Add application file

.gitignore

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+sent.env
+sent

app.py

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+import gradio as gr
+import tensorflow as tf
+from transformers import DistilBertTokenizer
+import numpy as np
+
+# Load the saved model and tokenizer
+model = tf.keras.models.load_model('tf_model.h5')
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+
+import gradio as gr
+# Function to make predictions on new text inputs
+def predict_sentiment(text):
+    # Tokenize and encode the input text
+    encoded_input = tokenizer.encode_plus(
+        text,
+        add_special_tokens=True,
+        max_length=512,
+        pad_to_max_length=True,
+        return_attention_mask=True,
+        truncation=True,
+        return_tensors="tf"
+    )
+
+    # Make predictions
+    output = model(encoded_input)
+    probabilities = tf.nn.softmax(output.logits, axis=1).numpy()[0]
+    predicted_label = np.argmax(probabilities)
+    confidence_score = probabilities[predicted_label]
+
+    # Decode the predicted label
+    label = "positive" if predicted_label == 1 else "negative"
+
+    return label, confidence_score
+
+# Create the Gradio interface
+text_input = gr.components.Textbox(lines=5, label="Enter your text here")
+output_text = gr.components.Textbox(label="Predicted Sentiment")
+
+# Define the Gradio interface
+iface=gr.Interface(fn=predict_sentiment, inputs=text_input, outputs=output_text, title="Sentiment Analysis")
+# Launch the Gradio app
+iface.launch(inline=False)

tf_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f09109d7f8c02a5480a0810c178771b8fabc5e10b067a6fdd6cfc11c74b9655
+size 267955144