Update app.py

app.py

@@ -1,66 +1,22 @@
 import streamlit as st
 import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from wordcloud import WordCloud
-from sentiment_labeling import add_sentiment_column
-from keras.models import load_model
-import pickle
+from eda import display_eda
+from prediction import predict_and_strategy
 
-# Load the model and tokenizer
-model = load_model('model.h5')
-with open('tokenizer.pkl', 'rb') as f:
-    tokenizer = pickle.load(f)
+# Load the data
+data = pd.read_csv('threads_reviews.csv')
 
-def predict_sentiment(text):
-    # Tokenize and pad the input text
-    seq = tokenizer.texts_to_sequences([text])
-    padded_seq = pad_sequences(seq, maxlen=MAX_LENGTH)
-    # Predict using the model
-    prediction = model.predict(padded_seq)
-    return np.argmax(prediction)
+st.title("Sentiment Analysis and Business Strategy")
 
-# Streamlit app
-st.title("Thread Review Sentiment Analysis")
+# EDA Section
+st.header("Exploratory Data Analysis")
+if st.checkbox("Show EDA", False):  # Checkbox to toggle EDA display
+    display_eda(data)
 
-# Upload CSV file
-uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
-if uploaded_file:
-    data = pd.read_csv(uploaded_file)
-    st.write("Data Loaded Successfully!")
-    
-    # Display raw data
-    if st.checkbox("Show raw data"):
-        st.write(data)
-
-    # Add sentiment column
-    data = add_sentiment_column(data)
-    
-    # Distribution of sentiments
-    st.subheader("Distribution of Sentiments")
-    sentiment_counts = data['sentiment'].value_counts()
-    fig, ax = plt.subplots()
-    sentiment_counts.plot(kind='bar', ax=ax)
-    ax.set_title('Distribution of Sentiments')
-    ax.set_xlabel('Sentiment')
-    ax.set_ylabel('Count')
-    st.pyplot(fig)
-
-    # Word cloud for each sentiment
-    st.subheader("Word Clouds for Sentiments")
-    sentiments = ['positive', 'neutral', 'negative']
-    for sentiment in sentiments:
-        st.write(f"Word Cloud for {sentiment}")
-        subset = data[data['sentiment'] == sentiment]
-        text = " ".join(review for review in subset['review'])
-        wordcloud = WordCloud(max_words=100, background_color="white").generate(text)
-        plt.figure()
-        plt.imshow(wordcloud, interpolation="bilinear")
-        plt.axis("off")
-        st.pyplot()
-
-    # Individual review prediction
-    user_input = st.text_area("Type a review here to predict its sentiment:")
-    if user_input:
-        sentiment_pred = predict_sentiment(user_input)
-        st.write(f"The predicted sentiment is: {sentiment_pred}")
+# Prediction Section
+st.header("Prediction")
+user_input = st.text_area("Enter text for sentiment analysis:", "")
+if st.button("Analyze"):
+    sentiment, strategy = predict_and_strategy(user_input)
+    st.write(f"Sentiment: {sentiment}")
+    st.write(f"Strategy: {strategy}")