Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+import requests
+import os
+
+# Hugging Face API configuration
+API_URL = "https://api-inference.huggingface.co/models/nlpconnect/vit-gpt2-image-captioning"
+API_KEY = os.getenv("HF_API_KEY")  # Fetch the API key from Hugging Face secrets
+
+headers = {"Authorization": f"Bearer {API_KEY}"}
+
+def generate_caption(image):
+    """Queries the Hugging Face API to generate an image caption."""
+    if not API_KEY:
+        return "API key is missing! Please set it in Hugging Face secrets."
+    response = requests.post(API_URL, headers=headers, files={"file": image})
+    if response.status_code == 200:
+        result = response.json()
+        return result[0].get("generated_text", "No caption generated.")
+    else:
+        return f"Error: {response.status_code}, {response.text}"
+
+# Gradio UI components
+title = "✨Captionator_X ✨"
+description = """
+Upload an image, and let AI describe it for you in a creative and accurate way!  
+This application uses Hugging Face's state-of-the-art image captioning model.
+"""
+theme = gr.themes.Monochrome()
+
+with gr.Blocks(theme=theme) as app:
+    gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
+    gr.Markdown(f"<p style='text-align: center;'>{description}</p>")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="file", label="Upload Your Image", elem_id="image-uploader")
+        with gr.Column():
+            output_text = gr.Textbox(label="Generated Caption", lines=4, interactive=False, elem_id="output-text")
+    
+    submit_button = gr.Button("✨ Generate Caption ✨", elem_id="submit-btn")
+    submit_button.click(generate_caption, inputs=[image_input], outputs=[output_text])
+    
+    gr.Markdown("<p style='text-align: center; font-size: 0.9rem;'>Powered by Hugging Face 🤗 and Gradio 🚀</p>")
+
+app.launch()