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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=os.getenv("x_api_key") | |
| 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) |