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import gradio as gr
import joblib
import pandas as pd
import datasets
import json
import numpy as np
# Load the model
pipe = joblib.load("./model.pkl")
title = "Premium Amount Prediction"
description = "This model predicts the Premium Amount. Drag and drop any slice from the dataset or edit values as you wish in the dataframe component below."
# Load and prepare dataset
df = datasets.load_dataset("silvaKenpachi/mental_health")["train"].to_pandas()
df.dropna(axis=0, inplace=True)
# Load configuration
with open("./config.json") as f:
config_dict = json.load(f)
all_headers = config_dict["sklearn"]["columns"]
# Filter headers to only include those present in the dataset
headers = [col for col in all_headers if col in df.columns]
# Define input and output interfaces
#inputs = [gr.Dataframe(headers=headers, row_count=(2, "dynamic"), col_count=(len(headers), "fixed"), label="Input Data", interactive=True)]
inputs = [gr.Dataframe(headers=all_headers, row_count=("dynamic"), col_count=(len(all_headers), "fixed"), label="Input Data", interactive=True)]
outputs = [gr.Dataframe(row_count=("dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["Depression"])]
#def infer(inputs):
#data = pd.DataFrame(inputs, columns=headers)
#predictions = pipe.predict(data)
#return pd.DataFrame(predictions, columns=["Depression"])
#code to fix missing columns with na
#def infer(inputs):
#data = pd.DataFrame(inputs, columns=headers)
# Add missing columns with default values (e.g., 0)
#for col in all_headers:
#if col not in data.columns:
#data[col] = 0
# Ensure the order of columns matches the training data
#data = data[all_headers]
#predictions = pipe.predict(data)
#return pd.DataFrame(predictions, columns=["Depression"])
def infer(inputs):
data = pd.DataFrame(inputs, columns=headers)
# Replace empty strings with NaN
data = data.replace('', np.nan)
# Add missing columns with default values (e.g., 0)
for col in all_headers:
if col not in data.columns:
data[col] = 0
# Ensure the order of columns matches the training data
data = data[all_headers]
# Fill NaN values with default values (e.g., 0)
data = data.fillna(0)
# Convert all data to float
data = data.astype(float)
predictions = pipe.predict(data)
return pd.DataFrame(predictions, columns=["Depression"])
gr.Interface(
fn=infer,
inputs=inputs,
outputs=outputs,
title=title,
description=description,
examples=[df[headers].head(3).values.tolist()],
cache_examples=False
).launch(debug=True)
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