Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import warnings
+
+warnings.filterwarnings("ignore")
+
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+from openai.embeddings_utils import get_embedding, cosine_similarity
+
+df = pd.read_pickle('/content/drive/MyDrive/apatti_movie_search/movie_data_embedding.pkl')
+
+
+embedder = SentenceTransformer('all-mpnet-base-v2')
+#embedder.to('cuda')
+cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-2-v2')
+
+@st.experimental_memo(suppress_st_warning=True)
+def search_bi_encoder(query,top_k=15):
+  query_embedding = embedder.encode(query)
+  df["bi_similarity"] = df.plot_embedding.apply(lambda x: cosine_similarity(x, query_embedding.reshape(768,-1)))
+
+  results = (
+      df.sort_values("bi_similarity", ascending=False)
+      .head(top_k))
+
+  resultlist = []
+
+  hlist = []
+  for r in results.index:
+      if results.title[r] not in hlist:
+          resultlist.append(
+          {
+            "name":results.title[r],
+            "bi_encoder_score": results.bi_similarity[r][0],
+            "year": results.year[r],
+            "language": results.language[r],
+            "cast":results.cast[r],
+            "plot":results['plot'][r],
+            "link":results.link[r]
+          })
+          hlist.append(results.title[r])
+  return resultlist
+
+
+@st.experimental_memo(suppress_st_warning=True)
+def search_cross_encoder(query,candidates):
+  cross_inp = [[query, candidate['plot']] for candidate in candidates]
+  cross_scores = cross_encoder.predict(cross_inp)
+  for idx in range(len(cross_scores)):
+    candidates[idx]['cross-score'] = cross_scores[idx]
+
+  sortedResult = sorted(candidates, key=lambda x: x['cross-score'], reverse=True)
+  return sortedResult
+
+
+@st.experimental_memo(suppress_st_warning=True)
+def search(query,top_k=15):
+  candidates = search_bi_encoder(query,top_k)
+  rankedResult = search_cross_encoder(query,candidates)
+
+  return rankedResult
+
+
+
+
+x = st.slider('Select a value')
+#st.subheader(f"Search Query: {query}")
+search_query = st.text_input("Please Enter your search query here",value="What are the expectations for inflation for Australia?",key="text_input")
+