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app (1).py

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+
+from gradio_client import Client
+import gradio as gr
+
+# Initialize Gradio Client
+client = Client("prithivMLmods/FireRed-Image-Edit-1.0-Fast")
+
+# Define Prediction Function
+def predict_image(images, prompt, seed, randomize_seed, guidance_scale, steps):
+    """
+    Calls the external model's /infer endpoint using the Gradio client
+    and returns the prediction result.
+
+    Args:
+        images: Input image(s).
+        prompt: Text prompt for image editing.
+        seed: Random seed.
+        randomize_seed: Boolean to randomize seed.
+        guidance_scale: Guidance scale for the model.
+        steps: Number of inference steps.
+
+    Returns:
+        The prediction result from the model (e.g., an image).
+    """
+    try:
+        result = client.predict(
+            images,
+            prompt,
+            seed,
+            randomize_seed,
+            guidance_scale,
+            steps,
+            api_name='/infer'
+        )
+        return result
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+        return None
+
+# Define input components
+input_images = gr.Image(type="filepath", label="Input Image")
+input_prompt = gr.Textbox(label="Prompt")
+input_seed = gr.Slider(minimum=0, maximum=2147483647, step=1, label="Seed", value=0)
+input_randomize_seed = gr.Checkbox(label="Randomize Seed", value=False)
+input_guidance_scale = gr.Slider(minimum=0.0, maximum=20.0, step=0.1, label="Guidance Scale", value=7.5)
+input_steps = gr.Slider(minimum=1, maximum=100, step=1, label="Inference Steps", value=20)
+
+# Create a list of input components
+input_components = [
+    input_images,
+    input_prompt,
+    input_seed,
+    input_randomize_seed,
+    input_guidance_scale,
+    input_steps
+]
+
+# Define the output component
+output_image = gr.Image(label="Edited Image")
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=input_components,
+    outputs=output_image,
+    title="FireRed Image Editor"
+)
+
+# Launch the Gradio app (optional for local testing, not needed for Spaces deployment if app.py is run directly)
+if __name__ == "__main__":
+    iface.launch()

requirements (1).txt

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+gradio
+gradio_client