Create app.py

app.py

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+import gradio as gr
+from gradio_client import Client, handle_file
+import PIL.Image
+
+# Connect to the verified IC-Light engine
+client = Client("lllyasviel/IC-Light") # cite: 5.3
+
+def dynamic_relight(image, prompt, lighting_choice, *slider_values):
+    # 1. Identify keywords from your project prompt
+    features = ["cinematic", "detailed", "texture", "focus"]
+    
+    # 2. Build a weighted prompt based on your SLIDERS
+    weighted_parts = []
+    for i, feature in enumerate(features):
+        weight = slider_values[i] / 50 # Convert 0-100 to 0.0-2.0
+        weighted_parts.append(f"({feature}:{weight:.1f})")
+    
+    final_prompt = f"{prompt}, " + ", ".join(weighted_parts)
+    
+    # 3. Execute the request to the high-end hardware
+    # This ensures accuracy without crashing your own session
+    result = client.predict(
+        input_fg=handle_file(image),
+        prompt=final_prompt,
+        image_relation="Appearance Variation",
+        lighting_preference=lighting_choice,
+        api_name="/relight"
+    )
+    
+    # Result[0] is typically the main relit image
+    return result[0]
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 💡 My Unique Project: Dynamic Feature Relighter")
+    
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(type="filepath", label="Input Image")
+            user_prompt = gr.Textbox(label="Base Prompt", value="A professional portrait")
+            light_pref = gr.Radio(["Left Light", "Right Light", "Top Light", "Bottom Light"], label="Light Direction", value="Left Light")
+            
+            gr.Markdown("### User-Desired Feature Adjustments")
+            s1 = gr.Slider(0, 100, value=50, label="Cinematic Intensity")
+            s2 = gr.Slider(0, 100, value=50, label="Detail Level")
+            s3 = gr.Slider(0, 100, value=50, label="Skin Texture Sharpness")
+            s4 = gr.Slider(0, 100, value=50, label="Focus Depth")
+            
+            run_btn = gr.Button("Execute Relighting", variant="primary")
+        
+        with gr.Column():
+            output_img = gr.Image(label="Accurate AI Output")
+
+    run_btn.click(
+        fn=dynamic_relight,
+        inputs=[input_img, user_prompt, light_pref, s1, s2, s3, s4],
+        outputs=output_img
+    )
+
+demo.launch()