Create app.py

app.py

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+import os
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+import gradio as gr
+
+# Read the dataset
+data = pd.read_csv('Well_Rates.csv')
+
+# Define input features and target variable
+input_features = ['Qwater', 'Qgas', 'BHP', 'WHP', 'WHT', 'Tsep', 'Psep', 'Choke_in']
+target_variable = 'Qoil'
+
+# Split the dataset into training and testing sets
+X = data[input_features]
+y = data[target_variable]
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train the random forest regression model
+rf = RandomForestRegressor(n_estimators=100, random_state=42)
+rf.fit(X_train, y_train)
+
+# Fine-tune the model
+rf_tuned = RandomForestRegressor(n_estimators=200, max_depth=10, random_state=42)
+rf_tuned.fit(X_train, y_train)
+
+def predict_qoil(Qwater, Qgas, BHP, WHP, WHT, Tsep, Psep, Choke_in):
+    new_input = [[Qwater, Qgas, BHP, WHP, WHT, Tsep, Psep, Choke_in]]
+    predicted_qoil = rf_tuned.predict(new_input)
+    return predicted_qoil[0]
+
+iface = gr.Interface(
+    fn=predict_qoil, 
+    inputs=["number", "number", "number", "number", "number", "number", "number", "number"], 
+    outputs="number",
+    interpretation="default")
+iface.launch()