new comit

main.py

@@ -1,57 +0,0 @@
-#Example 5 Gradio Version
-# Example 4 Decision Treen Analysis
-# Example 2 rework on classification of flow regime type
-import pandas as pd
-import gradio as gr
-df = pd.read_csv("olga.tab" ,sep="\t")
-import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D
-plt.figure(figsize=(16,8))
-import numpy as np
-from sklearn.tree import DecisionTreeClassifier
-plt.rcParams['font.size'] = 16
-plt.style.use("seaborn-whitegrid")
-def rs(array):
-    return np.array(array).reshape(-1, 1)
-#drop empty values
-df.dropna(inplace=True)
-#Redcue to stable/non stable regime 1 = Unstable, 2
-df = df.dropna()
-df = df[ (df["QOST [STB/d]"]>= 0)  & (df["QGST [MMscf/d]"] >=0) ] 
-#create training values
-X_train = df[["QOST [STB/d]", "QGST [MMscf/d]"]]
-y_train = df["ID"]
-#initialize mode
-DT = DecisionTreeClassifier(max_depth=5)
-#train and predict
-DT.fit(X_train, y_train)
-y_pred = list(DT.predict(X_train))
-
-color_map = {1:"red", 2:"blue", 3:"green", 4:"magenta"}
-y_color_original = pd.Series(y_train).map(color_map)
-y_pred_color = pd.Series(y_pred).map(color_map)
-#create dual pltis plot
-#tcf = plt.tricontourf(df["QOST [STB/d]"], df["QGST [MMscf/d]"],y_pred , cmap="rainbow")
-#plt[0].scatter(df["QOST [STB/d]"], df["QGST [MMscf/d]"], color=y_color_original, s=100, alpha=0.5)
-
-#Create custom legend
-legend_items = []
-regime = ["Stratified", "Bubble", "Slug", "Annular"]
-for i in range(1,5):
-    #Create Custom Scatter Points
-    scatt = Line2D([0], [0], color=color_map[i],marker="o", markersize=15, lw=0,label=regime[i-1])
-    legend_items.append(scatt)
-#Setup Plot
-
-def predict_model(qost, qgst):
-    x_pred = pd.DataFrame({"QOST [STB/d]":[qost], "QGST [MMscf/d]":[qgst]})
-    _pred = DT.predict(x_pred)
-    plt.scatter(df["QOST [STB/d]"], df["QGST [MMscf/d]"], color=y_pred_color, s=100, alpha=0.5)
-    plt.scatter(x_pred["QOST [STB/d]"], x_pred["QGST [MMscf/d]"], color="black",marker="X", s=200, label="Your Point")
-    plt.xlabel("QOST") ;plt.ylabel("QGST")
-    plt.legend(True,handles=legend_items)
-    plt.title("Original Dataset"); plt.title("Predicted Dataset")
-    return plt, regime[ int(_pred[0])-1  ]
-interface = gr.Interface(predict_model, inputs=[gr.Number(label="QOil[bbl/day]", value=20000), gr.Number(label="QGas[mmscf/day]", value=100)], 
-                         outputs=[gr.Plot(label="Flow Map"),gr.Textbox(label="Identified Regime")])
-interface.launch()

requirements.txt

@@ -1,6 +1,6 @@
-gradio==4.10.0
-matplotlib==3.7.1
-matplotlib==3.7.0
-numpy==1.23.5
-pandas==1.5.3
-scikit_learn==1.2.1
+gradio
+matplotlib
+matplotlib
+numpy
+pandas
+scikit_learn