Update app.py

app.py

@@ -1,3 +1,24 @@
+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)
+
 def predict_sentiment(input_text):
     model, vectorizer = load_model()
     if model and vectorizer:
@@ -7,13 +28,7 @@ def predict_sentiment(input_text):
         # Predict
         prediction = model.predict(input_vector)[0]
         probabilities = model.predict_proba(input_vector)[0]
-        
-        # Debugging prints
-        print(f"Input Text: {input_text}")
-        print(f"Predicted Class: {prediction}")
-        print(f"Probabilities: {probabilities}")
-        
-        # Determine sentiment and confidence
+       # Determine sentiment and confidence
         if prediction == 1:
             sentiment = "Positive"
             confidence = probabilities[1]
@@ -21,7 +36,13 @@ def predict_sentiment(input_text):
             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(
+    description="Enter a text to predict its sentiment."
+)
+
+iface.launch()