Update app.py

app.py

@@ -1,7 +1,61 @@
 import gradio as gr
+import pandas as pd
+import tiktoken
 
-def greet(name):
-    return "Hello " + name + "!!"
+import time
+from sentence_transformers import SentenceTransformer
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+
+
+
+
+import os
+import torch
+
+from openai.embeddings_utils import get_embedding, cosine_similarity
+import os
+
+
+
+df = pd.read_pickle('entire_data.pkl') #to load 123.pkl back to the dataframe df
+embedder = SentenceTransformer('all-mpnet-base-v2')
+
+def search(query):
+  n = 15
+  query_embedding = embedder.encode(query)
+  df["similarity"] = df.embedding.apply(lambda x: cosine_similarity(x, query_embedding.reshape(768,-1)))
+
+  results = (
+      df.sort_values("similarity", ascending=False)
+      .head(n))
+  
+  resultlist = []
+
+  hlist = []
+  for r in results.index:
+      if results.name[r] not in hlist:
+          smalldf = results.loc[results.name == results.name[r]]
+          smallarr = smalldf.similarity[r].max()
+          sm =smalldf.rating[r].mean()
+          
+          if smalldf.shape[1] > 3:
+            smalldf = smalldf[:3]
+
+          resultlist.append(
+          {
+            "name":results.name[r],
+            "description":results.description[r],
+            "relevance score": smallarr.tolist(),
+            "rating": sm.tolist(),
+            "relevant_reviews": [ smalldf.text[s] for s in smalldf.index]
+          })
+          hlist.append(results.name[r])
+  return resultlist
+
+def greet(query):
+    bm25 = search(query)
+    return bm25
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="json")
+
+demo.launch()