Upload app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from transformers import pipeline
+from transformers import pipeline
+import re
+
+dante = pipeline('text-generation',model='./model', tokenizer='GroNLP/gpt2-small-italian-embeddings')
+
+def grammatical_cleaning(generated: str) -> str:
+	generated = re.sub("\.[^\s]",". ", generated)
+	generated = re.sub("\,[^\s]",", ", generated)
+	generated = re.sub("\;[^\s]","; ", generated)
+	generated = re.sub("\:[^\s]",": ", generated)
+	generated = re.sub("\![^\s]","! ", generated)
+	generated = list(generated)
+	for n in range(len(generated)-2):
+		if generated[n]=="." or generated[n]=="?":
+			if generated[n+1].islower() and generated[n+1].isalpha():
+				generated[n+1] = generated[n+1].upper()
+			elif generated[n+2].islower() and generated[n+2].isalpha():
+				generated[n+2] = generated[n+2].upper()
+	return ''.join(generated)
+
+def get_text(input):
+	generated = dante(input, max_length=128)[0]['generated_text']
+	generated = grammatical_cleaning(generated)
+	return generated
+
+inp = input()
+print(get_text(inp))
+
+#iface = gr.Interface(fn=get_text, inputs="text", outputs="text")
+#iface.launch()