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import streamlit as st
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from functions import preprocess, sentiment_analysis, map_sentiment_score_to_rating
def render_home(model, tokenizer):
st.title("Movie Review App")
st.write("Welcome to our Movie Review App powered by the state-of-the-art TinyBERT model with an impressive accuracy score of 0.86 respectively. Get ready to dive into the world of cinema and discover the sentiments behind your favorite movies. Whether it's a thrilling 9 or a heartwarming 3, our app not only predicts the sentiment but also rates the movie on a scale of 1 to 10. Express your thoughts, press 'Analyze,' and uncover the emotional depth of your movie review")
st.image("Assets/movie_review.png", caption="", use_column_width=True)
# Create a list to store comments
comments = []
# Input text area for the user to enter a review
input_text = st.text_area("Write your movie review here...")
# Output area for displaying sentiment
if st.button("Analyze Review"):
if input_text:
# Perform sentiment analysis using the loaded model
scores = sentiment_analysis(input_text, tokenizer, model)
# Display sentiment scores
st.text("Sentiment Scores:")
for label, score in scores.items():
st.text(f"{label}: {score:.2f}")
# Determine the overall sentiment label
sentiment_label = max(scores, key=scores.get)
# Map sentiment labels to human-readable forms
sentiment_mapping = {
"Negative": "Negative",
"Positive": "Positive"
}
sentiment_readable = sentiment_mapping.get(sentiment_label)
# Display the sentiment label
st.text(f"Sentiment: {sentiment_readable}")
rating = map_sentiment_score_to_rating(scores[sentiment_label])
# Convert the rating to an integer
rating = int(rating)
st.text(f"Rating: {rating}")
# Button to Clear the input text
if st.button("Clear Input"):
input_text = ""
# Input area for adding comments
new_comment = st.text_area("Add a comment:", "")
if st.button("Submit Comment"):
if new_comment:
comments.append(new_comment)
# Display the comments
st.subheader("Comments")
for comment in comments:
st.write(comment) |