Create app.py

app.py

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+import gradio as gr
+from gradio_client import Client, file
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Initialize the Hugging Face API clients
+captioning_client = Client("fancyfeast/joy-caption-pre-alpha")
+generation_client = Client("black-forest-labs/FLUX.1-dev")
+
+# Function to caption an image
+def caption_image(image):
+    caption = captioning_client.predict(
+        input_image=image,
+        api_name="/stream_chat"
+    )
+    return caption
+
+# Function to generate an image from a text prompt using Hugging Face API
+def generate_image_from_caption(caption):
+    image = generation_client.predict(
+        prompt=caption,
+        seed=0,
+        randomize_seed=True,
+        width=1024,
+        height=1024,
+        guidance_scale=3.5,
+        num_inference_steps=28,
+        api_name="/infer"
+    )
+    return image
+
+# Main function to handle the upload and generate images and captions in a loop
+def process_image(image, iterations):
+    generated_images = []
+    captions = []
+    
+    current_image = image
+    
+    for i in range(iterations):
+        # Caption the current image
+        caption = caption_image(current_image)
+        captions.append(caption)
+        
+        # Generate a new image based on the caption
+        new_image = generate_image_from_caption(caption)
+        generated_images.append(new_image)
+        
+        # Set the newly generated image as the current image for the next iteration
+        current_image = new_image
+    
+    return generated_images, captions
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload an Image")
+        iterations_input = gr.Number(value=3, label="Number of Iterations")
+    
+    with gr.Row():
+        output_images = gr.Gallery(label="Generated Images")
+        output_captions = gr.Textbox(label="Generated Captions")
+    
+    generate_button = gr.Button("Generate")
+    
+    generate_button.click(
+        fn=process_image,
+        inputs=[image_input, iterations_input],
+        outputs=[output_images, output_captions]
+    )
+
+# Launch the app
+demo.launch()