Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import pickle
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# Load model from Hugging Face Hub
+model_path = hf_hub_download(repo_id="i0xs0/Sentiment_Analysis_DeepLr", filename="AC-BiLSTM_Model.h5")
+model = tf.keras.models.load_model(model_path)
+
+# Load tokenizer from Hugging Face Hub
+tokenizer_path = hf_hub_download(repo_id="i0xs0/Sentiment_Analysis_DeepLr", filename="tokenizer_AC-BiLSTM.pkl")
+with open(tokenizer_path, "rb") as handle:
+    tokenizer = pickle.load(handle)
+
+# Define prediction function
+def predict_sentiment(text, max_seq_length=100):
+    sequences = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(sequences, maxlen=max_seq_length, padding='post', truncating='post')
+    prediction = model.predict(padded_sequence)[0]
+    sentiment_class = np.argmax(prediction)
+    sentiment_map = {0: 'negative', 1: 'neutral', 2: 'positive'}
+    sentiment_label = sentiment_map[sentiment_class]
+    confidence = float(prediction[sentiment_class])
+    return f"Sentiment: {sentiment_label} (Confidence: {confidence:.2f})"
+
+# Gradio UI
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs="text",
+    outputs="text",
+    title="LSTM Sentiment Analysis",
+    description="Enter text and get a sentiment prediction (Positive, Neutral, Negative) using an LSTM model."
+)
+
+iface.launch()