Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-from PIL import Image, ImageDraw, ImageFont
 
 # Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("OatNapat/finetuned_yelp")
@@ -9,28 +8,6 @@ model = AutoModelForSequenceClassification.from_pretrained("OatNapat/finetuned_y
 # Create a sentiment analysis pipeline with the explicit tokenizer
 nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 
-# Function to convert text to an image
-def text_to_image(text):
-    # Create a blank image with a white background
-    image = Image.new("RGB", (500, 100), "white")
-    draw = ImageDraw.Draw(image)
-
-    # Define the font and font size
-    font = ImageFont.load_default()
-    font_size = 20
-
-    # Calculate text width and height
-    text_width, text_height = font.getsize(text)
-
-    # Calculate the position to center the text in the image
-    x = (image.width - text_width) / 2
-    y = (image.height - text_height) / 2
-
-    # Draw the text on the image
-    draw.text((x, y), text, fill="black", font=font)
-
-    return image
-
 st.title("Sentiment Analysis App")
 user_input = st.text_input("ป้อนประโยคเพื่อวิเคราะห์ความรู้สึก:")
 if user_input:
@@ -51,8 +28,4 @@ if user_input:
     sentiment_explanation = sentiment_explanations.get(sentiment_label, "Unknown")
 
     st.write(f"Sentiment: {sentiment_explanation}")
-    st.write(f"Confidence: {sentiment_score:.4f}")
-
-    # Convert the sentiment explanation to an image
-    sentiment_image = text_to_image(sentiment_explanation)
-    st.image(sentiment_image, caption="Sentiment Explanation", use_column_width=True)
+    st.write(f"Confidence: {sentiment_score:.4f}")