update the port number

Dockerfile

@@ -20,6 +20,6 @@ RUN pip install --upgrade pip \
 
 COPY . .
 
-EXPOSE 7861
+EXPOSE 7860
 
 CMD ["python", "app.py"]

app.py

@@ -1,174 +1,3 @@
-# import gradio as gr
-# import torch
-# import os
-# import tempfile
-# import shutil
-# from PIL import Image
-# import numpy as np
-# from pathlib import Path
-# import sys
-# import copy
-
-# # --- Import logic from your project ---
-# from options.test_options import TestOptions
-# from data import create_dataset
-# from models import create_model
-# try:
-#     from best_ldr import compute_metrics_for_images, score_records
-# except ImportError:
-#     raise ImportError("Could not import from best_ldr.py. Make sure the file is in the same directory as app.py.")
-
-# print("--- Initializing LDR-to-HDR Model (this may take a moment) ---")
-
-# # --- Global Setup: Load the CycleGAN model once when the app starts ---
-
-# # We need to satisfy the parser's requirement for a dataroot at startup
-# if '--dataroot' not in sys.argv:
-#     sys.argv.extend(['--dataroot', './dummy_dataroot_for_init'])
-
-# # Load the base options
-# opt = TestOptions().parse()
-
-# # Manually override settings for our model
-# opt.name = 'ldr2hdr_cyclegan_728'
-# opt.model = 'test'
-# opt.netG = 'resnet_9blocks'
-# opt.norm = 'instance'
-# opt.no_dropout = True
-# opt.checkpoints_dir = './checkpoints'
-# opt.gpu_ids = [0] if torch.cuda.is_available() else []
-# opt.device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if opt.gpu_ids else torch.device('cpu')
-
-# # Create the model using these options
-# model = create_model(opt)
-# model.setup(opt)
-# model.eval()
-
-# print("--- Model Loaded Successfully ---")
-
-
-# # --- Helper Function for Inference ---
-
-# def run_inference(model, image_path, process_options):
-#     """
-#     A reusable function to run the model with specific preprocessing options.
-#     """
-#     # Deep copy the base options to avoid modifying the global state
-#     local_opt = copy.deepcopy(opt)
-
-#     # Apply the specific settings for this run
-#     for key, value in process_options.items():
-#         setattr(local_opt, key, value)
-
-#     with tempfile.TemporaryDirectory() as temp_dir:
-#         shutil.copy(image_path, temp_dir)
-#         local_opt.dataroot = temp_dir
-#         local_opt.num_test = 1
-#         dataset = create_dataset(local_opt)
-
-#         for i, data in enumerate(dataset):
-#             model.set_input(data)
-#             model.test()
-#             visuals = model.get_current_visuals()
-            
-#             for label, image_tensor in visuals.items():
-#                 if label == 'fake':
-#                     image_numpy = (np.transpose(image_tensor.cpu().float().numpy()[0], (1, 2, 0)) + 1) / 2.0 * 255.0
-#                     return Image.fromarray(image_numpy.astype(np.uint8))
-
-# # --- The Main Gradio Processing Function ---
-
-# def process_images_and_display(list_of_temp_files):
-#     """
-#     The main workflow: select best LDR, then run two inference modes.
-#     """
-#     if not list_of_temp_files:
-#         raise gr.Error("Please upload your bracketed LDR images.")
-#     if len(list_of_temp_files) < 2:
-#         gr.Warning("For best results, upload at least 2 bracketed LDR images.")
-
-#     uploaded_filepaths = [Path(f.name) for f in list_of_temp_files]
-    
-#     try:
-#         # --- Step 1: Select the Best LDR ---
-#         print(f"Analyzing {len(uploaded_filepaths)} uploaded images...")
-#         weights = {"clipped": 0.35, "coverage": 0.25, "exposure": 0.15, "sharpness": 0.15, "noise": 0.10}
-#         records = compute_metrics_for_images(uploaded_filepaths, resize_max=1024)
-#         scored_records = score_records(records, weights)
-#         if not scored_records:
-#             raise gr.Error("Could not read or score any of the uploaded images.")
-            
-#         best_ldr_record = scored_records[0]
-#         best_ldr_path = best_ldr_record['path']
-#         print(f"Best LDR selected: {os.path.basename(best_ldr_path)} (Score: {best_ldr_record['score']:.4f})")
-#         chosen_ldr_image = Image.open(best_ldr_path).convert("RGB")
-
-#         # --- Step 2: Run Inference in Both Modes ---
-        
-#         # Mode A: High-Quality Crop (at model's native resolution)
-#         print("Running Mode A: High-Quality Crop...")
-#         crop_options = {
-#             'preprocess': 'resize_and_crop',
-#             'load_size': 728,
-#             'crop_size': 728
-#         }
-#         hdr_cropped = run_inference(model, best_ldr_path, crop_options)
-#         print("Mode A successful.")
-        
-#         # Mode B: Full Image (at a higher resolution)
-#         print("Running Mode B: Full Image (High-Res Scaled)...")
-#         scale_options = {
-#             'preprocess': 'scale_width',
-#             'load_size': 1024, # <-- THIS IS THE CHANGE FOR HIGHER RESOLUTION
-#             'crop_size': 728 # This value is ignored by scale_width but needs to be present
-#         }
-#         hdr_scaled = run_inference(model, best_ldr_path, scale_options)
-#         print("Mode B successful.")
-        
-#         # Return all the images to update the UI
-#         return uploaded_filepaths, chosen_ldr_image, hdr_cropped, hdr_scaled
-
-#     except Exception as e:
-#         print(f"An error occurred: {e}")
-#         raise gr.Error(f"An error occurred during processing: {e}")
-
-# # --- Create and Launch the Gradio Interface ---
-
-# with gr.Blocks(theme=gr.themes.Monochrome(), css="footer {display: none !important}") as demo:
-#     gr.Markdown("# LDR Bracketing to HDR Converter")
-#     gr.Markdown("Upload a set of bracketed LDR images. The app will automatically select the best one and convert it to HDR using two different methods for comparison.")
-    
-#     with gr.Row():
-#         with gr.Column(scale=1, min_width=300):
-#             input_files = gr.Files(
-#                 label="Upload Bracketed LDR Images",
-#                 file_types=["image"]
-#             )
-#             process_button = gr.Button("Process Images", variant="primary")
-            
-#             with gr.Accordion("See Your Uploads", open=False):
-#                  input_gallery = gr.Gallery(label="Uploaded LDR Bracket", show_label=False, columns=3, height="auto")
-
-#         with gr.Column(scale=2):
-#             gr.Markdown("## Results")
-#             with gr.Row():
-#                 chosen_ldr_display = gr.Image(label="Best LDR Chosen by Algorithm", type="pil", interactive=False)
-#             with gr.Row():
-#                 output_cropped = gr.Image(label="Result 1: High-Quality Crop (728x728)", type="pil", interactive=False)
-#                 output_scaled = gr.Image(label="Result 2: Full Image (Scaled to 1024px Width)", type="pil", interactive=False)
-
-#     process_button.click(
-#         fn=process_images_and_display,
-#         inputs=input_files,
-#         outputs=[input_gallery, chosen_ldr_display, output_cropped, output_scaled]
-#     )
-
-# print("--- Launching Gradio App ---")
-# demo.launch(share=True)
-
-
-
-
 import gradio as gr
 import torch
 import os
@@ -438,5 +267,5 @@ with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}")
 print("--- Launching Gradio App ---")
 demo.launch(
     server_name="0.0.0.0",
-    server_port=7861
+    server_port=7860
 )