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.gitattributes

@@ -33,3 +33,4 @@ 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
+examples/pandesal.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.DS_Store
+__pycache__/

README.md

@@ -1,12 +1,6 @@
 ---
-title: Bread Detector Gradio
-emoji: 👁
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.40.1
+title: Bread-Detector-Gradio
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.39.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import cv2 as cv2
+import pandas as pd
+from sahi.prediction import ObjectPrediction
+from sahi.utils.cv import visualize_object_predictions, read_image
+from ultralyticsplus import YOLO 
+
+# Gradio Theme
+theme = gr.themes.Soft(
+    primary_hue="yellow",
+    secondary_hue="blue",
+    neutral_hue="gray",
+    font=[gr.themes.GoogleFont('Inter'), 'ui-sans-serif', 'system-ui', 'sans-serif'],
+    font_mono=[gr.themes.GoogleFont('Inter'), 'ui-monospace', 'Consolas', 'monospace'],
+).set(
+    background_fill_primary='*neutral_100',
+)
+
+# Bread Prices
+bread_types = {
+    "baguette": {"name": "Baguette", "price": 108},
+    "binangkal": {"name": "Binangkal", "price": 11},
+    "bonete": {"name": "Bonete", "price": 8},
+    "cornbread": {"name": "Cornbread", "price": 55},
+    "croissant": {"name": "Croissant", "price": 75},
+    "ensaymada": {"name": "Ensaymada", "price": 14},
+    "flatbread": {"name": "Flatbread", "price": 17},
+    "kalihim": {"name": "Kalihim", "price": 15},
+    "monay": {"name": "Monay", "price": 6},
+    "pandesal": {"name": "Pandesal", "price": 3},
+    "sourdough": {"name": "Sourdough", "price": 150},
+    "spanish-bread": {"name": "Spanish Bread", "price": 14},
+    "wheat-bread": {"name": "Wheat Bread", "price": 8},
+    "white-bread": {"name": "White Bread", "price": 4},
+    "whole-grain-bread": {"name": "Whole Grain Bread", "price": 10},
+}
+
+# Instantiate the model
+model = YOLO('best.pt')
+
+# Bread Prediction function
+def detect_bread(image):
+    results = model.predict(image, conf=0.4)
+    result = results[0]
+    object_prediction_list = []
+    
+    # Image Output function
+    for box in result.boxes:
+        bbox = box.xyxy[0].tolist()
+        score = round(box.conf[0].item(), 2)
+        category_id = box.cls[0]
+        category_name = result.names[box.cls[0].item()]
+
+        object_prediction = ObjectPrediction(
+            bbox=bbox,
+            category_id=int(category_id),
+            score=score,
+            category_name=category_name,
+        )
+        object_prediction_list.append(object_prediction)
+
+    image = read_image(image)
+    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+
+    # Receipt Output function
+    detected_classes = []
+    detected_items = []
+    counts = {}     # Dictionary to store bread type counts
+    
+    for cls in result.boxes.cls:                                # Stores all detected classes in the list
+        detected_classes.append(result.names[int(cls)])         
+
+    for item_class in detected_classes:                         # Counts the quantity of each class
+        counts[item_class] = counts.get(item_class, 0) + 1      
+
+    for item_class, count in counts.items():                    # Gets the name and price of each class
+        bread_info = bread_types.get(item_class, {})
+        item_name = bread_info.get("name", "Unknown Bread")
+        price = bread_info.get("price", 0)
+        detected_items.append({"item": item_name, "quantity": count, "price": price})
+
+    total_amount = sum(item["quantity"] * item["price"] for item in detected_items)
+
+    # Creates the receipt dictionary 
+    data = {"Item": [], "Quantity": [], "Price": [], "Amount": []}
+
+    for item_info in detected_items:
+        item = item_info["item"]
+        quantity = item_info["quantity"]
+        price = item_info["price"]
+        total_item_amount = quantity * price
+
+        data["Item"].append(item)
+        data["Quantity"].append(quantity)
+        data["Price"].append(price)
+        data["Amount"].append(total_item_amount)
+
+    # Appends the last row of the dataframe for the total amount
+    data["Item"].append("TOTAL")
+    data["Quantity"].append("")
+    data["Price"].append("")
+    data["Amount"].append(total_amount)
+
+    df = pd.DataFrame(data)
+
+    return output_image['image'], df
+
+# Export to CSV function
+def export_csv(df):
+    df.to_csv("receipt.csv", index=False)
+    return gr.File.update(value="receipt.csv", visible=True)
+
+# Export to JSON function
+def export_json(df):
+    df.to_json("receipt.json")
+    return gr.File.update(value="receipt.json", visible=True)
+
+# Gradio Interface
+with gr.Blocks(theme=theme) as demo:
+
+    gr.Markdown("# Bread Detector w/ POS")
+    gr.Markdown("An application that detects different types of bread and calculates the total price.")
+    gr.Markdown("**Bread types include:** baguette, binangkal, bonete, cornbread, croissant, ensaymada, flatbread, kalihim, monay, pandesal, sourdough, spanish bread, wheat bread, white bread, and whole grain bread.")
+
+    with gr.Row():
+        with gr.Column():
+            fn = detect_bread
+            img_input = gr.Image(type="filepath", label="Input Image")
+            #img_input = gr.Files(file_types=["filepath"], label="Input Image") 
+            detect_btn = gr.Button(variant="primary", value="Detect")
+
+        with gr.Column():
+            img_output = gr.Image(type="filepath", label='Output Image')
+            receipt_output = gr.Dataframe(
+                    headers=["Item", "Quantity", "Price", "Amount"],
+                    datatype=["str", "number", "number", "number"],
+                    label="Receipt",
+                    interactive=False,
+                )
+            with gr.Row():
+                clear_btn = gr.ClearButton([img_input, img_output, receipt_output])
+                export_btn = gr.Button(variant="primary", value="Export as CSV")
+                export_json_btn = gr.Button(variant="primary", value="Export as JSON")
+            with gr.Row():
+                csv = gr.File(interactive=False, visible=False)
+                
+    with gr.Row():
+        gr.Examples(
+            examples = ["examples/bonete.jpg", "examples/pandesal.jpg", "examples/croissant_baguette.jpg", "examples/slices.png"],
+            inputs = img_input,
+        )
+
+    detect_btn.click(detect_bread, inputs=img_input, outputs=[img_output, receipt_output])
+    export_btn.click(export_csv, receipt_output, csv)
+    export_json_btn.click(export_json, receipt_output, csv)
+
+demo.queue()
+demo.launch()

requirements.txt

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+gradio==3.40.1
+opencv_python==4.8.0.74
+pandas==2.0.3
+sahi==0.11.14
+ultralyticsplus==0.0.28

weights/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3ec84ed70c3f5b739b1aa271b767d6225fb85c0922aa8fb38b01023de24884fc
+size 22519224