themeetjani commited on
Commit
c219fea
1 Parent(s): 7fe00f9

Update pages/tc.py

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Files changed (1) hide show
  1. pages/tc.py +3 -2
pages/tc.py CHANGED
@@ -1,5 +1,6 @@
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  import streamlit as st
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  import openai
 
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  openai.api_key = os.getenv("OPENAI_API_KEY")
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  from streamlit import session_state
@@ -79,6 +80,7 @@ if uploaded_file:
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  agg_clustering = AgglomerativeClustering(n_clusters=None, distance_threshold=distance_threshold, linkage='ward')
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  cluster_labels = agg_clustering.fit_predict(matrix)
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  df['Cluster'] = cluster_labels
 
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  # Visualize clusters with t-SNE
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  tsne = TSNE(n_components=2, perplexity=15, random_state=42, init="random", learning_rate=200)
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  vis_dims2 = tsne.fit_transform(matrix)
@@ -111,11 +113,10 @@ if uploaded_file:
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  # Display the plot in Streamlit
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  st.pyplot(fig)
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  st.text_area("Number of Cluster Labels", value=len(np.unique(cluster_labels.tolist())))
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-
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  # Reading a review which belong to each group.
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- rev_per_cluster = 3
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  n_clusters = len(np.unique(cluster_labels.tolist()))
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  for i in range(n_clusters):
 
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  import streamlit as st
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  import openai
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+ import os
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  openai.api_key = os.getenv("OPENAI_API_KEY")
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  from streamlit import session_state
 
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  agg_clustering = AgglomerativeClustering(n_clusters=None, distance_threshold=distance_threshold, linkage='ward')
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  cluster_labels = agg_clustering.fit_predict(matrix)
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  df['Cluster'] = cluster_labels
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+
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  # Visualize clusters with t-SNE
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  tsne = TSNE(n_components=2, perplexity=15, random_state=42, init="random", learning_rate=200)
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  vis_dims2 = tsne.fit_transform(matrix)
 
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  # Display the plot in Streamlit
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  st.pyplot(fig)
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  st.text_area("Number of Cluster Labels", value=len(np.unique(cluster_labels.tolist())))
 
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  # Reading a review which belong to each group.
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+ rev_per_cluster = 1
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  n_clusters = len(np.unique(cluster_labels.tolist()))
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  for i in range(n_clusters):