Update app.py

app.py

@@ -8,6 +8,8 @@ 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")
+
 @st.cache_resource
 def load_model():
     return SentenceTransformer('distiluse-base-multilingual-cased-v1')
@@ -62,48 +64,47 @@ def tsne_visualization(embeddings, words):
     df['word'] = words
     return df
 
-def main():
-    st.title("Multilingual Text Analysis System")
-    user_input = st.text_area("Enter your text here:")
-
-    if st.button("Analyze"):
-        if user_input:
-            lang = detect_language(user_input)
-            st.write(f"Detected language: {lang}")
-
-            embedding_agent = WordEmbeddingAgent(multi_embedding_model)
-            similarity_agent = SimilarityAgent(multi_embedding_model)
-            topic_modeling_agent = TopicModelingAgent()
-
-            words = user_input.split()
-            
-            with st.spinner("Generating word embeddings..."):
-                embeddings = embedding_agent.get_embeddings(words)
-            st.success("Word Embeddings Generated.")
-
-            with st.spinner("Creating t-SNE visualization..."):
-                tsne_df = tsne_visualization(embeddings, words)
-                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)
-
-            with st.spinner("Extracting topics..."):
-                texts = [user_input, "Another text to improve topic modeling."]
-                topic_distr, vectorizer = topic_modeling_agent.fit_transform(texts, lang)
-                topics = topic_modeling_agent.get_topics(vectorizer)
-                st.subheader("Topics Extracted:")
-                for topic, words in topics.items():
-                    st.write(f"Topic {topic}: {', '.join(words)}")
-
-            with st.spinner("Computing similarity..."):
-                text2 = "Otro texto de ejemplo para comparación de similitud." if lang != 'en' else "Another example text for similarity comparison."
-                similarity_score = similarity_agent.compute_similarity(user_input, text2)
-                st.write(f"Similarity Score with example text: {similarity_score:.4f}")
-
-        else:
-            st.warning("Please enter some text to analyze.")
-
-if __name__ == "__main__":
-    main()
+st.title("Multilingual Text Analysis System")
+user_input = st.text_area("Enter your text here:")
+
+if st.button("Analyze") or user_input:
+    if user_input:
+        lang = detect_language(user_input)
+        st.write(f"Detected language: {lang}")
+
+        embedding_agent = WordEmbeddingAgent(multi_embedding_model)
+        similarity_agent = SimilarityAgent(multi_embedding_model)
+        topic_modeling_agent = TopicModelingAgent()
+
+        words = user_input.split()
+        
+        with st.spinner("Generating word embeddings..."):
+            embeddings = embedding_agent.get_embeddings(words)
+        st.success("Word Embeddings Generated.")
+
+        with st.spinner("Creating t-SNE visualization..."):
+            tsne_df = tsne_visualization(embeddings, words)
+            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)
+
+        with st.spinner("Extracting topics..."):
+            texts = [user_input, "Another text to improve topic modeling."]
+            topic_distr, vectorizer = topic_modeling_agent.fit_transform(texts, lang)
+            topics = topic_modeling_agent.get_topics(vectorizer)
+            st.subheader("Topics Extracted:")
+            for topic, words in topics.items():
+                st.write(f"Topic {topic}: {', '.join(words)}")
+
+        with st.spinner("Computing similarity..."):
+            text2 = "Otro texto de ejemplo para comparación de similitud." if lang != 'en' else "Another example text for similarity comparison."
+            similarity_score = similarity_agent.compute_similarity(user_input, text2)
+            st.write(f"Similarity Score with example text: {similarity_score:.4f}")
+
+    else:
+        st.warning("Please enter some text to analyze.")
+
+st.sidebar.title("About")
+st.sidebar.info("This app performs multilingual text analysis using various NLP techniques.")