Update .venv/PythonProjectFile1.py

.venv/PythonProjectFile1.py

@@ -1,10 +1,20 @@
-from transformers import pipeline
+from transformers import pipeline, DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
+import gradio as gr
 
-myPipe = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+#generator = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
 
-inputText = "Dr. Davis makes me want to run out of the room laughing."
-inputText = input("Input the text you want classified:")
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
 
-result = myPipe(inputText)
+def classify_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
 
-print(result)
+    predicted_class_id = logits.argmax().item()
+    return model.config.id2label[predicted_class_id]
+
+# Create a Gradio interface
+iface = gr.Interface(fn=classify_text, inputs="text", outputs="text")
+iface.launch()