Update app.py

app.py

@@ -1,141 +1,3 @@
-# Codes that passed test
-
-'''
-import streamlit as st
-from transformers import pipeline
-
-# Load model only once using caching
-@st.cache_resource  # Use Streamlit's caching to avoid reloading the model
-def load_sentiment_pipeline():
-    return pipeline("sentiment-analysis", model="Rocky080808/finetuned-roberta-base")
-
-# Main application logic
-def main():
-    # Load the sentiment analysis pipeline only once
-    sentiment_pipeline = load_sentiment_pipeline()
-
-    st.title("Final Project Demonstration for Group 8")
-    st.write("This is an application for customer comments sentiment analysis for an e-commerce company.")
-    st.write("Please input the customer comments for analysis below:")
-
-    user_input = st.text_input("Enter customer comments here:")
-
-    # Define a mapping from label to English descriptions
-    label_to_text = {
-        0: "Very dissatisfied, immediate follow-up is required.",
-        1: "Dissatisfied, please arrange follow-up.",
-        2: "Neutral sentiment, further case analysis is needed.",
-        3: "Satisfied, the customer may return for a purchase.",
-        4: "Very satisfied, the customer is very likely to return and recommend."
-    }
-
-    if user_input:
-        # Call the preloaded pipeline to analyze sentiment
-        result = sentiment_pipeline(user_input)
-        label_str = result[0]["label"]  # Get the label as a string, e.g., "LABEL_0"
-        label = int(label_str.split("_")[-1])  # Extract the numeric part of the label
-        confidence = result[0]["score"]
-
-        # Get the corresponding text description based on the label
-        sentiment_text = label_to_text.get(label, "Unrecognized sentiment")
-
-        st.write(f"Sentiment Analysis Result: {sentiment_text}")
-        # Hide the confidence score, no need to show to the users
-        # st.write(f"Confidence Score: {confidence:.2f}")
-
-if __name__ == "__main__":
-    main()
-'''
-
-# New codes to be tested
-
-'''
-import streamlit as st
-from transformers import pipeline
-from langdetect import detect
-
-# Load translation pipeline for multiple languages
-@st.cache_resource  # Cache the model to avoid reloading it
-def load_translation_pipeline():
-    return pipeline("translation", model="facebook/m2m100_418M")
-
-# Load sentiment analysis pipeline
-@st.cache_resource  # Cache the sentiment analysis model
-def load_sentiment_pipeline():
-    return pipeline("sentiment-analysis", model="Rocky080808/finetuned-roberta-base")
-
-# Function to detect language and translate to English
-def translate_to_english(text, translation_pipeline):
-    # Detect the language of the input text
-    detected_language = detect(text)
-    
-    # Supported languages: Chinese, Japanese, German, Spanish, French
-    language_map = {
-        'zh-cn': "zh",  # Simplified Chinese
-        'zh-tw': "zh",  # Traditional Chinese
-        'ja': "ja",  # Japanese
-        'de': "de",  # German
-        'es': "es",  # Spanish
-        'fr': "fr"   # French
-    }
-    
-    if detected_language not in language_map:
-        return None, "Unsupported language"
-    
-    # Translate the text to English using the detected language
-    translated_text = translation_pipeline(text, src_lang=language_map[detected_language], tgt_lang="en")
-    
-    return translated_text[0]['translation_text'], detected_language
-
-# Main application logic
-def main():
-    # Load the translation and sentiment pipelines
-    translation_pipeline = load_translation_pipeline()
-    sentiment_pipeline = load_sentiment_pipeline()
-
-    st.title("Final Project Demonstration for Group 8")
-    st.write("This application supports customer comments sentiment analysis for an e-commerce company.")
-    st.write("You can input text in Chinese, Japanese, German, Spanish, or French. The text will be translated to English for sentiment analysis.")
-
-    user_input = st.text_input("Enter customer comments in supported languages:")
-
-    # Define a mapping from label to English descriptions
-    label_to_text = {
-        0: "Very dissatisfied, immediate follow-up is required.",
-        1: "Dissatisfied, please arrange follow-up.",
-        2: "Neutral sentiment, further case analysis is needed.",
-        3: "Satisfied, the customer may return for a purchase.",
-        4: "Very satisfied, the customer is very likely to return and recommend."
-    }
-
-    if user_input:
-        # Step 1: Translate the input text to English
-        translated_text, detected_language = translate_to_english(user_input, translation_pipeline)
-        
-        if detected_language == "Unsupported language":
-            st.write("The input language is not supported. Please use Chinese, Japanese, German, Spanish, or French.")
-        else:
-            # Display the translated text
-            st.write(f"Detected language: {detected_language}")
-            st.write(f"Translated Text: {translated_text}")
-
-            # Step 2: Perform sentiment analysis on the translated text
-            result = sentiment_pipeline(translated_text)
-            label_str = result[0]["label"]  # Get the label as a string, e.g., "LABEL_0"
-            label = int(label_str.split("_")[-1])  # Extract the numeric part of the label
-            confidence = result[0]["score"]
-
-            # Get the corresponding text description based on the label
-            sentiment_text = label_to_text.get(label, "Unrecognized sentiment")
-
-            st.write(f"Sentiment Analysis Result: {sentiment_text}")
-            st.write(f"Confidence Score: {confidence:.2f}")
-
-if __name__ == "__main__":
-    main()
-'''
-
-
 # V3
 # Improve speed and user experience