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app.py

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+import warnings
+warnings.filterwarnings("ignore")
+import pickle
+import pandas as pd
+import numpy as np
+import gradio as gr
+import random
+
+def import_model():
+    model_names = ['random_forest', 'xgboost', 'decision_tree']
+    loaded_models = tuple()
+    for model_name in model_names:
+        with open(f'models/{model_name}.pkl', 'rb') as file:
+            model = pickle.load(file)
+            loaded_models += (model,)
+    return loaded_models
+
+def predict_liquid_rate(*input):
+    input_list = list(input)
+    inp_arr = np.array(input_list[:-1]).reshape(1, -1)
+    random_forest_model, xgboost_model, decision_tree_model = import_model()
+    model_selection = input_list[-1]
+    print(model_selection)
+
+    result = {}
+    model_names = {
+        'XGBoost': 'XGBoost Oil Rate',
+        'Random Forest': 'Random Forest Oil Rate',
+        'Decision Tree': 'Decision Tree Oil Rate',
+        'Prophet Model': 'Prophet Model Oil Rate'
+    }
+    xg_output = ''
+    dt_output = ''
+    rf_output = ''
+    for choice in model_selection:
+        if choice == 'XGBoost':
+            xg_pred = xgboost_model.predict(inp_arr)
+            result[choice] = xg_pred[0]
+            xg_output = f"{model_names['XGBoost']}: {result['XGBoost']:.2f} Bbls/day"
+        elif choice == 'Decision Tree':
+            dt_pred = decision_tree_model.predict(inp_arr)
+            result[choice] = dt_pred[0]
+            dt_output = f"{model_names['Decision Tree']}: {result['Decision Tree']:.2f} Bbls/day"
+        elif choice == 'Random Forest':
+            rf_pred = random_forest_model.predict(inp_arr)
+            result[choice] = rf_pred[0]
+            rf_output = f"{model_names['Random Forest']}: {result['Random Forest']:.2f} Bbls/day"
+
+    return xg_output, dt_output, rf_output
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """
+    # Oil Rate Prediction
+    Use this table as Reference for Last Well test data.
+    """)
+    with gr.Column():
+        with gr.Box():
+            frame_output = gr.Dataframe(
+                value=[['2022-12-23', 32, 1000, 280, 0.45,  775.12]],
+                headers=['Date', 'Choke', 'FTHP', 'FLP', 'BS&W', 'OilRate'],
+                datatype=["str", "number", "number", "number",  "number", "number"],
+                )
+
+        gr.Markdown(
+        """    Use the different input slider to select new welltest information
+        """)
+        with gr.Box():
+            choke = gr.Slider(minimum=0, maximum=100, value=32, step=2, label="Choke Size (1/64\")", interactive=True)
+            fthp = gr.Slider(minimum=500, maximum=5000, step=1, value=1000, label="Tubing Head Pressure (FTHP)(psi)", interactive=True)
+            flp = gr.Slider(minimum=0, maximum=5000, step=1, value=280,label="Flow Line Pressure (FLP)(psi)", interactive=True)
+            bsw = gr.Slider(minimum=0, maximum=100, value=0.45, label="Basic Sediment and Water (BS&W)(%)", interactive=True)
+
+        gr.Markdown(
+        """    Use the different trained models to perform Oil rate prediction
+        """)
+        # Output Controls
+        with gr.Column():
+            select_model = gr.CheckboxGroup(choices=["Random Forest", "XGBoost", "Decision Tree"], value='XGBoost', label="Select Model", info="Select Model to make prediction", interactive=True)
+            btn_predict = gr.Button("Test Prediction")
+            xg_output = gr.Label(label="XGBoost model")
+            dt_output = gr.Label(label="Decision Tree")
+            rf_output = gr.Label(label="Random Forest")
+
+
+    input_items = [choke, fthp, flp, bsw, select_model]
+    btn_predict.click(fn=predict_liquid_rate, inputs=input_items, outputs=[xg_output,dt_output,rf_output])
+    #gr.describe()
+    
+demo.launch(debug=True, share=True)