Update app.py

app.py

@@ -1,25 +1,34 @@
 import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import joblib
+from sklearn.feature_extraction.text import TfidfVectorizer
 
-# Load your model and tokenizer
-model_name = "tunisian_arabiz_sentiment_analysis_model"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Load the saved model and vectorizer
+model = joblib.load('tunisian_arabiz_sentiment_analysis_model.pkl')
+vectorizer = joblib.load('tfidf_vectorizer.pkl')
 
-def predict(input_text):
-    # Tokenize and make prediction
-    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
-    outputs = model(**inputs)
-    prediction = outputs.logits.argmax(-1).item()
-    return prediction
+def predict_sentiment(text):
+    # Transform the input text using the loaded vectorizer
+    text_vectorized = vectorizer.transform([text])
+    
+    # Make prediction
+    prediction = model.predict(text_vectorized)[0]
+    
+    # Convert prediction to sentiment
+    sentiment = "Positive" if prediction == 1 else "Negative"
+    
+    # Get prediction probability
+    probabilities = model.predict_proba(text_vectorized)[0]
+    confidence = max(probabilities)
+    
+    return f"Sentiment: {sentiment}\nConfidence: {confidence:.2f}"
 
-# Define Gradio interface
+# Create Gradio interface
 iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
-    outputs="label",
-    title="Your Model Name",
-    description="Enter some text to get a prediction."
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Enter Tunisian Arabiz text here..."),
+    outputs="text",
+    title="Tunisian Arabiz Sentiment Analysis",
+    description="This model predicts the sentiment of Tunisian Arabiz text as either Positive or Negative."
 )
 
 # Launch the interface