update app.py

app.py

@@ -1,7 +1,130 @@
+# 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
+#     # 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
+    
+#     # 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 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.queue()
+# iface.launch(share=True)
+
+
+
 import gradio as gr
 from ultralytics import YOLO
+import torch 
 import os
-import torch
 
 # --- DOCUMENTATION STRINGS (Coin Detector App) ---
 
@@ -12,7 +135,8 @@ This application uses a trained YOLO model to automatically detect coins in an i
 
 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.
+3.  **Run:** Click the **"Detect Coins"** button.
+4.  **Review:** The output image will show bounding boxes around all detections that meet or exceed the set threshold.
 """
 
 GUIDELINE_INPUT = """
@@ -36,26 +160,22 @@ The output is a single image component displaying the **Annotated Frame**.
 """
 
 # 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
-    # Note: Using verbose=False to keep the interface clean during prediction
+    # Using verbose=False to suppress unnecessary console output during inference
     results = model(img, verbose=False) 
     
-    # Filter predictions based on the confidence threshold
-    # The results[0].boxes.data contains the detection results, including confidence scores
-    
     # 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.
-    
+    # Filter predictions based on the confidence threshold
     filtered_data = [box.cpu() for box in results[0].boxes.data if box[4] >= confidence_threshold]
     
     if filtered_data:
+        # Stack the filtered tensors back into a single tensor
         filtered_tensor = torch.stack(filtered_data)
         
-        # Create a deep copy of the original results object to manipulate its boxes data
+        # Create a results object to plot only the filtered boxes
         filtered_results = results[0].cpu()
         filtered_results.boxes.data = filtered_tensor
         
@@ -85,17 +205,14 @@ with gr.Blocks(title="Coin Detector") as iface:
 
     # 2. Input/Output Layout
     with gr.Row():
-        with gr.Column(scale=2):
-            gr.Markdown("## Step 1: Upload an Image ")
+        with gr.Column(scale=1):
             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")
+        with gr.Column(scale=2):
+            output_img = gr.Image(label="Detected Coins (Annotated Frame)")
+
 
     # 3. Example Data (if available, added here for completeness)
     # Note: Since no examples were provided, this is commented out or left as placeholders.
@@ -108,7 +225,7 @@ with gr.Blocks(title="Coin Detector") as iface:
         cache_examples=False
     )
 
-    # 4. Event Handler
+    # 3. Event Handler
     submit_btn.click(
         fn=predict,
         inputs=[input_img, confidence_slider],