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| import gradio as gr | |
| import joblib | |
| import pandas as pd | |
| import xgboost as xgb | |
| import numpy as np | |
| from sklearn.preprocessing import LabelEncoder | |
| # Load the trained model | |
| model = joblib.load(r"mutual_fund_model.pkl") | |
| def predict_mutual_fund(min_sip, min_lumpsum, expense_ratio, fund_size_cr, fund_age_yr, | |
| fund_manager, sortino, alpha, sd, amc_name, rating, | |
| category, sub_category): | |
| # Add the missing features with default values | |
| input_data = pd.DataFrame({ | |
| 'scheme_name': ['N/A'], # Default placeholder value | |
| 'min_sip': [min_sip], | |
| 'min_lumpsum': [min_lumpsum], | |
| 'expense_ratio': [expense_ratio], | |
| 'fund_size_cr': [fund_size_cr], | |
| 'fund_age_yr': [fund_age_yr], | |
| 'fund_manager': [fund_manager], | |
| 'sortino': [sortino], | |
| 'alpha': [alpha], | |
| 'sd': [sd], | |
| 'beta': [0.0], # Default placeholder value | |
| 'sharpe': [0.0], # Default placeholder value | |
| 'risk_level': ['low'], # Default placeholder value | |
| 'amc_name': [amc_name], | |
| 'rating': [rating], | |
| 'category': [category], | |
| 'sub_category': [sub_category], | |
| 'returns_3yr': [0.0], # Default placeholder value | |
| 'returns_5yr': [0.0] # Default placeholder value | |
| }) | |
| # Convert categorical columns to numeric | |
| label_encoder = LabelEncoder() | |
| input_data['scheme_name'] = label_encoder.fit_transform(input_data['scheme_name']) | |
| input_data['risk_level'] = label_encoder.fit_transform(input_data['risk_level']) | |
| # Use the model to make predictions | |
| predictions = model.predict(input_data) | |
| # Handle multi-dimensional predictions | |
| if isinstance(predictions, np.ndarray) and predictions.ndim == 2: | |
| rounded_predictions = predictions[0] # Take the first row (single input case) | |
| rounded_predictions = [round(float(val), 4) for val in rounded_predictions] | |
| else: | |
| raise ValueError("Unexpected output from model. Expected a 2D array.") | |
| # Print the result (for debugging purposes) | |
| print("Rounded Predictions:", rounded_predictions) | |
| # Return the rounded predictions in the desired format | |
| return { | |
| '1M Return': rounded_predictions[0], | |
| '3M Return': rounded_predictions[1], | |
| '6M Return': rounded_predictions[2], | |
| '1Y Return': rounded_predictions[3] | |
| } | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=predict_mutual_fund, | |
| inputs=[ | |
| gr.Number(label="Minimum SIP Amount (₹)"), | |
| gr.Number(label="Minimum Lump Sum Amount (₹)"), | |
| gr.Number(label="Expense Ratio (%)"), | |
| gr.Number(label="Fund Size (in Cr)"), | |
| gr.Number(label="Fund Age (in Years)"), | |
| gr.Number(label="Fund Manager ID"), | |
| gr.Number(label="Sortino Ratio"), | |
| gr.Number(label="Alpha"), | |
| gr.Number(label="Standard Deviation (SD)"), | |
| gr.Number(label="AMC Name ID"), | |
| gr.Number(label="Rating (1-5)"), | |
| gr.Number(label="Category ID"), | |
| gr.Number(label="Sub-Category ID") | |
| ], | |
| outputs=[ | |
| gr.JSON(label="Predicted Returns (1M, 3M, 6M, 1Y)") | |
| ], | |
| live=False, | |
| title="Mutual Fund Return Prediction", | |
| description="Enter the details of the mutual fund to predict its returns over 1M, 3M, 6M, and 1Y." | |
| ) | |
| # Launch the interface | |
| iface.launch() | |