Create app.py

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+from gradio_client import Client, handle_file
+
+def get_flux_image(prompt):
+    client = Client("black-forest-labs/FLUX.1-schnell")
+    result = client.predict(
+		prompt=prompt,
+		seed=0,
+		randomize_seed=True,
+		width=1024,
+		height=1024,
+		num_inference_steps=4,
+		api_name="/infer"
+    )
+    print(result)
+    return result[0]
+
+def get_upscale(prompt, img_path):
+    client = Client("finegrain/finegrain-image-enhancer")
+    result = client.predict(
+		input_image=handle_file(img_path),
+		prompt=prompt,
+		negative_prompt="",
+		seed=42,
+		upscale_factor=2,
+		controlnet_scale=0.6,
+		controlnet_decay=1,
+		condition_scale=6,
+		tile_width=112,
+		tile_height=144,
+		denoise_strength=0.35,
+		num_inference_steps=18,
+		solver="DDIM",
+		api_name="/process"
+    )
+    print(result)
+    return result[0]
+
+def main(prompt):
+    step_one_flux = get_flux_image(prompt)
+    step_two_upscale = get_upscale(prompt, step_one_flux)
+    return step_two_upscale
+
+css = """
+#col-container{
+    margin: 0 auto;
+    max-width: 1024px;
+}
+"""
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("Flux Upscaled")
+        prompt_in = gr.Textbox(label="Prompt")
+        submit_btn = gr.Button("Submit")
+        output_res = gr.Image(label="Result")
+
+    submit_btn.click(
+        fn=main,
+        inputs=[prompt_in],
+        outputs=[output_res],
+        
+    )
+
+demo.queue().launch(show_api=False, show_error=True)
+