Update app.py

app.py

@@ -1,3 +1,6 @@
+# Codes that passed test
+
+'''
 import streamlit as st
 from transformers import pipeline
 
@@ -42,3 +45,92 @@ def main():
 
 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': "zh",  # 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()
+
+
+