app.py

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()