Update app.py

app.py

@@ -1,61 +1,62 @@
-import gradio as gr
-from PIL import Image
-from io import BytesIO
-from src.pipeline import InferencePipeline
-from src.app.config import load_config
-
-# Load configuration and initialize the inference pipeline
-config = load_config()
-inference_pipeline = InferencePipeline(config)
-
-def process_image_from_bytes(file, apply_clahe_postprocess,apply_pre_contrast_adjustment,return_original_size):
-    """
-    Process the image bytes using the inference pipeline.
-
-    Args:
-        file_bytes: The image file in bytes.
-        apply_clahe_postprocess: Boolean indicating if CLAHE postprocessing should be applied.
-
-    Returns:
-        The processed image.
-    """
-    try:
-        # Perform super-resolution
-        sr_image = inference_pipeline.run(file, apply_pre_contrast_adjustment=apply_pre_contrast_adjustment, apply_clahe_postprocess=apply_clahe_postprocess,return_original_size=return_original_size)
-        return sr_image
-    except Exception as e:
-        return f"An exception occurred: {str(e)}"
-
-# Define the Gradio interface
-def gradio_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("""
-        # X-Ray Image Super-Resolution-Denoiser Demo
-        Provide image bytes to process and optionally apply CLAHE postprocessing.
-        """)
-
-        with gr.Row():
-            file_input = gr.File(label="Upload Image (PNG, JPEG, or DICOM)")
-            apply_clahe_checkbox = gr.Checkbox(label="Apply CLAHE Postprocessing", value=False)
-            apply_pre_contrast_adjustment_checkbox = gr.Checkbox(label="Apply PreContrast Adjustment", value=False)
-            return_original_size_checkbox = gr.Checkbox(label="Return Original Size", value=True)
-
-        process_button = gr.Button("Process Image")
-        output_image = gr.Image(label="Processed Image")
-
-
-
-        process_button.click(
-            process_image_from_bytes,
-            inputs=[file_input, apply_clahe_checkbox,apply_pre_contrast_adjustment_checkbox,return_original_size_checkbox],
-            outputs=output_image
-        )
-
-    return demo
-
-# Launch the Gradio interface
-demo = gradio_interface()
-
-demo.launch(
-    debug=True,
-)
+import gradio as gr
+from PIL import Image
+from io import BytesIO
+from src.pipeline import InferencePipeline
+from src.app.config import load_config
+
+# Load configuration and initialize the inference pipeline
+config = load_config()
+inference_pipeline = InferencePipeline(config)
+
+def process_image_from_bytes(file, apply_clahe_postprocess,apply_pre_contrast_adjustment,return_original_size):
+    """
+    Process the image bytes using the inference pipeline.
+
+    Args:
+        file_bytes: The image file in bytes.
+        apply_clahe_postprocess: Boolean indicating if CLAHE postprocessing should be applied.
+
+    Returns:
+        The processed image.
+    """
+    try:
+        # Perform super-resolution
+        sr_image = inference_pipeline.run(file, apply_pre_contrast_adjustment=apply_pre_contrast_adjustment, apply_clahe_postprocess=apply_clahe_postprocess,return_original_size=return_original_size)
+        return sr_image
+    except Exception as e:
+        return f"An exception occurred: {str(e)}"
+
+# Define the Gradio interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("""
+        # X-Ray Image Super-Resolution-Denoiser Demo
+        Provide image bytes to process and optionally apply CLAHE postprocessing.
+        For github : Whole code with FastAPI and Docker  - https://github.com/SerdarHelli/xray-superres-enhancer
+        """)
+
+        with gr.Row():
+            file_input = gr.File(label="Upload Image (PNG, JPEG, or DICOM)")
+            apply_clahe_checkbox = gr.Checkbox(label="Apply CLAHE Postprocessing", value=False)
+            apply_pre_contrast_adjustment_checkbox = gr.Checkbox(label="Apply PreContrast Adjustment", value=False)
+            return_original_size_checkbox = gr.Checkbox(label="Return Original Size", value=True)
+
+        process_button = gr.Button("Process Image")
+        output_image = gr.Image(label="Processed Image")
+
+
+
+        process_button.click(
+            process_image_from_bytes,
+            inputs=[file_input, apply_clahe_checkbox,apply_pre_contrast_adjustment_checkbox,return_original_size_checkbox],
+            outputs=output_image
+        )
+
+    return demo
+
+# Launch the Gradio interface
+demo = gradio_interface()
+
+demo.launch(
+    debug=True,
+)