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import json
import pandas as pd
import time
import spacy
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
from collections import Counter
from heapq import nlargest
import nltk
import numpy as np
from sentence_transformers import SentenceTransformer, util
from openai.embeddings_utils import get_embedding, cosine_similarity
import gradio as gr
from huggingface_hub import InferenceClient
import nltk
from spacy.cli import download
nltk.download('punkt')
nltk.download('punkt_tab')
df_with_embedding2 = pd.read_pickle('df_2.pkl')
from sentence_transformers import SentenceTransformer #import model
embedder = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5",trust_remote_code=True)
def search(query):
# return the first 15 results ranked by similarity.
n = 15
# Embedding the query.
query_embedding = embedder.encode(query)
# Generate the similarity column.
df_with_embedding2["similarity"] = (df_with_embedding2.embedding_summary.apply(lambda x: cosine_similarity(x, query_embedding.reshape(768,-1)))+df_with_embedding2.embedding_reviews.apply(lambda x: cosine_similarity(x, query_embedding.reshape(768,-1))))/2
results = (
df_with_embedding2.sort_values("similarity", ascending=False)
.head(n))
resultlist = []
hlist = []
for r in results.index:
if results.hotel_name[r] not in hlist:
smalldf = results.loc[results.hotel_name == results.hotel_name[r]]
if smalldf.shape[1] > 3:
smalldf = smalldf[:3]
resultlist.append(
{
"name":results.hotel_name[r],
"score": smalldf.similarity[r][0],
"rating": smalldf.rating_value[r],
"review_count": smalldf.review_count[r],
"street_address": smalldf.street_address[r],
"city": smalldf.locality[r],
"country": smalldf.country[r],
"hotel_description":smalldf.hotel_description[r],
"hotel_image":smalldf.hotel_image[r]
})
hlist.append(results.hotel_name[r])
return resultlist
import gradio as gr
import json
def display_hotel_info(query_json_str):
"""This app helps you find hotels based on your search query. Enter a city, location, hotel name or just type what you looking for ."""
try:
query_json = search(query_json_str) # Assume this function returns a list of hotel data dictionaries
hotel_infos = []
image_outputs = []
for hotel in query_json:
if not isinstance(hotel, dict):
raise ValueError("Expected hotel data to be a dictionary.")
name = hotel.get("name", "N/A")
score = hotel.get("score", 0.0)
rating = hotel.get("rating", "N/A")
review_count = hotel.get("review_count", 0)
street_address = hotel.get("street_address", "N/A")
city = hotel.get("city", "N/A")
country = hotel.get("country", "N/A")
hotel_description=hotel.get("hotel_description","N/A")
hotel_image = hotel.get("hotel_image", None)
hotel_info = f"""
<div style="display: flex; align-items: center; margin-bottom: 20px;">
<div style="flex: 1;">
<h3>{name}</h3>
<p><strong>Rating:</strong> {rating}</p>
<p><strong>Review Count:</strong> {review_count}</p>
<p><strong>Address:</strong> {street_address}, {city}, {country}</p>
<p><strong>hotel_description:</strong> {hotel_description}</p>
</div>
<div style="flex: 0 0 150px;">
<img src="{hotel_image}" alt="{name}" style="max-width: 150px; max-height: 150px; object-fit: cover;">
</div>
</div>
"""
hotel_infos.append(hotel_info)
return "<br><br>".join(hotel_infos)
except (json.JSONDecodeError, ValueError) as e:
return f"Error: {str(e)}"
interface = gr.Interface(
fn=display_hotel_info,
inputs="text",
outputs=gr.HTML(label="Hotel Information"),
title="Hotel Recommendation Display",
description="This app helps you find hotels based on your search query. Enter a city, location, hotel name or just type what you looking for.",
)
interface.launch()
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