theme

app.py

@@ -1,39 +1,41 @@
-import tensorflow as tf
-import pickle
-import gradio as gr
-import numpy as np
-import json
-
-
-with open('tokenizer.json') as file:
-    tokenizer_data = file.read()
-    tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_data)
-
-         
-def Infernce_Pipe(text,max_length = 100):
-    model = tf.keras.models.load_model("LSTM_senti.h5")   
-
-    sequences = tokenizer.texts_to_sequences([text])
-    
-    padded = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post')
-   
-    pred = model.predict(padded)
-    predicted_index = np.argmax(pred)
-    # Define the label mapping
-    labels = ['Negative', 'Neutral', 'Positive']
-
-    # Map index to label
-    predicted_label = labels[predicted_index]
-
-    return predicted_label
-
-interface = gr.Interface(
-                            fn=Infernce_Pipe,  
-                            inputs=gr.Textbox(placeholder="Enter text here..."),
-                            outputs=gr.Text(label="Prediction"),
-                            title="Sentiment Analysis on Customer Review",
-                            description="Enter a review to get its sentiment classification (Negative, Neutral, Positive)."
-                        )
-
-interface.launch(share=True)
-
+import tensorflow as tf
+import pickle
+import gradio as gr
+import numpy as np
+import json
+
+
+with open('tokenizer.json') as file:
+    tokenizer_data = file.read()
+    tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_data)
+
+         
+def Infernce_Pipe(text,max_length = 100):
+    model = tf.keras.models.load_model("LSTM_senti.h5")   
+
+    sequences = tokenizer.texts_to_sequences([text])
+    
+    padded = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post')
+   
+    pred = model.predict(padded)
+    predicted_index = np.argmax(pred)
+    # Define the label mapping
+    labels = ['Negative', 'Neutral', 'Positive']
+
+    # Map index to label
+    predicted_label = labels[predicted_index]
+
+    return predicted_label
+
+interface = gr.Interface(
+    fn=Infernce_Pipe,  
+    inputs=gr.Textbox(placeholder="Enter text here..."),
+    outputs=gr.Text(label="Prediction"),
+    title="Sentiment Analysis on Customer Review",
+    description="Enter a review to get its sentiment classification (Negative, Neutral, Positive). \n\n **If you find this model helpful, please like it using the button above in the navigation bar!**",
+    theme="huggingface"
+)
+
+interface.launch(share=True)
+
+