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import gradio as gr
import pandas as pd
import xgboost as xgb
import json
def train_and_predict(training_json, new_data_json, target_col):
try:
# 1. Parse Training Data
train_data = json.loads(training_json)
df_train = pd.DataFrame(train_data)
if target_col not in df_train.columns:
return {"error": f"Target column '{target_col}' not found in training data"}
# Split into X (Features) and y (Target)
X_train = df_train.drop(columns=[target_col])
y_train = df_train[target_col]
# 2. Train XGBoost (runs on server CPU)
model = xgb.XGBRegressor()
model.fit(X_train, y_train)
# 3. Parse New Data for Prediction
new_data = json.loads(new_data_json)
df_new = pd.DataFrame(new_data)
# Ensure columns match training data order
# (Align columns to handle JSON key scrambling)
existing_cols = [c for c in X_train.columns if c in df_new.columns]
df_new = df_new[existing_cols]
# 4. Predict
predictions = model.predict(df_new)
# 5. Format Output
df_new['prediction'] = predictions.astype(float)
return df_new.to_json(orient='records')
except Exception as e:
return json.dumps({"error": str(e)})
# We use Interface to guarantee the '/predict' API name exists
demo = gr.Interface(
fn=train_and_predict,
inputs=[
gr.Textbox(label="Training Data (JSON)"),
gr.Textbox(label="New Data (JSON)"),
gr.Textbox(label="Target Column", value="reach")
],
outputs="json"
)
demo.launch()
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