refine the gradio 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
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+venv

app.py

@@ -1,33 +1,120 @@
 import gradio as gr
 from ultralytics import YOLO
+import os
+import torch
+
+# --- DOCUMENTATION STRINGS (Coin Detector App) ---
+
+GUIDELINE_SETUP = """
+## 1. Quick Start Guide: Detection and Filtering
+
+This application uses a trained YOLO model to automatically detect coins in an image and allows you to filter the results based on detection confidence.
+
+1.  **Upload Image:** Upload the image you want to analyze in the 'Input Image' box.
+2.  **Adjust Threshold:** Use the 'Confidence Threshold' slider to set the minimum certainty required for a coin to be displayed.
+3.  **Review:** The output image will show bounding boxes around all detections that meet or exceed the set threshold.
+"""
+
+GUIDELINE_INPUT = """
+## 2. Expected Inputs and Parameters
+
+| Input Field | Purpose | Requirement |
+| :--- | :--- | :--- |
+| **Input Image** | The photograph containing the coins you wish to detect. | Must be an image file (e.g., JPG, PNG). |
+| **Confidence Threshold** | Filters the model's predictions. Only detections with a confidence score equal to or higher than this value will be shown. | Slider range: 0.0 (least strict) to 1.0 (most strict). Default is 0.5. |
+
+**Tip:** If you see too many false positives (non-coins being detected), raise the threshold. If the model misses coins you know are there, try lowering the threshold.
+"""
+
+GUIDELINE_OUTPUT = """
+## 3. Expected Outputs (Annotated Image)
+
+The output is a single image component displaying the **Annotated Frame**.
+
+*   **Content:** This image is the original input image with colored bounding boxes drawn around every coin detected by the model that passed the `Confidence Threshold` filter.
+*   **Bounding Boxes:** Each box confirms a coin detection and is usually accompanied by a label (e.g., 'coin') and the confidence score (e.g., 0.95).
+"""
 
 # Load the YOLO model
+# NOTE: The model file 'best1.pt' must exist in the same directory or accessible path.
 model = YOLO('best1.pt')
 
 def predict(img, confidence_threshold):
     # Perform inference
-    results = model(img)
+    # Note: Using verbose=False to keep the interface clean during prediction
+    results = model(img, verbose=False) 
     
     # Filter predictions based on the confidence threshold
     # The results[0].boxes.data contains the detection results, including confidence scores
-    filtered_boxes = [box for box in results[0].boxes.data if box[4] >= confidence_threshold]
     
-    # Plot the results (with the filtered detections)
-    annotated_frame = results[0].plot(labels=filtered_boxes)
+    # We filter the bounding boxes data array based on the confidence score (index 4)
+    # Then we must convert the filtered list back to a tensor format expected by the plotting function.
     
+    filtered_data = [box.cpu() for box in results[0].boxes.data if box[4] >= confidence_threshold]
+    
+    if filtered_data:
+        filtered_tensor = torch.stack(filtered_data)
+        
+        # Create a deep copy of the original results object to manipulate its boxes data
+        filtered_results = results[0].cpu()
+        filtered_results.boxes.data = filtered_tensor
+        
+        # Plot the results using the filtered results object
+        annotated_frame = filtered_results.plot()
+    else:
+        # If no coins pass the filter, plot the original image without boxes
+        annotated_frame = results[0].plot()
+        
     return annotated_frame
 
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Image(label="Input Image", type="filepath"),
-        gr.Slider(minimum=0, maximum=1, value=0.5, label="Confidence Threshold", step=0.01)
-    ],
-    outputs="image",
-    title="Coin Detector",
-    description="Upload an image to detect coins. Adjust the confidence threshold to filter results."
-)
+# Create the Gradio interface using gr.Blocks to allow for documentation placement
+with gr.Blocks(title="Coin Detector") as iface:
+    
+    gr.Markdown("# Coin Detector")
+    gr.Markdown("Upload an image to detect coins. Adjust the confidence threshold to filter results.")
+    
+    # 1. Guidelines Section
+    with gr.Accordion("User Guidelines and Documentation", open=False):
+        gr.Markdown(GUIDELINE_SETUP)
+        gr.Markdown("---")
+        gr.Markdown(GUIDELINE_INPUT)
+        gr.Markdown("---")
+        gr.Markdown(GUIDELINE_OUTPUT)
+        
+    gr.Markdown("---")
+
+    # 2. Input/Output Layout
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown("## Step 1: Upload an Image ")
+            input_img = gr.Image(label="Input Image", type="filepath")
+            gr.Markdown("## Step 2: Adjest Confidence Threshold (Optional) ")
+            confidence_slider = gr.Slider(minimum=0, maximum=1, value=0.5, label="Confidence Threshold", step=0.01)
+            gr.Markdown("## Step 3: Click Detect Coins ")
+            submit_btn = gr.Button("Detect Coins", variant="primary")
+            
+        with gr.Column(scale=1):
+            gr.Markdown("## Result ")
+            output_img = gr.Image(label="Output Image")
+
+    # 3. Example Data (if available, added here for completeness)
+    # Note: Since no examples were provided, this is commented out or left as placeholders.
+    gr.Markdown("## Examples ")
+    gr.Examples(
+        examples=[["./sample_data/coin.jpeg", 0.5], ["./sample_data/Test21.png", 0.4]],
+        inputs=[input_img, confidence_slider],
+        outputs=output_img,
+        fn=predict,
+        cache_examples=False
+    )
+
+    # 4. Event Handler
+    submit_btn.click(
+        fn=predict,
+        inputs=[input_img, confidence_slider],
+        outputs=output_img
+    )
 
 # Launch the Gradio interface
-iface.launch(share=True)
+iface.queue()
+iface.launch(share=True)

requirements.txt

@@ -1,4 +1,5 @@
 ultralytics
-gradio
-numpy
-pillow
+pillow
+numpy<2
+gradio==3.50.2 
+gradio-client==0.6.1