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5f70f3c ac11aa7 5f70f3c ac11aa7 5f70f3c | 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 | # Import libraries
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import gradio as gr
import joblib
# Step 1: Generate or Load Sample Data
np.random.seed(42)
data = pd.DataFrame({
"Pressure": np.random.randint(50, 200, 200),
"Temperature": np.random.randint(300, 700, 200),
"Material_Type": np.random.randint(1, 5, 200),
"Defect_Type": np.random.choice([0, 1, 2], 200, p=[0.6, 0.3, 0.1])
})
# Splitting data
X = data[["Pressure", "Temperature", "Material_Type"]]
y = data["Defect_Type"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Step 2: Train the Model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Save the model
joblib.dump(model, "defect_model.pkl")
# Step 3: Test the Model
y_pred = model.predict(X_test)
print(f"Model Accuracy: {accuracy_score(y_test, y_pred):.2f}")
# Step 4: Create a Gradio Interface
def predict_defect(pressure, temperature, material_type):
# Load the model
loaded_model = joblib.load("defect_model.pkl")
# Predict
input_data = np.array([[pressure, temperature, material_type]])
prediction = loaded_model.predict(input_data)[0]
defect_map = {0: "No Defect", 1: "Crack", 2: "Wrinkle"}
return defect_map[prediction]
# Gradio Interface
interface = gr.Interface(
fn=predict_defect,
inputs=[
gr.Slider(minimum=50, maximum=200, step=1, label="Pressure"),
gr.Slider(minimum=300, maximum=700, step=1, label="Temperature"),
gr.Dropdown(choices=["1", "2", "3", "4"], label="Material Type")
],
outputs=gr.Textbox(label="Predicted Defect"),
title="Defect Prediction Model",
description="Predicts defect type based on input features: Pressure, Temperature, and Material Type."
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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