# streamlit_app.py
import streamlit as st

# --- Helper Function for Model Logic (Placeholder) ---
# In a real app, this function would load your trained model
# and perform summarization and sentiment analysis.
def analyze_text(review_text):
    # FAKE MODEL LOGIC FOR DEMO PURPOSES
    # Replace this with your actual model predictions
    summary = f"Summary: The model would summarize the review '{review_text[:30]}...' here."

    # Fake a sentiment score based on review length for this demo
    if len(review_text) > 50:
        sentiment_score = -0.85
        sentiment_label = "Negative"
    else:
        sentiment_score = 0.95
        sentiment_label = "Positive"

    return summary, sentiment_label, sentiment_score

# --- Streamlit App Layout ---

# Set the title of the app
st.title("📈 ConnectiVerse Customer Review Analyser")

# Add a text input box for the user to enter a review
st.write("Enter a customer review below to analyze its summary and sentiment.")
user_input = st.text_area("Customer Review", "The battery life is amazing and lasts all day!")

# Add a button to trigger the analysis
if st.button("Analyse Review"):
    if user_input:
        # If the user has entered text, call our function
        summary, sentiment_label, sentiment_score = analyze_text(user_input)

        # Display the results
        st.subheader("Analysis Results")
        st.write(summary)

        # Use color to make the sentiment more obvious
        if sentiment_label == "Positive":
            st.success(f"Sentiment: {sentiment_label} (Score: {sentiment_score})")
        else:
            st.error(f"Sentiment: {sentiment_label} (Score: {sentiment_score})")
    else:
        # If the text box is empty, show a warning
        st.warning("Please enter a review to analyse.")