Create app.py

app.py

@@ -0,0 +1,40 @@
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import numpy as np
+from scipy.special import softmax
+
+MODEL = "Davlan/naija-twitter-sentiment-afriberta-large"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+
+# PT
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+
+
+def get_senti(text):
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+    
+    id2label = {0:"positive✅", 1:"neutral😐", 2:"negative❌"}
+    
+    ranking = np.argsort(scores)
+    ranking = ranking[::-1]
+    out = []
+    for i in range(scores.shape[0]):
+        l = id2label[ranking[i]]
+        s = scores[ranking[i]]
+        out.append(f"{i+1}) {l} {np.round(float(s), 4)}")
+    return "\n".join(out)
+
+
+
+import gradio as gr
+
+
+demo = gr.Interface(
+    fn=get_senti,
+    inputs=gr.Textbox(lines=2, placeholder="Enter Words Here..."),
+    outputs="text",
+)
+demo.launch()