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README.md

@@ -1,12 +1,6 @@
 ---
-title: Hackathon Gradio App
-emoji: 🌖
-colorFrom: red
-colorTo: blue
+title: hackathon-gradio-app
+app_file: app.py
 sdk: gradio
 sdk_version: 4.21.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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)

data.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98f0d222e8fcef69188ed5b58c9e257789378546ffc90b78169a81d7f3bb8601
+size 51660046