Commit App.py

App.py

@@ -0,0 +1,148 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+from sklearn.preprocessing import LabelEncoder
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+
+def read(file,dep,ord):
+    df = pd.read_csv(file.name)
+    cat = list()
+    dep_type = str(df.dtypes[dep])
+    for col in df.columns.values:
+        if str(df.dtypes[col]) == 'bool' or str(df.dtypes[col]) == 'object':
+            cat.append(col)
+    new_df = df.dropna(axis=0)
+    if ord == "" and (dep_type == 'bool' or dep_type == 'object'):
+        ord = list()
+        ord.append(dep)
+    elif ord == "":
+        ord = list()
+    else:
+        pass
+    if len(ord)!=0:
+        le = LabelEncoder()
+        new_df[ord] = new_df[ord].apply(lambda col: le.fit_transform(col))
+    nom = list(set(cat).difference(set(ord)))
+    if len(nom) == 0:
+        pass
+    else:
+        ohe_df = pd.get_dummies(new_df[nom], drop_first=True)
+        new_df.drop(columns=nom, axis=1,inplace=True)
+        new_df = pd.concat([new_df,ohe_df],axis=1)
+    if dep_type == 'bool' or dep_type == 'object':
+        text = "classification"
+        result = classification(new_df,dep)
+    else:
+        text = "regression"
+        result = regression(new_df,dep)
+    return df.head(5),new_df.head(5),result, text, cat, ord, nom
+    
+def classification(df,dep):
+    X = df.drop(dep,axis=1)
+    y = df[dep]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+    scale = StandardScaler()
+
+    pipe = Pipeline(steps=[('scale',scale),('classification','pass')])
+
+    parameters = [
+        {
+            'classification':[LogisticRegression()],
+        },
+        {
+            'classification':[RandomForestClassifier()],
+        },
+        {
+            'classification':[DecisionTreeClassifier()],
+        },
+        {
+            'classification':[SVC()],
+        },
+        {
+            'classification':[KNeighborsClassifier(n_neighbors=5)],
+        },
+    ]
+
+    search = GridSearchCV(pipe, param_grid=parameters, n_jobs=-1, scoring='accuracy')
+    search.fit(X_train,y_train)
+
+    result = pd.DataFrame(search.cv_results_)[['params','rank_test_score','mean_test_score']]
+
+    result['mean_test_score']= (result['mean_test_score'])*100
+    result = result.astype({'params': str})
+
+    result.sort_values('rank_test_score',inplace=True)
+    return result
+
+def regression(df,dep):
+    X = df.drop(dep,axis=1)
+    y =df[dep]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+    scale = StandardScaler()
+
+    pipe = Pipeline(steps=[('scale',scale),('regression','pass')])
+
+    parameters = [
+        {
+            'regression':[LinearRegression()]
+        },
+        {
+            'regression':[RandomForestRegressor()],
+        },
+        {
+            'regression':[DecisionTreeRegressor()],
+        },
+        {
+            'regression':[SVR()],
+        },
+    ]
+
+    search = GridSearchCV(pipe, param_grid=parameters, cv=5, n_jobs=-1, scoring='neg_mean_absolute_percentage_error')
+    search.fit(X_train,y_train)
+
+    result = pd.DataFrame(search.cv_results_)[['params','rank_test_score','mean_test_score']]
+    
+    result['mean_test_score']= (result['mean_test_score']+1)*100
+    result = result.astype({'params': str})
+    
+    result.sort_values('rank_test_score',inplace=True)
+    return result
+    
+
+with gr.Blocks() as demo:
+    gr.Markdown("Model Recommendation App **Upload** file to see the output.")
+    with gr.Column():
+        with gr.Row():
+            file = gr.File(label="Upload File(Comma Separated)")
+            dep = gr.Textbox(label="Dependent Variable(Variable as in the file)")
+            ord = gr.Textbox(label="Ordinal Variables(Seperate with a comma)")
+            submit = gr.Button("Submit")
+        text = gr.Text(label="Suitable Algorithm")
+        other1 = gr.Text(label="Categorical Variables")
+        other2 = gr.Text(label="LabelEncoded Vairables")
+        other3 = gr.Text(label="OneHotEncoded Variables")
+        with gr.Row():
+            org = gr.DataFrame(overflow_row_behaviour="paginate", label="Original Data")
+            converted = gr.DataFrame(overflow_row_behaviour="paginate", label="Transformed Data")
+        result = gr.DataFrame(label="Result")
+    submit.click(fn=read, inputs=[file,dep,ord], outputs=[org,converted,result,text,other1,other2,other3])
+demo.launch()