Spaces:
Sleeping
Sleeping
File size: 3,438 Bytes
f7ad21e b99fdee f7ad21e |
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 |
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()
|