Create home.py

home.py

@@ -0,0 +1,64 @@
+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)