Updated app.py

app.py

@@ -21,42 +21,49 @@ st.title(" Your Top 3 Important Sessions")
 st.markdown("This application is a dashboard for displaying your top 3 Sessions at the summit")
 
 doc_emb = np.loadtxt("abstract-embed.txt", dtype=float)
-# 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"])
-
-
+def main():
+        # display the front end aspect
+    st.markdown(html_temp, unsafe_allow_html = True)
+
+        # 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: ")
+# Load model
+    model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
+
+    query =  st.text_input("Enter your query: ")
 
+    if query:
+#st.text_area('Text area')
+        #age = st.number_input("Age in Years")
 #Encode query and documents
-query_emb = model.encode(query).astype(float)
+        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))
 
-#Compute dot score between query and all document embeddings
-scores = util.dot_score(query_emb, doc_emb.astype(float))[0].cpu().tolist()
+    # top_k results to return
+        top_k=3
 
-#Combine docs & scores with other attributes
-doc_score_pairs = list(zip(docs, scores, titles, start_times, end_times, locations))
+        print(" Your top", top_k, "most similar sessions in the Summit:")
 
-# 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)
 
-#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]:
 
-#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')
-    
-    
+            st.write("Score: %f" %score)
+            st.write("Title: %s" %title)
+            st.write("Abstract: %s" %doc)
+            st.write("Location: %s" %location)
+            st.write(f"From {start_time} to {end_time}")
+            st.write('\n')