refine the gradio app

.gitignore

@@ -0,0 +1,2 @@
+venv
+gradio_cached_examples

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 7860
+
+CMD ["python", "app.py"]

README.md

@@ -3,7 +3,7 @@ title: Cell Segmentation FZJ INM1 BDA
 emoji: 🐠
 colorFrom: green
 colorTo: red
-sdk: gradio
+sdk: docker
 sdk_version: 5.33.2
 app_file: app.py
 pinned: false

app.py

@@ -5,6 +5,39 @@ import numpy as np
 import matplotlib.pyplot as plt
 from celldetection import fetch_model, to_tensor
 
+
+# --- DOCUMENTATION STRINGS (Client Friendly) ---
+
+USAGE_GUIDELINES = """
+## 1. Clear Setup and Run Instructions (Quick Start)
+This application uses the advanced GINORO segmentation model, pre-trained for identifying cell nuclei in microscopy images.
+
+1.  **Preparation:** Ensure your image is a clear microscopy slide image, preferably showing distinct cell nuclei.
+2.  **Upload:** Click the 'Input Microscopy Image' box and upload your image (drag and drop, or click to select).
+3.  **Run:** Click the **"Run Segmentation"** button. If using an example, clicking the thumbnail will load and run the segmentation automatically.
+4.  **Review:** The result panel will display two images side-by-side: the Original (Left) and the Segmented result (Right).
+"""
+
+INPUT_EXPLANATION = """
+## 2. Expected Inputs
+
+| Input Field | Purpose | Requirement |
+| :--- | :--- | :--- |
+| **Input Microscopy Image** | The high-resolution image containing the cells you wish to analyze. | Must be an image file (PNG, JPG, TIF). Optimal results are achieved with clear, well-focused images typical of fluorescence microscopy (e.g., DAPI staining for nuclei). |
+
+"""
+
+OUTPUT_EXPLANATION = """
+## 3. Expected Outputs (Side-by-Side Segmentation)
+
+The output is a single image combining the original input and the segmented result for easy comparison.
+
+*   **Left Side (Original):** The unmodified input image.
+*   **Right Side (Segmented):** The same image with outlines (contours) drawn over the detected cellular structures.
+*   **Contour Color:** The detected cell nuclei are outlined in **Blue**.
+
+"""
+
 # ✅ Load the model
 device = 'cpu'
 model = fetch_model('ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c').to(device).eval()
@@ -35,17 +68,131 @@ def segment(image):
 
 # ✅ Example images list
 examples = [
-    ["1.png"],
-    ["2.png"],
-    ["3.png"]
+    ["./sample_data/1.png"],
+    ["./sample_data/2.png"],
+    ["./sample_data/3.png"]
 ]
 
 # ✅ Launch the Gradio interface
-gr.Interface(
-    fn=segment,
-    inputs=gr.Image(type="numpy"),
-    outputs="image",
-    title="Cell Segmentation Demo (FZJ-INM1)",
-    description="Upload a microscopy image to see side-by-side segmentation.",
-    examples=examples
-).launch()
+
+with gr.Blocks(title="Cell Segmentation Demo (FZJ-INM1)") as demo:
+    
+    gr.Markdown(
+        """
+        # Cell Segmentation Demo (FZJ-INM1)
+        **Purpose:** Automatically identify and outline cell nuclei in microscopy images using a specialized neural network.
+        """
+    )
+
+    # 1. Guidelines Accordion (Documentation Section)
+    with gr.Accordion(" Tips & Guidelines ", open=False):
+        gr.Markdown(USAGE_GUIDELINES)
+        gr.Markdown("---")
+        gr.Markdown(INPUT_EXPLANATION)
+        gr.Markdown("---")
+        gr.Markdown(OUTPUT_EXPLANATION)
+        
+    gr.Markdown("---")
+    
+
+    #  Define Components
+    gr.Markdown("## Step 1: Upload an Image")
+    input_image = gr.Image(type="numpy", label="Input Microscopy Image")
+
+    gr.Markdown("## Step 2: Click button")
+    run_button = gr.Button("Run Segmentation", variant="primary")
+
+    gr.Markdown("## Output")
+    output_image = gr.Image(label="Output: Original (Left) vs. Segmented (Right)")
+    
+    # Layout the Application Interface
+    # with gr.Row():
+        
+    #     with gr.Column(scale=1):
+    #         input_image
+    #         gr.Markdown("## Step 2: Click button")
+    #         run_button
+    #     with gr.Column(scale=2):
+    #         output_image
+            
+    # Event Handler
+    run_button.click(
+        fn=segment,
+        inputs=input_image,
+        outputs=output_image
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("## Examples ")
+    
+    # 2. Examples Section (Error Fixed)
+    # By providing explicit inputs, outputs, and fn, we resolve the ValueError.
+    gr.Examples(
+        examples=examples,
+        inputs=[input_image],
+        outputs=output_image,
+        fn=segment,
+        label="Click on an image thumbnail below to load and run a sample segmentation.",
+    )
+
+    demo.launch(
+        server_name = "0.0.0.0",
+        server_port = 7860
+    )
+
+
+
+
+
+
+# import gradio as gr
+# import cv2
+# import torch
+# import numpy as np
+# import matplotlib.pyplot as plt
+# from celldetection import fetch_model, to_tensor
+
+# # ✅ Load the model
+# device = 'cpu'
+# model = fetch_model('ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c').to(device).eval()
+
+# # ✅ Inference function
+# def segment(image):
+#     img_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
+#     x = to_tensor(img_rgb, transpose=True, device=device, dtype=torch.float32)[None]
+
+#     with torch.no_grad():
+#         output = model(x)
+
+#     contours = output['contours'][0]
+#     original = (img_rgb * 255).astype(np.uint8).copy()
+#     segmented = original.copy()
+
+#     for contour in contours:
+#         contour = np.array(contour.cpu(), dtype=np.int32)
+#         cv2.drawContours(segmented, [contour], -1, (255, 0, 0), 2)
+
+#     h, w, c = original.shape
+#     gap = 60
+#     canvas = np.zeros((h, w * 2 + gap, c), dtype=np.uint8)
+#     canvas[:, :w, :] = original
+#     canvas[:, w + gap:, :] = segmented
+
+#     return cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR)
+
+# # ✅ Example images list
+# examples = [
+#     ["1.png"],
+#     ["2.png"],
+#     ["3.png"]
+# ]
+
+# # ✅ Launch the Gradio interface
+# gr.Interface(
+#     fn=segment,
+#     inputs=gr.Image(type="numpy"),
+#     outputs="image",
+#     title="Cell Segmentation Demo (FZJ-INM1)",
+#     description="Upload a microscopy image to see side-by-side segmentation.",
+#     examples=examples
+# ).launch()

flagged/log.csv

@@ -0,0 +1,2 @@
+image,output,flag,username,timestamp
+/Users/waqas/Documents/mlbench-workingspace/hikmat/cell-segmentation-FZJ-INM1-BDA/flagged/image/0258f8ed5ac723a71bb25be9a65d2efb4089573e/tmpubuvjgdc.png,/Users/waqas/Documents/mlbench-workingspace/hikmat/cell-segmentation-FZJ-INM1-BDA/flagged/output/c9a65caf91e2f56068a0133a03aca90edec7f722/tmpa9w_87zc.png,,,2026-01-16 12:04:26.300121

requirements.txt

@@ -1,6 +1,7 @@
-gradio
 opencv-python
 celldetection
 torch
-numpy
 matplotlib
+numpy<2
+gradio==3.50.2 
+gradio-client==0.6.1