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app.py

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+from datetime import datetime
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
+import gradio as gr
+import plotly.graph_objects as go
+from huggingface_hub import from_pretrained_keras
+import os
+
+
+def predictAirPassengers(df, split):
+    ts= pd.read_csv('AirPassengers.csv')
+    df2 =ts.copy()
+    ttSplit=split/100
+    ts['Month']=pd.to_datetime(ts['Month'])
+    ts.rename(columns={'#Passengers':'Passengers'},inplace=True)
+    ts=ts.set_index(['Month'])
+    ts['months'] = [x.month for x in ts.index]
+    ts['years'] = [x.year for x in ts.index]
+    ts.reset_index(drop=True, inplace=True)
+
+    # Split Data
+    X=ts.drop("Passengers",axis=1)
+    Y= ts["Passengers"]
+    X_train=X[:int (len(Y)*ttSplit)] 
+    X_test=X[int(len(Y)*ttSplit):]
+    Y_train=Y[:int (len(Y)*ttSplit)] 
+    Y_test=Y[int(len(Y)*ttSplit):]
+
+    # fit the model
+    rf = RandomForestRegressor()
+    rf.fit(X_train, Y_train)
+    
+    df1=df2.set_index(['Month'])
+    df1.rename(columns={'#Passengers':'Passengers'},inplace=True)
+    train=df1.Passengers[:int (len(ts.Passengers)*ttSplit)]
+    test=df1.Passengers[int(len(ts.Passengers)*ttSplit):]
+    preds=rf.predict(X_test).astype(int) 
+    predictions=pd.DataFrame(preds,columns=['Passengers'])
+    predictions.index=test.index
+    predictions.reset_index(inplace=True)
+    predictions['Month']=pd.to_datetime(predictions['Month'])
+    print(predictions)
+    
+    #combine all into one table
+    ts_df=df.copy()
+    ts_df.rename(columns={'#Passengers':'Passengers'},inplace=True)
+    train= ts_df[:int (len(ts_df)*ttSplit)]
+    test= ts_df[int(len(ts_df)*ttSplit):] 
+
+    df2['Month']=pd.to_datetime(df2['Month'])
+    df2.rename(columns={'#Passengers':'Passengers'},inplace=True)
+    df3= predictions
+    df2['origin']='ground truth'
+    df3['origin']='prediction'
+    df4=pd.concat([df2, df3])
+    print(df4)
+    return df4
+
+demo = gr.Interface(
+    fn =predictAirPassengers,
+    inputs = [
+        gr.Timeseries(label="Input for the timeseries", max_rows=1, interactive=False),
+        gr.Slider(1, 100, value=75, step=1, label="Train test split percentage"),
+    ],
+    outputs= [
+       gr.LinePlot(x='Month', y='Passengers', color='origin')
+       #gr.Timeseries(x='Month')
+
+    ],
+    examples=[
+        [os.path.join(os.path.abspath(''), "AirPassengers_dt.csv"), 75],
+    ]
+)
+
+demo.launch() 

requirements.txt

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+gradio
+pandas
+datetime
+
+plotly
+torch
+git+https://github.com/huggingface/transformers.git
+scikit-learn