Create app.py

app.py

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+import streamlit as st
+import time
+import pandas as pd
+import numpy as np
+import plotly.express as px
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
+import matplotlib.pyplot as plt
+
+!pip install -U sentence-transformers
+
+from sentence_transformers import SentenceTransformer, util
+import numpy as np
+import pandas as pd
+
+# Load document embeddings
+doc_emb = np.loadtxt("abstract-embed.txt", dtype=float)
+doc_emb
+
+# Load data
+df = pd.read_csv("sessions.csv", usecols=['Unique ID', 'Name', 'Description', 'Activity Code', 'Start Time', 'End Time', 'Location Name'])
+df.head()
+
+# Get attributes from dataframe
+docs = list(df["Description"])
+titles = list(df["Name"])
+start_times = list(df["Start Time"])
+end_times = list(df["End Time"])
+locations = list(df["Location Name"])
+
+
+# Query
+query = input("Enter your query: ")
+
+#Encode query and documents
+query_emb = model.encode(query).astype(float)
+
+#Compute dot score between query and all document embeddings
+scores = util.dot_score(query_emb, doc_emb.astype(float))[0].cpu().tolist()
+
+#Combine docs & scores with other attributes
+doc_score_pairs = list(zip(docs, scores, titles, start_times, end_times, locations))
+
+# top_k results to return
+top_k=3
+print(" Your top", top_k, "most similar sessions in the Summit:")
+
+#Sort by decreasing score
+doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+
+
+#Output presentation recommendations
+for doc, score, title, start_time, end_time, location in doc_score_pairs[:top_k]:
+   
+    print("Score: %f" %score)
+    print("Title: %s" %title)
+    print("Abstract: %s" %doc)
+    print("Location: %s" %location)
+    f"From {start_time} to {end_time}"
+    print('\n')
+    
+