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title =("<center>"
"<p>""Welcome to Hotel Recommendation System!""</p>"
"</center>")
head = (
"<center>"
"<img src='https://img.freepik.com/free-vector/hotel-tower-concept-illustration_114360-12962.jpg?w=740&t=st=1710571774~exp=1710572374~hmac=6daf26dbfb918ba737df6d2f091351ab0348437afeff121f973efd2d55bfe092' width=400>"
"The robot was trained to search for relevant hotels from the dataset provided."
"</center>"
)
#importing libraries
import requests
import os
import gradio as gr
import pandas as pd
import pprint
from sentence_transformers import SentenceTransformer, CrossEncoder, util
from openai.embeddings_utils import get_embedding, cosine_similarity
df = pd.read_pickle('data.pkl')
embedder = SentenceTransformer('all-mpnet-base-v2')
def search(query,pprint=True):
n = 15
query_embedding = embedder.encode(query,show_progress_bar=True) #encode the query
df["rev_sim_score"] = df.embed_1.apply(lambda x: cosine_similarity(x, query_embedding.reshape(768,-1))) #similarity against each doc
review_results = (
df.sort_values("rev_sim_score", ascending=False) # re-rank
.head(n))
resultlist = []
hlist = []
for r in review_results.index:
if review_results.hotel_name[r] not in hlist:
smalldf = review_results.loc[review_results.hotel_name == review_results.hotel_name[r]]
smallarr = smalldf.rev_sim_score[r].max()
sm =smalldf.rate[r].mean()
if smalldf.shape[1] > 3:
smalldf = smalldf[:3]
resultlist.append(
{
"hotel_name":review_results.hotel_name[r],
"description":review_results.hotel_description[r],
"relevance score": smallarr.tolist(),
"rating": sm.tolist(),
"relevant_reviews": [ smalldf.hotel_info[s] for s in smalldf.index]
})
hlist.append(review_results.hotel_name[r])
return resultlist
def hotel_info(query, pprint=True):
query_embedding = embedder.encode(query,show_progress_bar=True) #encode the query
df["hotel_sim_score"] = df.embed_2.apply(lambda x: cosine_similarity(x, query_embedding.reshape(768,-1)))
#similarity against each doc
n=3
hotel_results = (
df.sort_values("hotel_sim_score", ascending=False) # re-rank
.head(n))
resultlist = []
hlist = []
for r in hotel_results.index:
if hotel_results.hotel_name[r] not in hlist:
smalldf = hotel_results.loc[hotel_results.hotel_name == hotel_results.hotel_name[r]]
smallarr = smalldf.hotel_sim_score[r].max()
sm =smalldf.rate[r].mean()
if smalldf.shape[1] > 3:
smalldf = smalldf[:3]
resultlist.append(
{
"name":hotel_results.hotel_name[r],
"description":hotel_results.hotel_description[r],
"hotel_picture":hotel_results.hotel_image[r],
"relevance score": smallarr.tolist(),
})
return resultlist
def search_ares(query):
x_api_key="ares_e77b47e2754d39b9989a83584d6c528a1980e42ea1f4827eb2584d5b4ee30ccc"
url = "https://api-ares.traversaal.ai/live/predict"
payload = {"query": [query]}
headers = {
"x-api-key": x_api_key,
"content-type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
content = response.json()
return content
def greet(name):
print("Hi! I am your AI assistant.Please let me know your name please.. ")
return "Hi " + name + "!"
#hotel_details = hotel_info(query)
#hotel_reviews = search(query)
#return hotel_details,hotel_reviews
blocks = gr.Blocks()
with blocks as demo:
greet = gr.Interface(fn=greet, inputs="textbox",title=title, description=head, outputs="textbox")
hotel_info= gr.Interface(fn=hotel_info, inputs="text",outputs=[gr.components.Textbox(lines=3, label="Write query to search about hotel info")])
search = gr.Interface(fn=search, inputs="text", outputs=[gr.components.Textbox(lines=3, label="Write query to search about hotel reviews")])
search_ares= gr.Interface(fn=search_ares, inputs="textbox", outputs=[gr.components.Textbox(lines=3, label="Write query to search using Ares API")])
demo.launch(share=True,debug=True) |