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b6193b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | """
Gradio Web Interface for Milk Spoilage Classification
This app provides an interactive web interface for predicting
milk spoilage type based on microbial count data.
"""
import gradio as gr
import joblib
import numpy as np
# Load the trained model
model = joblib.load("model.joblib")
# Feature information for the UI
FEATURE_INFO = {
"SPC_D7": ("Standard Plate Count - Day 7", "log CFU/mL", 0.0, 10.0, 4.0),
"SPC_D14": ("Standard Plate Count - Day 14", "log CFU/mL", 0.0, 10.0, 5.0),
"SPC_D21": ("Standard Plate Count - Day 21", "log CFU/mL", 0.0, 10.0, 6.0),
"TGN_D7": ("Total Gram-Negative - Day 7", "log CFU/mL", 0.0, 10.0, 3.0),
"TGN_D14": ("Total Gram-Negative - Day 14", "log CFU/mL", 0.0, 10.0, 4.0),
"TGN_D21": ("Total Gram-Negative - Day 21", "log CFU/mL", 0.0, 10.0, 5.0),
}
# Class descriptions
CLASS_DESCRIPTIONS = {
"PPC": "Post-Pasteurization Contamination - Bacteria introduced after pasteurization",
"no spoilage": "No significant spoilage detected in the sample",
"spore spoilage": "Spoilage caused by spore-forming bacteria"
}
def predict_spoilage(spc_d7, spc_d14, spc_d21, tgn_d7, tgn_d14, tgn_d21):
"""
Predict milk spoilage type based on microbial counts.
Args:
spc_d7: Standard Plate Count at Day 7
spc_d14: Standard Plate Count at Day 14
spc_d21: Standard Plate Count at Day 21
tgn_d7: Total Gram-Negative count at Day 7
tgn_d14: Total Gram-Negative count at Day 14
tgn_d21: Total Gram-Negative count at Day 21
Returns:
Dictionary of class probabilities for Gradio Label component
"""
# Prepare input features
features = np.array([[spc_d7, spc_d14, spc_d21, tgn_d7, tgn_d14, tgn_d21]])
# Get prediction and probabilities
prediction = model.predict(features)[0]
probabilities = model.predict_proba(features)[0]
# Create probability dictionary for Gradio Label
prob_dict = {
cls: float(prob)
for cls, prob in zip(model.classes_, probabilities)
}
return prob_dict
def create_interface():
"""Create and configure the Gradio interface."""
# Custom CSS for styling
custom_css = """
.gradio-container {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
.feature-group {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
border-radius: 10px;
padding: 15px;
margin: 10px 0;
}
"""
with gr.Blocks(
title="Milk Spoilage Classifier",
theme=gr.themes.Soft(
primary_hue="indigo",
secondary_hue="purple",
),
css=custom_css
) as demo:
# Header
gr.Markdown(
"""
# 🥛 Milk Spoilage Classification Model
Predict milk spoilage type based on microbial count data measured at different time points.
Enter the Standard Plate Count (SPC) and Total Gram-Negative (TGN) values below.
"""
)
with gr.Row():
# Input Section
with gr.Column(scale=1):
gr.Markdown("### 📊 Standard Plate Count (SPC)")
gr.Markdown("*Total bacterial count in log CFU/mL*")
spc_d7 = gr.Number(
label="Day 7",
value=4.0,
minimum=0.0,
maximum=10.0,
info="SPC measurement at day 7"
)
spc_d14 = gr.Number(
label="Day 14",
value=5.0,
minimum=0.0,
maximum=10.0,
info="SPC measurement at day 14"
)
spc_d21 = gr.Number(
label="Day 21",
value=6.0,
minimum=0.0,
maximum=10.0,
info="SPC measurement at day 21"
)
with gr.Column(scale=1):
gr.Markdown("### 🦠 Total Gram-Negative (TGN)")
gr.Markdown("*Gram-negative bacterial count in log CFU/mL*")
tgn_d7 = gr.Number(
label="Day 7",
value=3.0,
minimum=0.0,
maximum=10.0,
info="TGN measurement at day 7"
)
tgn_d14 = gr.Number(
label="Day 14",
value=4.0,
minimum=0.0,
maximum=10.0,
info="TGN measurement at day 14"
)
tgn_d21 = gr.Number(
label="Day 21",
value=5.0,
minimum=0.0,
maximum=10.0,
info="TGN measurement at day 21"
)
# Predict button
predict_btn = gr.Button("🔬 Classify Spoilage Type", variant="primary", size="lg")
# Output Section
gr.Markdown("### 📋 Prediction Results")
output_label = gr.Label(
label="Spoilage Classification",
num_top_classes=3
)
# Connect the prediction function
predict_btn.click(
fn=predict_spoilage,
inputs=[spc_d7, spc_d14, spc_d21, tgn_d7, tgn_d14, tgn_d21],
outputs=output_label
)
# Also trigger on any input change
for input_component in [spc_d7, spc_d14, spc_d21, tgn_d7, tgn_d14, tgn_d21]:
input_component.change(
fn=predict_spoilage,
inputs=[spc_d7, spc_d14, spc_d21, tgn_d7, tgn_d14, tgn_d21],
outputs=output_label
)
# Information Section
gr.Markdown(
"""
---
### ℹ️ About the Classes
| Class | Description |
|-------|-------------|
| **PPC** | Post-Pasteurization Contamination - Bacteria introduced after pasteurization process |
| **no spoilage** | No significant spoilage detected in the sample |
| **spore spoilage** | Spoilage caused by spore-forming bacteria that survive pasteurization |
---
### 📖 How to Use
1. Enter the microbial count values (in log CFU/mL) for each time point
2. Click "Classify Spoilage Type" or wait for automatic prediction
3. View the predicted spoilage category and confidence scores
---
*Model: Random Forest Classifier trained on milk quality data*
"""
)
return demo
# Create and launch the interface
if __name__ == "__main__":
demo = create_interface()
demo.launch()
|