Update app.py

app.py

@@ -1,91 +1,93 @@
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-plt.style.use("fivethirtyeight")
-import sklearn
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.ensemble import RandomForestRegressor, StackingRegressor
-from sklearn.preprocessing import FunctionTransformer, MinMaxScaler, StandardScaler, LabelEncoder, OrdinalEncoder
-from sklearn.model_selection import train_test_split
-import xgboost
-
-import tqdm
-from tqdm.auto import trange, tqdm
-import pickle
-
-import gradio as gr
-
-
-def run():
-    def make_predictions(domain_, item_, unit_, flag_, flag_description_, element_, mean_temp_, total_temp_, mrh_, trh_, mrf_, trf_, mean_fert_, total_fert_, mean_pest_, total_pest_):
-        X_train = pd.read_csv("xtrain.csv")
-        X = X_train.copy()
-        le = OrdinalEncoder()
-        le.fit(X_train[["Domain", "Item", "Unit", "Flag Description", "Element"]])
-        X_train[["Domain", "Item", "Unit", "Flag Description", "Element"]] = le.transform(X_train[["Domain", "Item", "Unit", "Flag Description", "Element"]])
-
-        scaler_ = MinMaxScaler(feature_range=(0, 1))
-        scaler_.fit(X_train)
-        X_train = scaler_.transform(X_train)
-
-        X_train = pd.DataFrame(X_train, columns = X.columns)
-        dataset_dict = {"Domain": [domain_],
-                        "Item": [item_],
-                        "Unit": [unit_],
-                        "Flag": [flag_],
-                        "Flag Description": [flag_description_],
-                        "Element": [element_],
-                        "Mean temperature": [float(mean_temp_)],
-                        "Total temperature": [float(total_temp_)],
-                        "Mean relative humidity": [float(mrh_)],
-                        "Total relative humidity": [float(trh_)],
-                        "Mean Rainfall(mm)": [float(mrf_)],
-                        "Total Rainfall(mm)": [float(trf_)],
-                        "Mean Fertilizer": [float(mean_fert_)],
-                        "Total Fertilizer": [float(total_fert_)],
-                        "Mean Pesticide": [float(mean_pest_)],
-                        "Total Pesticide": [float(total_pest_)],}
-        df = pd.DataFrame(data = dataset_dict)
-        df = df[['Domain', 'Element', 'Item', 'Year', 'Unit', 'Flag Description',
-                'Mean temperature', 'Total temperature', 'Mean relative humidity',
-                'Total relative humidity', 'Mean Rainfall(mm)', 'Total Rainfall(mm)',
-                'Mean Fertilizer', 'Total Fertilizer', 'Mean Pesticide',
-                'Total Pesticide']]
-        df[["Domain", "Item", "Unit", "Flag Description", "Element"]] = le.transform(df[["Domain", "Item", "Unit", "Flag Description", "Element"]])
-        X = scaler_.transform(df)
-        df = pd.DataFrame(X, columns = X_train.columns)
-        with open("model.pkl", "rb") as file_:
-            model = pickle.load(file_)
-        result = model.predict(df)
-        file_.close()
-        return f"Estimated Crop Yield based on Inputted Values is {result[0]}"
-    
-    title = "Simple Gradio Interface for Estimating/Predicting crop Yield"
-    description = " "
-    cassava_yield = pd.read_csv("cassava_yield.csv")
-    demo = gr.Interface(fn = make_predictions, inputs = [gr.Dropdown(choices = cassava_yield["Domain"].unique().tolist(), multiselect=False),
-                                                        gr.Dropdown(choices = cassava_yield["Item"].unique().tolist(), multiselect=False),
-                                                        gr.Dropdown(choices = cassava_yield["Unit"].unique().tolist(), multiselect=False),
-                                                        gr.Dropdown(choices = cassava_yield["Flag"].unique().tolist(), multiselect=False),
-                                                        gr.Dropdown(choices = cassava_yield["Flag Description"].unique().tolist(), multiselect=False),
-                                                        gr.Dropdown(choices = cassava_yield["Element"].unique().tolist(), multiselect=False),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Temperature(mean)", placeholder = "mean temp"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Temperature(Total)", placeholder = "total temp"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Relative Humidity(mean)", placeholder = "mean relative humidity"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Relative Humidity(Total)", placeholder = "total relative humidity"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Rainfall(mm)(mean)", placeholder = "mean rainfall"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Rainfall(mm)(Total)", placeholder = "total rainfall"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Fertilizer(mean)", placeholder = "mean fertilizer"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Fertilizer(Total)", placeholder = "total fertilizer"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Pesticide(mean)", placeholder = "mean pesticide"),
-                                                        gr.TextArea(lines = 1, label = "Input Estimated Pesticide(Total)", placeholder = "total pesticide")],
-                        outputs = gr.Textbox(label = "Result....."),
-                        title = title,
-                        description=description)
-
-    demo.launch(share = False)
-
-
-if __name__ == "__main__":
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+plt.style.use("fivethirtyeight")
+import sklearn
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor, StackingRegressor
+from sklearn.preprocessing import FunctionTransformer, MinMaxScaler, StandardScaler, LabelEncoder, OrdinalEncoder
+from sklearn.model_selection import train_test_split
+import xgboost
+
+import tqdm
+from tqdm.auto import trange, tqdm
+import pickle
+
+import gradio as gr
+
+
+def run():
+    def make_predictions(domain_, year_, item_, unit_, flag_, flag_description_, element_, mean_temp_, total_temp_, mrh_, trh_, mrf_, trf_, mean_fert_, total_fert_, mean_pest_, total_pest_):
+        X_train = pd.read_csv("xtrain.csv")
+        X = X_train.copy()
+        le = OrdinalEncoder()
+        le.fit(X_train[["Domain", "Item", "Unit", "Flag Description", "Element"]])
+        X_train[["Domain", "Item", "Unit", "Flag Description", "Element"]] = le.transform(X_train[["Domain", "Item", "Unit", "Flag Description", "Element"]])
+
+        scaler_ = MinMaxScaler(feature_range=(0, 1))
+        scaler_.fit(X_train)
+        X_train = scaler_.transform(X_train)
+
+        X_train = pd.DataFrame(X_train, columns = X.columns)
+        dataset_dict = {"Domain": [domain_],
+                        "Year": [year_],
+                        "Item": [item_],
+                        "Unit": [unit_],
+                        "Flag": [flag_],
+                        "Flag Description": [flag_description_],
+                        "Element": [element_],
+                        "Mean temperature": [float(mean_temp_)],
+                        "Total temperature": [float(total_temp_)],
+                        "Mean relative humidity": [float(mrh_)],
+                        "Total relative humidity": [float(trh_)],
+                        "Mean Rainfall(mm)": [float(mrf_)],
+                        "Total Rainfall(mm)": [float(trf_)],
+                        "Mean Fertilizer": [float(mean_fert_)],
+                        "Total Fertilizer": [float(total_fert_)],
+                        "Mean Pesticide": [float(mean_pest_)],
+                        "Total Pesticide": [float(total_pest_)],}
+        df = pd.DataFrame(data = dataset_dict)
+        df = df[['Domain', 'Element', 'Item', 'Year', 'Unit', 'Flag Description',
+                'Mean temperature', 'Total temperature', 'Mean relative humidity',
+                'Total relative humidity', 'Mean Rainfall(mm)', 'Total Rainfall(mm)',
+                'Mean Fertilizer', 'Total Fertilizer', 'Mean Pesticide',
+                'Total Pesticide']]
+        df[["Domain", "Item", "Unit", "Flag Description", "Element"]] = le.transform(df[["Domain", "Item", "Unit", "Flag Description", "Element"]])
+        X = scaler_.transform(df)
+        df = pd.DataFrame(X, columns = X_train.columns)
+        with open("model.pkl", "rb") as file_:
+            model = pickle.load(file_)
+        result = model.predict(df)
+        file_.close()
+        return f"Estimated Crop Yield based on Inputted Values is {result[0]}"
+    
+    title = "Simple Gradio Interface for Estimating/Predicting crop Yield"
+    description = " "
+    cassava_yield = pd.read_csv("cassava_yield.csv")
+    demo = gr.Interface(fn = make_predictions, inputs =[gr.Dropdown(choices = cassava_yield["Domain"].unique().tolist(), multiselect=False),
+                                                        gr.Dropdown(choices = cassava_yield["Domain"].unique().tolist(), multiselect=False),
+                                                        gr.Dropdown(choices = cassava_yield["Item"].unique().tolist(), multiselect=False),
+                                                        gr.Dropdown(choices = cassava_yield["Unit"].unique().tolist(), multiselect=False),
+                                                        gr.Dropdown(choices = cassava_yield["Flag"].unique().tolist(), multiselect=False),
+                                                        gr.Dropdown(choices = cassava_yield["Flag Description"].unique().tolist(), multiselect=False),
+                                                        gr.Dropdown(choices = cassava_yield["Element"].unique().tolist(), multiselect=False),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Temperature(mean)", placeholder = "mean temp"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Temperature(Total)", placeholder = "total temp"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Relative Humidity(mean)", placeholder = "mean relative humidity"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Relative Humidity(Total)", placeholder = "total relative humidity"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Rainfall(mm)(mean)", placeholder = "mean rainfall"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Rainfall(mm)(Total)", placeholder = "total rainfall"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Fertilizer(mean)", placeholder = "mean fertilizer"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Fertilizer(Total)", placeholder = "total fertilizer"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Pesticide(mean)", placeholder = "mean pesticide"),
+                                                        gr.TextArea(lines = 1, label = "Input Estimated Pesticide(Total)", placeholder = "total pesticide")],
+                        outputs = gr.Textbox(label = "Result....."),
+                        title = title,
+                        description=description)
+
+    demo.launch(share = False)
+
+
+if __name__ == "__main__":
     run()