Update app.py

app.py

@@ -2,40 +2,27 @@ import os
 import joblib
 import gradio as gr
 from sklearn.feature_extraction.text import TfidfVectorizer
-
 from sklearn.linear_model import LogisticRegression
 
-
-
-
-
-
-
 MODEL_PATH = 'tunisian_arabiz_sentiment_analysis_model.pkl'
 VECTORIZER_PATH = 'tfidf_vectorizer.pkl'
 
-
 def load_model():
     if os.path.exists(MODEL_PATH) and os.path.exists(VECTORIZER_PATH):
         model = joblib.load(MODEL_PATH)
+        vectorizer = joblib.load(VECTORIZER_PATH)
+        return model, vectorizer
+    return None, None
 
 def predict_sentiment(input_text):
     model, vectorizer = load_model()
     if model and vectorizer:
-        # Transform input text
-        input_vector = vectorizer.transform([input_text])
-        
-        # Predict
         prediction = model.predict(input_vector)[0]
         probabilities = model.predict_proba(input_vector)[0]
-       # Determine sentiment and confidence
-        if prediction == 1:
-            sentiment = "Positive"
-            confidence = probabilities[1]
-        else:
-            sentiment = "Negative"
-            confidence = probabilities[0]
         
+        sentiment = "Positive" if prediction == 1 else "Negative"
+        confidence = probabilities[1] if prediction == 1 else probabilities[0]
+
         return f"Sentiment: {sentiment}\nConfidence: {confidence:.4f}"
     else:
         return "Model not found or could not be loaded."
@@ -44,9 +31,9 @@ def predict_sentiment(input_text):
 iface = gr.Interface(
     fn=predict_sentiment,
     inputs="text",
-    outputs=["text", "text"],
+    outputs="text",
     title="Sentiment Analysis Predictor",
-    description="Enter a text to predict its sentiment and see confidence level."
+    description="Enter a text to predict its sentiment."
 )
 
 iface.launch()