Update app.py

app.py

@@ -1,51 +1,46 @@
 import gradio as gr
 from transformers import pipeline
 import re
+from PIL import Image, ImageDraw, ImageFont
 
-# Initialize the pipeline with your custom model
 text_pipe = pipeline("text-classification", model="karalif/myTestModel", return_all_scores=True)
 
-def predict(text):
-    greeting_pattern = r"^(Halló|Hæ|Sæl|Góða|Kær|Daginn|Kvöldið|Ágæt|Elsku)"
+def draw_text_with_highlight(text):
+    # Create an image with white background
+    image = Image.new("RGB", (800, 100), "white")
+    draw = ImageDraw.Draw(image)
     
-    greeting_feedback = ""
+    # Define the font and size
+    try:
+        # Attempt to use a nicer font if available
+        font = ImageFont.truetype("arial.ttf", 24)
+    except IOError:
+        # Fallback to default font
+        font = ImageFont.load_default()
     
-    # Splitting the input text into words
+    # Split the text to find the third word
     words = text.split()
-    # Highlighting the third word if it exists with a background color
     if len(words) >= 3:
-        words[2] = f"<span style='background-color: yellow;'>{words[2]}</span>"
-    highlighted_text = " ".join(words)
-    
-    # Adding the highlighted input text at the beginning of the response, followed by a new line
-    response = f"Input: {highlighted_text}\n\n"
-    
-    results = text_pipe(text)
-    all_scores = results[0]
-    
-    for result in all_scores:
-        label = result['label']
-        score = result['score']
-        response += f"{label}: {score:.3f}\n"
+        # Highlight the third word with a different color
+        highlighted_text = " ".join(words[:2]) + " " + words[2] + " " + " ".join(words[3:])
+        draw.text((10, 10), highlighted_text, fill="black", font=font)
+    else:
+        # Draw the text as is if less than three words
+        draw.text((10, 10), text, fill="black", font=font)
     
-    if not re.match(greeting_pattern, text, re.IGNORECASE):
-        greeting_feedback = "\n- Heilsaðu dóninn þinn\n"
+    return image
 
-    response += greeting_feedback
-    
-    return response
-
-description_html = """
-    <center>
-        <img src='http://www.ru.is/media/HR_logo_vinstri_transparent.png' width='250' height='auto'>
-    </center>
-    """
+def predict(text):
+    # Your existing prediction logic here
+    # For demonstration, we'll just create an image with the highlighted third word
+    image = draw_text_with_highlight(text)
+    return image
 
+# Update the Gradio interface to use the image output
 gr.Interface(
     fn=predict,
     inputs=gr.TextArea(label="Enter text here:"),
-    outputs=gr.TextArea(label="Feedback on text input:"),
-    description=description_html,
+    outputs="image",  # Changed to image output
     examples=[
         ["Það voru vitni að árásinni sem tilkynntu málið til lögreglu sem kom skjótt á vettvang."],
         ["Ég held þetta sé ekki góður tími fara heimsókn."],