Update app.py

app.py

@@ -1,19 +1,3 @@
-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:
@@ -24,20 +8,20 @@ def predict_sentiment(input_text):
         prediction = model.predict(input_vector)[0]
         probabilities = model.predict_proba(input_vector)[0]
         
-        sentiment = "Positive" if prediction == 1 else "Negative"
-        confidence = probabilities[1] if prediction == 1 else probabilities[0]
+        # Debugging prints
+        print(f"Input Text: {input_text}")
+        print(f"Predicted Class: {prediction}")
+        print(f"Probabilities: {probabilities}")
         
+        # Determine sentiment and confidence
+        if prediction == 1:
+            sentiment = "Positive"
+            confidence = probabilities[1]
+        else:
+            sentiment = "Negative"
+            confidence = probabilities[0]
+        
+        # Return sentiment and confidence
         return f"Sentiment: {sentiment}\nConfidence: {confidence:.4f}"
     else:
         return "Model not found or could not be loaded."
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=predict_sentiment,
-    inputs="text",
-    outputs="text",
-    title="Sentiment Analysis Predictor",
-    description="Enter a text to predict its sentiment."
-)
-
-iface.launch()