Update app.py

app.py

@@ -1,6 +1,22 @@
 import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+
+# Load the trained model
+model = load_model('sentiment_model.h5')
+
+# Load the tokenizer
+with open('tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+# Define the max sequence length (as used during training)
+max_seq_length = 100  # Adjust this based on your training setup
+
+# Sentiment mapping
+sentiment_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
 
-# Define a function to classify sentiment
 def classify_sentiment(text):
     # Preprocess the text (tokenization, padding, etc.)
     text_sequence = tokenizer.texts_to_sequences([text])
@@ -13,14 +29,18 @@ def classify_sentiment(text):
     predicted_label = np.argmax(prediction)
 
     # Map class label to sentiment
-    sentiment_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
     sentiment = sentiment_mapping[predicted_label]
 
     return sentiment
 
-# Define Gradio interface
-input_text = gr.inputs.Textbox(lines=5, label="Enter your text")
-output_sentiment = gr.outputs.Textbox(label="Sentiment")
+# Gradio interface
+interface = gr.Interface(
+    fn=classify_sentiment,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter a sentence..."),
+    outputs="text",
+    title="Sentiment Analysis",
+    description="Enter a sentence to classify its 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()
+if __name__ == "__main__":
+    interface.launch()