Add logging and error handling to app.py; update Gradio interface and fix JSON schema issue

app.py

@@ -1,6 +1,30 @@
 import gradio as gr
 import numpy as np
 import random
+import logging
+import sys
+
+# 设置日志记录
+logging.basicConfig(level=logging.INFO, 
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+                    stream=sys.stdout)
+logger = logging.getLogger(__name__)
+
+# 修复 Gradio JSON Schema 错误
+try:
+    import gradio_client.utils
+    # 添加对布尔值的检查
+    original_get_type = gradio_client.utils.get_type
+    def patched_get_type(schema):
+        if isinstance(schema, bool):
+            return "bool"
+        if not isinstance(schema, dict):
+            return "any"
+        return original_get_type(schema)
+    gradio_client.utils.get_type = patched_get_type
+    logger.info("Successfully patched Gradio JSON schema processing")
+except Exception as e:
+    logger.error(f"Failed to patch Gradio: {str(e)}")
 
 # import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
@@ -9,18 +33,25 @@ import torch
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 
+logger.info(f"Using device: {device}")
+logger.info(f"Loading model: {model_repo_id}")
+
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+try:
+    pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+    pipe = pipe.to(device)
+    logger.info("Model loaded successfully")
+except Exception as e:
+    logger.error(f"Error loading model: {str(e)}")
+    raise
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
@@ -33,23 +64,31 @@ def infer(
     num_inference_steps,
     progress=gr.Progress(track_tqdm=True),
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
+    try:
+        logger.info(f"Processing prompt: {prompt}")
+        
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+            
+        logger.info(f"Using seed: {seed}, width: {width}, height: {height}")
+        
+        generator = torch.Generator().manual_seed(seed)
+
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+        ).images[0]
+
+        logger.info("Image generation successful")
+        return image, seed
+    except Exception as e:
+        logger.error(f"Error in inference: {str(e)}")
+        raise gr.Error(f"Error generating image: {str(e)}")
 
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -64,91 +103,101 @@ css = """
 }
 """
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+try:
+    with gr.Blocks(css=css) as demo:
+        with gr.Column(elem_id="col-container"):
+            gr.Markdown(" # Text-to-Image Gradio Template")
 
             with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
                 )
 
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+                run_button = gr.Button("Run", scale=0, variant="primary")
 
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+            result = gr.Image(label="Result", show_label=False)
+
+            with gr.Accordion("Advanced Settings", open=False):
+                negative_prompt = gr.Text(
+                    label="Negative prompt",
+                    max_lines=1,
+                    placeholder="Enter a negative prompt",
+                    visible=True, # 改为可见
                 )
 
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=0,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+                with gr.Row():
+                    width = gr.Slider(
+                        label="Width",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,
+                    )
+
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,
+                    )
+
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.0,
+                        maximum=10.0,
+                        step=0.1,
+                        value=0.0,
+                    )
+
+                    num_inference_steps = gr.Slider(
+                        label="Number of inference steps",
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        value=2,
+                    )
+
+            gr.Examples(examples=examples, inputs=[prompt])
+        gr.on(
+            triggers=[run_button.click, prompt.submit],
+            fn=infer,
+            inputs=[
+                prompt,
+                negative_prompt,
+                seed,
+                randomize_seed,
+                width,
+                height,
+                guidance_scale,
+                num_inference_steps,
+            ],
+            outputs=[result, seed],
+        )
+    
+    logger.info("Gradio interface created successfully")
+except Exception as e:
+    logger.error(f"Error creating Gradio interface: {str(e)}")
+    raise
 
 if __name__ == "__main__":
-    demo.launch()
+    try:
+        logger.info("Starting Gradio app")
+        demo.launch()
+    except Exception as e:
+        logger.error(f"Error launching app: {str(e)}")

requirements.txt

@@ -4,4 +4,4 @@ invisible_watermark
 torch
 transformers
 xformers
-gradio==3.50.2
+gradio==3.45.0