Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from nltk.corpus import stopwords 
+from nltk.tokenize import word_tokenize
+from nltk.stem.wordnet import WordNetLemmatizer
+
+# Import the dataset
+df = pd.read_csv('Hotel_Reviews.csv')
+df['countries'] = df.Hotel_Address.apply(lambda x: x.split(' ')[-1])
+
+# Define the function to recommend hotels
+def Input_your_destination_and_description(location,description):
+    # Making these columns lowercase
+    df['countries']=df['countries'].str.lower()
+    df['Tags']=df['Tags'].str.lower()
+    
+    # Dividing the texts into small tokens (sentences into words)
+    description = description.lower()
+    description_tokens=word_tokenize(description)  
+    
+    sw = stopwords.words('english') # List of predefined english  stopwords to be used for computing
+    lemm = WordNetLemmatizer() # We now define the functions below connecting these imported packages
+    filtered_sen = {w for w in description_tokens if not w in sw}
+    f_set=set()
+    for fs in filtered_sen:
+        f_set.add(lemm.lemmatize(fs))
+    
+    # Defining a new variable that takes in the location inputted and bring out the features defined below
+    country_feat = df[df['countries']==location.lower()]
+    country_feat = country_feat.set_index(np.arange(country_feat.shape[0]))
+    cos=[];
+    for i in range(country_feat.shape[0]):
+        temp_tokens=word_tokenize(country_feat['Tags'][i])
+        temp1_set={w for w in temp_tokens if not w in sw}
+        temp_set=set()
+        for se in temp1_set:
+            temp_set.add(lemm.lemmatize(se))
+        rvector = temp_set.intersection(f_set)
+        cos.append(len(rvector))
+    country_feat['similarity']=cos
+    country_feat=country_feat.sort_values(by='similarity',ascending=False)
+    country_feat.drop_duplicates(subset='Hotel_Name',keep='first',inplace=True)
+    country_feat.sort_values('Average_Score',ascending=False,inplace=True)
+    country_feat.reset_index(inplace=True)
+    return country_feat[['Hotel_Name','Average_Score','Hotel_Address']].head(10)
+
+# Create the input interface
+inputs = [gr.inputs.Textbox(label="Location"), 
+          gr.inputs.Textbox(label="Purpose of Travel")]
+
+# Create the output interface
+outputs=gr.outputs.Dataframe(label="Hotel Recommendations",type="pandas")
+
+# Create the interface
+gr.Interface(fn=Input_your_destination_and_description, 
+             inputs=inputs, 
+             outputs=outputs).launch()
+