Update app.py

app.py

@@ -1,24 +1,10 @@
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import Tokenizer
 import gradio as gr
 
-# Load the trained model
-model_path = "/content/drive/MyDrive/Twitter_data/sentiment_analysis_model.h5"
-model = tf.keras.models.load_model(model_path)
-
-# Load the tokenizer
-tokenizer_path = "/content/drive/MyDrive/Twitter_data/tokenizer.pickle"
-with open(tokenizer_path, 'rb') as handle:
-    tokenizer = pickle.load(handle)
-
-# Define a function for sentiment classification
+# Define a function to classify sentiment
 def classify_sentiment(text):
-    # Preprocess the text
+    # Preprocess the text (tokenization, padding, etc.)
     text_sequence = tokenizer.texts_to_sequences([text])
-    padded_sequence = pad_sequences(text_sequence, maxlen=100)
+    padded_sequence = pad_sequences(text_sequence, maxlen=max_seq_length)
 
     # Make prediction using the trained model
     prediction = model.predict(padded_sequence)
@@ -32,6 +18,9 @@ def classify_sentiment(text):
 
     return sentiment
 
-# Create Gradio interface
-iface = gr.Interface(fn=classify_sentiment, inputs="text", outputs="text", title="Sentiment Analysis", description="Enter a sentence to classify its sentiment.")
-iface.launch()
+# Define Gradio interface
+input_text = gr.inputs.Textbox(lines=5, label="Enter your text")
+output_sentiment = gr.outputs.Textbox(label="Sentiment")
+
+# Launch Gradio app
+gr.Interface(fn=classify_sentiment, inputs=input_text, outputs=output_sentiment, title="Sentiment Analysis", description="Enter a text to classify its sentiment.").launch()