Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import numpy as np
+
+# Load the pre-trained text classification model from Hugging Face
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+def classify_text(text):
+    # Preprocess the text input
+    encoded_text = tokenizer(text, truncation=True, padding=True, return_tensors="pt")
+
+    # Make predictions using the pre-trained model
+    with torch.no_grad():
+        output = model(**encoded_text)
+        logits = output.logits
+        predictions = np.argmax(logits, axis=1)
+
+    # Convert predictions to class labels
+    class_labels = ["positive", "negative"]
+    predicted_labels = [class_labels[i] for i in predictions]
+
+    # Return the predicted labels
+    return predicted_labels
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=classify_text,
+    inputs=gr.inputs.Textbox(label="Enter text to classify:"),
+    outputs=gr.outputs.Label(label="Predicted Label:")
+)
+
+# Launch the Gradio interface
+interface.launch()