refine and dockerize the app

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+venv/
+checkpoints

Dockerfile

@@ -0,0 +1,25 @@
+FROM python:3.9-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+# Required for OpenCV image display & ultralytics
+RUN apt-get update && apt-get install -y \
+    libgl1 \
+    libglib2.0-0 \
+    git \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+
+RUN pip install --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7861
+
+CMD ["python", "app.py"]

README.md

@@ -3,7 +3,7 @@ title: ReaLens
 emoji: 🐢
 colorFrom: pink
 colorTo: pink
-sdk: gradio
+sdk: docker
 sdk_version: 5.43.1
 app_file: app.py
 pinned: false

app.py

@@ -1,6 +1,3 @@
-
-
-
 # import gradio as gr
 # import torch
 # import os
@@ -172,11 +169,6 @@
 
 
 
-
-
-
-
-
 import gradio as gr
 import torch
 import os
@@ -187,18 +179,73 @@ 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:
+    # This is handled globally but kept here for local context
     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) ---")
 
+# --- Documentation Strings ---
+
+USAGE_GUIDELINES = """
+## 1. Quick Start Guide: Generating an HDR Image
+This tool uses a sophisticated AI model (CycleGAN) to translate the characteristics of a single, optimally selected Low Dynamic Range (LDR) image into a High Dynamic Range (HDR) output.
+
+1.  **Upload:** Use the 'Upload Bracketed LDR Images' box to upload **at least two** images of the same scene, taken at different exposures (bracketed).
+2.  **Run:** Click the **"Process Images"** button.
+3.  **Review:**
+    *   The model first runs an analysis to select the 'Best LDR'.
+    *   The selected LDR is then processed, and the 'Final HDR Image' will appear.
+"""
+
+INPUT_EXPLANATION = """
+## 2. Input Requirements and Best Practices
+
+| Input Field | Purpose | Requirement |
+| :--- | :--- | :--- |
+| **LDR Images** | A set of images of the same scene captured with different exposure values (bracketing). | Must be 2 or more standard image files (JPG, PNG). |
+
+### Best Practices for Input Images
+*   **Bracketing is Key:** The quality of the final HDR output heavily depends on the diversity and quality of the input bracket set (underexposed, correctly exposed, and overexposed).
+*   **Scene Consistency:** All uploaded images must be of the **exact same scene** and taken from the **exact same camera position** (tripod recommended). Motion between frames will lead to conversion artifacts.
+*   **Resolution:** While the model processes images internally, uploading high-resolution sources ensures the final scaled 1024xN output maintains sharp detail.
+"""
+
+TECHNICAL_GUIDANCE = """
+## 3. The Best LDR Selection Algorithm (Internal Logic)
+
+Unlike traditional HDR merging, this application first selects the single 'Best LDR' image from your uploads and then translates *that specific image* into HDR using a deep learning model.
+
+The selection process scores each image based on the following weighted metrics:
+
+| Metric | Weight | Description |
+| :--- | :--- | :--- |
+| **Clipped Pixels** | 35% | Penalizes images with over-saturated whites or completely black shadows. |
+| **Coverage** | 25% | Measures the range of usable tones across the image. |
+| **Exposure** | 15% | Measures closeness to ideal scene brightness. |
+| **Sharpness** | 15% | Measures overall clarity and focus of the image. |
+| **Noise** | 10% | Penalizes excessive grain or image noise. |
+
+The image with the highest composite score is chosen for the final AI conversion.
+"""
+
+OUTPUT_EXPLANATION = """
+## 4. Expected Outputs and Interpretation
+
+| Output Field | Description | Guidance |
+| :--- | :--- | :--- |
+| **Uploaded Images** | A gallery showing all LDR images provided as input. | Confirms which files were successfully loaded and analyzed by the scoring algorithm. |
+| **Final HDR Image** | The resulting image generated by the **CycleGAN** translation model. | This image should exhibit enhanced detail in very bright and very dark areas, greater overall contrast, and richer color vibrancy compared to the original LDRs. |
+
+### Note on Resolution
+The inference process scales the selected LDR image to **1024 pixels wide** internally, maintaining the original aspect ratio, before running the conversion model. The final output resolution will match this scaled size.
+"""
+
 # --- Global Setup: Load the CycleGAN model once when the app starts ---
 
 # We need to satisfy the parser's requirement for a dataroot at startup
@@ -244,8 +291,16 @@ def process_images_to_hdr(list_of_temp_files):
         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)
+        
+        # Check if the list of records is valid before scoring
+        valid_records = [r for r in records if r is not None]
+        if not valid_records:
+             raise gr.Error("Could not process any uploaded images (ensure they are valid image files).")
+
+        scored_records = score_records(valid_records, weights)
+        
         if not scored_records:
+            # This should ideally be caught by the valid_records check, but remains a safeguard
             raise gr.Error("Could not read or score any of the uploaded images.")
             
         best_ldr_record = scored_records[0]
@@ -264,6 +319,9 @@ def process_images_to_hdr(list_of_temp_files):
         
         # Deep copy the base options to avoid modifying the global state
         local_opt = copy.deepcopy(opt)
+        local_opt.num_threads = 0   # disable multiprocessing
+        local_opt.batch_size = 1    # safety
+        local_opt.serial_batches = True
         for key, value in inference_options.items():
             setattr(local_opt, key, value)
 
@@ -301,20 +359,35 @@ with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}")
         """
     )
     
+    # Add Guidelines
+    with gr.Accordion("Tips & User Guidelines", open=False):
+        gr.Markdown(USAGE_GUIDELINES)
+        gr.Markdown("---")
+        gr.Markdown(INPUT_EXPLANATION)
+        gr.Markdown("---")
+        gr.Markdown(TECHNICAL_GUIDANCE)
+        gr.Markdown("---")
+        gr.Markdown(OUTPUT_EXPLANATION)
+
     with gr.Row():
-        with gr.Column(scale=1, min_width=350):
+        with gr.Column(scale=1):
             # --- INPUT ---
+            gr.Markdown("## Step 1: Upload LDR Images")
             input_files = gr.Files(
-                label="Upload Bracketed LDR Images",
+                label="Bracketed LDR Images",
                 file_types=["image"]
             )
+            gr.Markdown("## Step 2: Click Process Images")
             process_button = gr.Button("Process Images", variant="primary")
-            with gr.Accordion("See Your Uploaded Images", open=True):
-                 input_gallery = gr.Gallery(label="Uploaded Images", show_label=False, columns=[2, 3], height="auto")
+
+    # with gr.Row():
+        
 
         with gr.Column(scale=2):
-            # --- OUTPUT ---
             gr.Markdown("## Generated HDR Result")
+            with gr.Accordion("See Your Uploaded Images", open=False):
+                 input_gallery = gr.Gallery(label="Uploaded Images", show_label=False, columns=[2, 3], height="auto")
+            
             output_image = gr.Image(label="Final HDR Image", type="pil", interactive=False, show_download_button=True)
 
     process_button.click(
@@ -327,13 +400,43 @@ with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}")
     # gr.Examples(
     #     examples=[
     #         [
-    #             "../pix2pix_dataset/testA/077A2406.jpg", 
-    #             "../pix2pix_dataset/testA/077A4049.jpg", 
-    #             "../pix2pix_dataset/testA/077A4073.jpg"
+    #             "./sample_data/ldr5.jpg", 
+    #             "./sample_data/ldr2.jpeg", 
+    #             "./sample_data/ldr1.jpg",
+    #             "./sample_data/ldr6.jpg",
     #         ]
     #     ],
-    #     inputs=input_files
+    #     inputs=input_files,
+    #     label="Click on an image to test"
     # )
 
+        # --- Find the base directory for robust path resolution ---
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+    SAMPLE_DATA_DIR = os.path.join(BASE_DIR, "sample_data")
+
+    EXAMPLE_FILES = [
+        os.path.join(SAMPLE_DATA_DIR, "ldr5.jpg"), 
+        os.path.join(SAMPLE_DATA_DIR, "ldr2.jpeg"), 
+        os.path.join(SAMPLE_DATA_DIR, "ldr1.jpg"),
+        os.path.join(SAMPLE_DATA_DIR, "ldr6.jpg"),
+    ]
+
+    # ... inside the gr.Blocks demo ...
+
+    gr.Markdown("### Examples")
+    gr.Examples(
+        # Correct structure: 
+        # examples=[ [ [value for input 1] ] ]
+        # Since input_files accepts a LIST of files, the value is that list.
+        examples=[
+            [EXAMPLE_FILES] 
+        ],
+        inputs=[input_files], # inputs must be a list of components
+        label="Click to load these LDR images"
+    )
+
 print("--- Launching Gradio App ---")
-demo.launch(share=True)
+demo.launch(
+    server_name="0.0.0.0",
+    server_port=7861
+)

requirements.txt

@@ -1,8 +1,75 @@
-# requirements.txt
-
-gradio
-numpy
-opencv-python
-pillow
-torch
-torchvision
+aiofiles==23.2.1
+altair==5.5.0
+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.12.1
+attrs==25.4.0
+certifi==2026.1.4
+charset-normalizer==3.4.4
+click==8.1.8
+contourpy==1.3.0
+cycler==0.12.1
+exceptiongroup==1.3.1
+fastapi==0.128.0
+ffmpy==1.0.0
+filelock==3.19.1
+fonttools==4.60.2
+fsspec==2025.10.0
+gradio==3.50.2
+gradio_client==0.6.1
+h11==0.16.0
+hf-xet==1.2.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface_hub==1.3.1
+idna==3.11
+importlib_resources==6.5.2
+Jinja2==3.1.6
+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+kiwisolver==1.4.7
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.4
+mdurl==0.1.2
+mpmath==1.3.0
+narwhals==2.15.0
+networkx==3.2.1
+numpy==1.26.4
+opencv-python==4.12.0.88
+orjson==3.11.5
+packaging==25.0
+pandas==2.3.3
+pillow==10.4.0
+pydantic==2.12.5
+pydantic_core==2.41.5
+pydub==0.25.1
+Pygments==2.19.2
+pyparsing==3.3.1
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2025.2
+PyYAML==6.0.3
+referencing==0.36.2
+requests==2.32.5
+rich==14.2.0
+rpds-py==0.27.1
+ruff==0.14.11
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+starlette==0.49.3
+sympy==1.14.0
+tomlkit==0.12.0
+torch==2.2.2
+torchvision==0.17.2
+tqdm==4.67.1
+typer==0.21.1
+typer-slim==0.21.1
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+tzdata==2025.3
+urllib3==2.6.3
+uvicorn==0.39.0
+websockets==11.0.3
+zipp==3.23.0