Create app.py

app.py

@@ -0,0 +1,62 @@
+import numpy as np
+import os
+import gradio as gr
+
+os.environ["WANDB_DISABLED"] = "true"
+
+from datasets import load_dataset, load_metric
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    TrainingArguments,
+    logging,
+pipeline
+)
+
+
+
+
+# model_name = 
+
+
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# config = AutoConfig.from_pretrained(model_name)
+
+# pipe = pipeline("text-classification")
+
+# pipe("This restaurant is awesome")
+
+
+
+
+# Question answering pipeline, specifying the checkpoint identifier
+model = AutoModelForSequenceClassification.from_pretrained(
+    pretrained_model_name_or_path= "thak123/Cro-Frida",
+    num_labels=3,
+)
+
+
+analyzer = pipeline(
+
+    "sentiment-analysis", model=model, tokenizer="EMBEDDIA/crosloengual-bert"
+
+)
+def predict_sentiment(x):
+    return analyzer(x)
+
+
+
+
+
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs='text',
+    outputs=['label'],
+    title='Latvian Twitter Sentiment Analysis',
+    examples= ["Es mīlu Tevi","Es ienīstu kafiju"],
+    description='Get the positive/neutral/negative sentiment for the given input.'
+)
+
+interface.launch(inline = False)