Update app.py

app.py

@@ -1,33 +1,31 @@
 import gradio as gr
 import pickle
-import numpy as np
-import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
 
-
-
-
-# Specify the filename for your PKL file
+# Load the model and vectorizer from the pickle file
 filename = 'sentiment_model.pkl'
 
-# Open the file in write binary ('wb') mode and save the model using pickle.dump()
 with open(filename, 'rb') as file:
-    nb_classifier=pickle.load(file)
+    loaded_objects = pickle.load(file)
+    nb_classifier = loaded_objects['model']  # Trained model
+    vectorizer = loaded_objects['vectorizer']  # Pre-trained vectorizer
 
+# Define the prediction function
 def predict_sentiment(text_input):
-    text_vector = vectorizer.transform([text])
-    prediction = nb_classifier.predict(text_vector)
-    return "Positive" if prediction == 1 else "Negative"
-
-# Test sample
-
+    try:
+        text_vector = vectorizer.transform([text_input])  # Transform input text
+        prediction = nb_classifier.predict(text_vector)  # Predict sentiment
+        return "Positive" if prediction[0] == 1 else "Negative"
+    except Exception as e:
+        return f"Error: {e}"
+
+# Create the Gradio interface
 with gr.Blocks(theme="compact") as demo:
-    gr.Markdown("predict_sentiment")
-    #chatbot_window = gr.Chatbot(label="")
+    gr.Markdown("## Sentiment Analysis Predictor")
     with gr.Row():
-        # Corrected 'lable' to 'label'
-        text_input = gr.Textbox(label="Write the Review", placeholder="Enter your sentiment", show_label=False)
-        # Create a Textbox to display the output
+        text_input = gr.Textbox(label="Write the Review", placeholder="Enter your sentiment")
         output_box = gr.Textbox(label="Sentiment Prediction")
-        # Submit the input to the function and display the output in the output_box
-        text_input.submit(fn=predict_sentiment, inputs=text_input, outputs=output_box)
-demo.launch(share=True)
+    text_input.submit(fn=predict_sentiment, inputs=text_input, outputs=output_box)
+
+# Launch the Gradio app
+demo.launch(share=True)