Update app.py

app.py

@@ -2,11 +2,7 @@ import streamlit as st
 from sentence_transformers import SentenceTransformer, util
 from sklearn.decomposition import LatentDirichletAllocation
 from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.manifold import TSNE
 from langdetect import detect, DetectorFactory
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
 
 st.set_page_config(page_title="Multilingual Text Analysis System", layout="wide")
 
@@ -56,18 +52,6 @@ def detect_language(text):
     except:
         return "unknown"
 
-@st.cache_data
-def tsne_visualization(embeddings, words):
-    if len(words) < 3:  # Not enough words for t-SNE
-        return pd.DataFrame({'word': words})
-    
-    perplexity = min(30, len(words) - 1)
-    tsne = TSNE(n_components=2, random_state=42, perplexity=perplexity)
-    embeddings_2d = tsne.fit_transform(embeddings)
-    df = pd.DataFrame(embeddings_2d, columns=['x', 'y'])
-    df['word'] = words
-    return df
-
 st.title("Multilingual Text Analysis System")
 user_input = st.text_area("Enter your text here:")
 
@@ -86,17 +70,8 @@ if st.button("Analyze"):
             embeddings = embedding_agent.get_embeddings(words)
         st.success("Word Embeddings Generated.")
 
-        with st.spinner("Visualizing words..."):
-            tsne_df = tsne_visualization(embeddings, words)
-            if 'x' in tsne_df.columns:
-                fig, ax = plt.subplots()
-                ax.scatter(tsne_df['x'], tsne_df['y'])
-                for i, word in enumerate(tsne_df['word']):
-                    ax.annotate(word, (tsne_df['x'][i], tsne_df['y'][i]))
-                st.pyplot(fig)
-            else:
-                st.write("Word list (not enough words for t-SNE visualization):")
-                st.write(", ".join(words))
+        st.write("Words in the input:")
+        st.write(", ".join(words))
 
         if len(words) > 1:
             with st.spinner("Extracting topics..."):