Update app.py

app.py

@@ -1,22 +1,66 @@
 import streamlit as st
 import pandas as pd
-from eda import display_eda
-from prediction import predict_and_strategy
+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
 
-# Load the data
-data = pd.read_csv('threads_reviews.csv')
+# Load the model and tokenizer
+model = load_model('model.h5')
+with open('tokenizer.pkl', 'rb') as f:
+    tokenizer = pickle.load(f)
 
-st.title("Sentiment Analysis and Business Strategy")
+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)
 
-# EDA Section
-st.header("Exploratory Data Analysis")
-if st.checkbox("Show EDA", False):  # Checkbox to toggle EDA display
-    display_eda(data)
+# Streamlit app
+st.title("Thread Review Sentiment Analysis")
 
-# 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}")
+# 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}")