Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline, BertForSequenceClassification, BertTokenizer
+import torch
+
+# Load the model and tokenizer
+model_path = 'D:\Study\university\Fourth Year\second semster\NLP\final exam\saved_model'
+model = BertForSequenceClassification.from_pretrained(model_path)
+tokenizer = BertTokenizer.from_pretrained(model_path)
+
+# Create a pipeline for inference
+nlp_pipeline = pipeline('text-classification', model=model, tokenizer=tokenizer)
+
+# Gradio app function
+def classify_text(input_text):
+    if input_text.strip() == "":
+        return "Please enter some text."
+    result = nlp_pipeline(input_text)
+    label = result[0]['label']
+    score = result[0]['score']
+
+    # Map labels to 1 for positive and 0 for negative
+    label_map = {'LABEL_0': " Negative ", 'LABEL_1': "  Postive "}
+    mapped_label = label_map[label]
+
+    return f"Prediction: {mapped_label}, Confidence Score: {score:.4f}"
+
+# Gradio interface
+iface = gr.Interface(fn=classify_text, inputs="text", outputs="text", title="Text Classification App",
+                     description="Classify text as Positive (1) or Negative (0)")
+
+# Launch the Gradio app
+iface.launch()