Update app.py

app.py

@@ -1,12 +1,23 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
+# Load pre-trained tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
+model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
 
-model = AutoModelForCausalLM.from_pretrained(model_id)
+def predict_sentiment(input_text):
+    # Tokenization
+    inputs = tokenizer(input_text, return_tensors='pt')
+    
+    # Prediction
+    outputs = model(**inputs)
+    probabilities = outputs[0][0].detach().numpy()
+    labels = ['Negative', 'Positive']
+    predicted_label = labels[probabilities.argmax()]
 
-text = "Hello my name is"
-inputs = tokenizer(text, return_tensors="pt")
+    return {"Text": input_text, "Sentiment": predicted_label}
 
-outputs = model.generate(**inputs, max_new_tokens=20)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+iface = gr.Interface(predict_sentiment, input_type="text", output_types=["text"],
+                      input_label="Enter Text", output_label="Predicted Sentiment")
+
+iface.launch()