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import numpy as np
import pandas as pd
import joblib
# Load model and preprocessing objects
model = joblib.load("model.pkl")
scaler = joblib.load("scaler.pkl")
label_encoder = joblib.load("label_encoder.pkl")
# Define prediction function
def predict_class(COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI):
# Input as a DataFrame with correct feature names
input_df = pd.DataFrame([{
"COUNT": COUNT,
"COUNT_CV": COUNT_CV,
"STRENGTH": STRENGTH,
"CSP": CSP,
"U_PERCENT": U_PERCENT,
"THIN": THIN,
"THICK": THICK,
"NEPS": NEPS,
"IPI": IPI
}])
# Scale input
scaled_input = scaler.transform(input_df)
# Predict
pred_index = model.predict(scaled_input)[0]
# Decode class label
predicted_label = label_encoder.inverse_transform([pred_index])[0]
return predicted_label
# Build Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("## 🧵 Textile Mixing Classifier (Scikit-learn Model)")
gr.Markdown("Use the sliders to set values and click **Predict** to classify the textile mix.")
with gr.Column():
with gr.Row():
COUNT = gr.Slider(7, 50, step=0.5, label="COUNT")
COUNT_CV = gr.Slider(0.48, 1.87, step=0.1, label="COUNT_CV")
STRENGTH = gr.Slider(62.71, 384.2, step=0.1, label="STRENGTH")
with gr.Row():
CSP = gr.Slider(1500, 4755, step=1, label="CSP")
U_PERCENT = gr.Slider(6.38, 12.12, step=0.1, label="U_PERCENT")
THIN = gr.Slider(0.0, 19, step=0.1, label="THIN")
with gr.Row():
THICK = gr.Slider(2.0, 150, step=1, label="THICK")
NEPS = gr.Slider(1, 494, step=1, label="NEPS")
IPI = gr.Slider(6.0, 646, step=1, label="IPI")
output = gr.Textbox(label="Predicted Class")
btn = gr.Button("Predict")
btn.click(fn=predict_class,
inputs=[COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI],
outputs=output)
demo.launch()
# import tensorflow as tf
# import gradio as gr
# import numpy as np
# import joblib
# # Load your trained model
# model = joblib.load("mixing_prediction_model.pkl")
# # Prediction function
# def predict(COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI):
# features = np.array([[COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI]])
# # pred = model.predict(features)[0]
# # mixing_name = le.inverse_transform([pred])[0]
# prediction = model.predict(features)[0][0]
# return prediction
# # Gradio app with custom sliders
# with gr.Blocks() as demo:
# gr.Markdown("## 🧵 Textile Mixing Predictor (TensorFlow Model)")
# gr.Markdown("Adjust the sliders for each feature and get the predicted output.")
# with gr.Row():
# with gr.Column():
# COUNT = gr.Slider(minimum=7, maximum=50, step=0.5, label="COUNT")
# COUNT_CV = gr.Slider(minimum=0.48, maximum=1.87, step=0.1, label="COUNT_CV")
# STRENGTH = gr.Slider(minimum=62.71, maximum=384.2, step=0.1, label="STRENGTH")
# with gr.Column():
# CSP = gr.Slider(minimum=1500, maximum=4755, step=1, label="CSP")
# U_PERCENT = gr.Slider(minimum=6.38, maximum=12.12, step=0.1, label="U_PERCENT")
# THIN = gr.Slider(minimum=0.0, maximum=19, step=0.1, label="THIN")
# with gr.Column():
# THICK = gr.Slider(minimum=2.0, maximum=150, step=1, label="THICK")
# NEPS = gr.Slider(minimum=1, maximum=494, step=1, label="NEPS")
# IPI = gr.Slider(minimum=6.0, maximum=646, step=1, label="IPI")
# output = gr.Number(label="Predicted Output")
# predict_button = gr.Button("Predict")
# # Bind inputs to function
# predict_button.click(
# fn=predict,
# inputs=[COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI],
# outputs=output
# )
# demo.launch()
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