Upload app.py

app.py

@@ -50,38 +50,40 @@ def preprocess_text_for_rnn(text, tokenizer, maxlen):
     sequence = tokenizer.texts_to_sequences([text])
     padded_sequence = pad_sequences(sequence, padding='post', maxlen=maxlen)
     return padded_sequence
-
+# Function to predict sentiment using Logistic Regression
 def predict_lr(text):
     preprocessed_text = preprocess_text(text)
     vectorized_text = tfidf_vectorizer.transform([preprocessed_text])
-    dense_vectorized_text = vectorized_text.toarray()
-    prediction_probs = lr_model.predict_proba(dense_vectorized_text)[0]
-    prediction = int(np.argmax(prediction_probs))
-    return prediction, prediction_probs
+    dense_vectorized_text = vectorized_text.toarray()  # Convert to dense array
+    prediction = int(lr_model.predict(dense_vectorized_text)[0])
+    return prediction
+
 
+# Function to predict sentiment using SVM
 def predict_svm(text):
     preprocessed_text = preprocess_text(text)
     vectorized_text = tfidf_vectorizer.transform([preprocessed_text])
-    dense_vectorized_text = vectorized_text.toarray()
-    prediction_probs = svm_model.predict_proba(dense_vectorized_text)[0]
-    prediction = int(np.argmax(prediction_probs))
-    return prediction, prediction_probs
+    dense_vectorized_text = vectorized_text.toarray()  # Convert to dense array
+    prediction = int(svm_model.predict(dense_vectorized_text)[0])
+    return prediction
 
+# Function to predict sentiment using Neural Network
 def predict_nn(text):
     preprocessed_text = preprocess_text(text)
     vectorized_text = tfidf_vectorizer.transform([preprocessed_text])
-    dense_vectorized_text = vectorized_text.toarray()
+    dense_vectorized_text = vectorized_text.toarray()  # Convert to dense array
+
     prediction_probs = nn_model.predict(dense_vectorized_text)[0]
     prediction = int(np.argmax(prediction_probs))
-    return prediction, prediction_probs
+    return prediction
 
+# Function to predict sentiment using Multinomial Naive Bayes
 def predict_mnb(text):
     preprocessed_text = preprocess_text(text)
     vectorized_text = tfidf_vectorizer.transform([preprocessed_text])
-    dense_vectorized_text = vectorized_text.toarray()
-    prediction_probs = mnb_model.predict_proba(dense_vectorized_text)[0]
-    prediction = int(np.argmax(prediction_probs))
-    return prediction, prediction_probs
+    dense_vectorized_text = vectorized_text.toarray()  # Convert to dense array
+    prediction = int(mnb_model.predict(dense_vectorized_text)[0])
+    return prediction
 
 def predict_rnn(text):
     processed_text = preprocess_text_for_rnn(text, tokenizer, maxlen=170)
@@ -92,15 +94,19 @@ def predict_rnn(text):
 def sentiment_prediction(text, model):
     prediction, percentages = 0, []
     if model == "Logistic Regression":
-        prediction, percentages = predict_lr(text)
+        prediction = predict_lr(text)
     elif model == "SVM":
-        prediction, percentages = predict_svm(text)
+        prediction = predict_svm(text)
     elif model == "Neural Network":
-        prediction, percentages = predict_nn(text)
+        prediction = predict_nn(text)
     elif model == "Multinomial Naive Bayes":
-        prediction, percentages = predict_mnb(text)
+        prediction = predict_mnb(text)
     elif model == "Recurrent Neural Network":
         prediction, percentages = predict_rnn(text)
+                # Calculate percentages for both labels
+        percentage_negative = percentages[0]
+        percentage_positive = percentages[1]
+
 
     # Displaying emojis based on sentiment
     emoji_positive = "😃"
@@ -113,11 +119,14 @@ def sentiment_prediction(text, model):
     # Create label for the prediction
     prediction_label = labels[prediction]
 
-    # Calculate percentages for both labels
-    percentage_negative = percentages[0]
-    percentage_positive = percentages[1]
+    if model == "Recurrent Neural Network":
+        return prediction_label, f" {labels[0]}: {percentage_negative:.2%}, Percentage {labels[1]}: {percentage_positive:.2%}", emoji
+    else:
+
+        return prediction_label, f"NOT AVAILABLE", emoji
+
+
 
-    return prediction_label, f"{labels[0]}: {percentage_negative:.2%}, Percentage {labels[1]}: {percentage_positive:.2%}", emoji
 
 # Create the Gradio interface
 iface = gr.Interface(