Update app.py

app.py

@@ -1,12 +1,10 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load the image to text model pipeline
-image_to_text = pipeline("image-to-text", model="microsoft/trocr-base-handwritten")
 
 # Load the text classification model pipeline
 classifier = pipeline("text-classification", model='lguoao123/model2', return_all_scores=True)
-
+translate = pipe = pipeline("text2text-generation", model="jieshenai/zh_en_translation")
 
 # Streamlit application title
 st.title("Financial News Sentiment Classification")
@@ -17,8 +15,9 @@ text = st.text_area("Enter the financial news to classify", "")
 
 # Perform text classification when the user clicks the "Classify" button
 if st.button("Classify"):
+    translate_text = translate(text)[0]['generated_text']
     # Perform text classification on the input text
-    results = classifier(text)[0]
+    results = classifier(translate_text)[0]
 
     # Display the classification result
     max_score = float('-inf')
@@ -33,6 +32,3 @@ if st.button("Classify"):
     st.write("Label:", max_label)
     st.write("Score:", max_score)
 
-    # Perform image to text conversion
-    image_result = image_to_text(text)
-    st.write("Image to Text Result:", image_result)