add sentiment

app.py

@@ -0,0 +1,23 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load pre-trained model and tokenizer
+model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def predict(text):
+    # Tokenize input text
+    inputs = tokenizer(text, return_tensors="pt")
+
+    # Get model's prediction
+    outputs = model(**inputs)
+
+    # Get predicted class index
+    predicted_class_idx = outputs.logits.argmax(-1).item()
+
+    # Return predicted class
+    return model.config.id2label[predicted_class_idx]
+
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+iface.launch(share=True)

flagged/log.csv

@@ -0,0 +1,2 @@
+name,output,flag,username,timestamp
+John,Hello John!!,,,2024-02-16 12:12:07.583126