Create app.py

app.py

@@ -0,0 +1,56 @@
+from model_functions import *
+from preprocessor import *
+import streamlit as st
+import pandas as pd
+
+
+
+def main():
+    st.title("WhatsApp Analysis Tool")
+
+    # File uploader
+    uploaded_file = st.file_uploader("Choose a file (.txt or .zip)", type=['txt', 'zip'])
+    if uploaded_file is not None:
+        file_type = detect_file_type(uploaded_file.name)
+        if file_type in ["txt", "zip"]:
+            # Process the file
+            data = preprocess_whatsapp_messages(uploaded_file, file_type)
+            if data.empty:
+                st.write("No messages found or the file could not be processed.")
+            else:
+                # Date selector
+                date_options = data['date'].dt.strftime('%Y-%m-%d').unique()
+                selected_date = st.selectbox("Select a date for analysis:", date_options)
+
+                if selected_date:
+                    text_for_analysis = get_dated_input(data, selected_date)
+                    with st.expander("Show/Hide Original Conversation"):
+                        st.markdown(f"```\n{text_for_analysis}\n```", unsafe_allow_html=True)
+
+                    # Load models
+                    tokenizer_sentiment, model_sentiment = load_sentiment_analyzer()
+                    tokenizer_summary, model_summary = load_summarizer()
+                    pipe_ner = load_NER()
+
+                    # Perform analysis
+                    sentiment = get_sentiment_analysis(text_for_analysis, tokenizer_sentiment, model_sentiment)
+                    summary = generate_summary(text_for_analysis, tokenizer_summary, model_summary)
+                    ner_results = get_NER(text_for_analysis, pipe_ner)
+
+                    # Display results
+                    st.subheader("Sentiment Analysis")
+                    st.write("Sentiment:", sentiment)
+
+                    st.subheader("Summary")
+                    st.write("Summary:", summary)
+
+                    st.subheader("Named Entity Recognition")
+                    ner_df = pd.DataFrame(ner_results, columns=["Word", "Entity Group"])
+                    st.write(ner_df)
+        else:
+            st.error("Unsupported file type. Please upload a .txt or .zip file.")
+    else:
+        st.info("Please upload a file to proceed.")
+
+if __name__ == "__main__":
+    main()