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import gradio as gr
import pickle
import pandas as pd
import numpy as np
# Load the model and preprocessor
model = pickle.load(open('model.pkl', 'rb'))
preprocessor = pickle.load(open('preprocess_pipeline.pkl', 'rb'))
def call_gradio(Sex, Equipment, Age, BodyweightKg, BestSquatKg, Bestbenchkg):
# Convert inputs to appropriate data types
Age = float(Age)
BodyweightKg = float(BodyweightKg)
BestSquatKg = float(BestSquatKg)
Bestbenchkg = float(Bestbenchkg)
# Create a DataFrame with the input data
df_x = pd.DataFrame({
'Sex': [Sex],
'Equipment': [Equipment],
'Age': [Age],
'BodyweightKg': [BodyweightKg],
'BestSquatKg': [BestSquatKg],
'Bestbench(kg)': [Bestbenchkg],
})
# Define the categorical and numerical feature lists
categorical_features = ['Sex', 'Equipment']
numerical_features = df_x.drop(categorical_features, axis=1).columns.tolist()
# Ensure that the preprocessor is fitted
if not hasattr(preprocessor, 'transformers_'):
raise RuntimeError("The preprocessor is not fitted yet. Fit the preprocessor before calling this function.")
# Transform the data using the preprocessor
X_processed = preprocessor.transform(df_x)
# Access the OneHotEncoder directly to get feature names
ohe = preprocessor.named_transformers_['cat'].named_steps['onehot']
cat_feature_names = ohe.get_feature_names_out(categorical_features)
# Combine numerical features and one-hot encoded feature names
all_feature_names = numerical_features + list(cat_feature_names)
# Create a DataFrame with processed features
X_processed_df = pd.DataFrame(X_processed, columns=all_feature_names)
# Predict using the model
y_pred = model.predict(X_processed_df)
max_kg = int(y_pred[0])
return max_kg
# Define Gradio inputs and outputs
sex_dropdown = gr.Dropdown(choices=['M', 'F'], label="Sex", info="Select Male or Female")
equipment_dropdown = gr.Dropdown(choices=['Raw', 'Wraps', 'Single-ply', 'Multi-ply'], label="Equipment", info="Select the equipment used for the competition.")
age_textbox = gr.Textbox(lines=1, label="Age", info="Enter your Age")
bodyweight_kg_textbox = gr.Textbox(lines=1, label="BodyweightKg", info="Enter your Bodyweight in Kg")
best_squat_kg_textbox = gr.Textbox(lines=1, label="BestSquatKg", info="Enter your Best Squat in Kg")
best_bench_kg_textbox = gr.Textbox(lines=1, label="BestbenchKg", info="Enter your Best Bench in Kg")
# Custom description with image and footer
description = """
<div style='text-align: center;'>
<h1 style='font-size: 50px;'>PowerLift Muscle Map</h1>
<p>Use this model to estimate your best Deadlift (kg) based on your selected features. Input your details and see the predicted weight (kg) you could lift.</p>
<p><strong>Output:</strong> Estimated Best Deadlift (kg)</p>
<br>
<p style='font-size: 10px; color: #555;'>❤️ PDS</p>
</div>
"""
# Create and launch Gradio interface
iface = gr.Interface(
fn=call_gradio,
inputs=[sex_dropdown, equipment_dropdown, age_textbox, bodyweight_kg_textbox, best_squat_kg_textbox, best_bench_kg_textbox],
outputs="number",
description=description,
)
iface.launch() |