Image Caption Generator
+        Upload an image to generate a descriptive caption using ViT+GPT2.
+        """
+    )
+
+    with gr.Row():
+        input_image = gr.Image(type="pil", label="Upload Image")
+        caption_output = gr.Textbox(label="Generated Caption", lines=2)
+
+    generate_btn = gr.Button("Generate Caption")
+    generate_btn.click(fn=generate_caption, inputs=input_image, outputs=caption_output)
+
+    gr.Markdown(
+        """
+        ---
+        Built with 🤗 Transformers (`nlpconnect/vit-gpt2-image-captioning`) and 🚀 Gradio
+        """
+    )
+
+demo.launch()