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LSTM_senti.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4bb6f3953f90b859667035fcc4ff3686809c5fc8db2f1ca00a70411581deb87c
+size 9402280

app.py

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+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)
+