Upload app.py

app.py

@@ -1,15 +1,21 @@
-import pandas as pd
-from sklearn.metrics.pairwise import cosine_similarity
-import gradio as gr
-def recommend_items(customer_id_1,customer_id_2):
-        H='Error';G=customer_id_2;F=customer_id_1;D='StockCode';C='CustomerID'
-        try:I=pd.read_excel('UBCF_Online_Retail.xlsx')
-        except FileNotFoundError:return'Error: Excel file not found.'
-        E=I.dropna(subset=[C]);A=E.pivot_table(index=C,columns=D,values='Quantity',aggfunc='sum');A=A.applymap(lambda x:1 if x>0 else 0);B=pd.DataFrame(cosine_similarity(A));B.columns=A.index;B[C]=A.index;B=B.set_index(C)
-        try:J=set(A.loc[F].iloc[A.loc[F].to_numpy().nonzero()].index)
-        except KeyError:return pd.DataFrame({H:['Customer ID 1 is invalid. Please enter a valid Customer ID']})
-        try:K=set(A.loc[G].iloc[A.loc[G].to_numpy().nonzero()].index)
-        except KeyError:return pd.DataFrame({H:['Customer ID 2 is invalid. Please enter a valid Customer ID']})
-        L=J-K;return E.loc[E[D].isin(L),[D,'Description']].drop_duplicates().set_index(D)
-iface=gr.Interface(fn=recommend_items,inputs=[gr.inputs.Number(label='Customer ID 1'),gr.inputs.Number(label='Customer ID 2')],outputs=gr.outputs.Dataframe(label='Recommended Items for Customer 2',type='pandas'),theme=gr.themes.Default(primary_hue='slate'),allow_flagging=False)
-iface.launch()
+_A='countries'
+import gradio as gr,numpy as np,pandas as pd
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.stem.wordnet import WordNetLemmatizer
+import nltk
+nltk.download('punkt')
+df=pd.read_csv('Hotel_Reviews.csv')
+df[_A]=df.Hotel_Address.apply(lambda x:x.split(' ')[-1])
+def Input_your_destination_and_description(location,description):
+        M='Average_Score';L='Hotel_Name';K=False;J='similarity';D=True;C='Tags';B=description;df[_A]=df[_A].str.lower();df[C]=df[C].str.lower();B=B.lower();N=word_tokenize(B);E=stopwords.words('english');F=WordNetLemmatizer();O={A for A in N if not A in E};G=set()
+        for P in O:G.add(F.lemmatize(P))
+        A=df[df[_A]==location.lower()];A=A.set_index(np.arange(A.shape[0]));H=[]
+        for Q in range(A.shape[0]):
+                R=word_tokenize(A[C][Q]);S={A for A in R if not A in E};I=set()
+                for T in S:I.add(F.lemmatize(T))
+                U=I.intersection(G);H.append(len(U))
+        A[J]=H;A=A.sort_values(by=J,ascending=K);A.drop_duplicates(subset=L,keep='first',inplace=D);A.sort_values(M,ascending=K,inplace=D);A.reset_index(inplace=D);return A[[L,M,'Hotel_Address']].head(10)
+inputs=[gr.inputs.Textbox(label='Location'),gr.inputs.Textbox(label='Purpose of Travel')]
+outputs=gr.outputs.Dataframe(label='Hotel Recommendations',type='pandas')
+gr.Interface(fn=Input_your_destination_and_description,inputs=inputs,outputs=outputs,theme=gr.themes.Default(primary_hue='slate')).launch()