Create app.py

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+import os
+
+# Set your Inference Endpoint URL and API key
+INFERENCE_ENDPOINT = "https://your-endpoint-url"  # Replace with your endpoint URL
+API_TOKEN = "your-api-token"  # Replace with your Hugging Face API token
+
+def generate_caption(image):
+    """
+    Sends an image to the Hugging Face Inference Endpoint for caption generation.
+    :param image: An image in PIL format.
+    :return: Generated caption or error message.
+    """
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    files = {"inputs": image}
+    response = requests.post(INFERENCE_ENDPOINT, headers=headers, files=files)
+
+    if response.status_code == 200:
+        return response.json().get("generated_text", "No caption generated.")
+    else:
+        return f"Error: {response.status_code} - {response.text}"
+
+
+
+# Gradio interface
+
+demo = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.inputs.Image(type="file", label="Upload Image"),
+    outputs=gr.outputs.Textbox(label="Generated Caption"),
+    examples=[image1, image2, image3],
+    title="Image Captioning App",
+    description=(
+        "Upload an image or use one of the predefined samples to generate a caption. "
+        "This app uses a Hugging Face Inference Endpoint for the `Salesforce/blip-image-captioning-base` model."
+    ),
+)
+
+if __name__ == "__main__":
+    demo.launch()