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app.py

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+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import random
+device = 'cpu'
+
+def ans(question ):
+    description=''
+    category=''
+    seed = random.randint(1, 10000000)
+    print(f'Seed: {seed}')
+    torch.manual_seed(seed)
+    
+    inp = tokenizer.encode(f'Вопрос: {question}\nОписание: {description}\nОтвет:',return_tensors="pt").to(device)
+    print('question',question)
+    gen = model.generate(inp, do_sample=True, top_p=0.9, temperature=0.86, max_new_tokens=100, repetition_penalty=1.2) #, stop_token="<eos>")
+    
+    gen = tokenizer.decode(gen[0])
+    gen = gen[:gen.index('<eos>') if '<eos>' in gen else len(gen)]
+    gen = gen.split('Ответ:')[1]
+    return gen
+
+
+
+
+
+
+
+# Download checkpoint:
+checkpoint = "its5Q/rugpt3large_mailqa"
+tokenizer =  AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint)
+model = model.eval()
+
+# Gradio
+
+title = "Ответы на главные вопросы жизни, вселенной и вообще"
+description = "t5 large predict activity "
+article = "<p style='text-align: center'><a href='https://github.com/NeuralPushkin/MailRu_Q-A'>Github with fine-tuning ruGPT3large on QA</a></p> Cозданно при поддержке <p style='text-align: center'><a href='https://t.me/lovedeathtransformers'>Love Death Transformers</a></p>"
+
+
+iface = gr.Interface(fn=ans, title=title, description=description, article=article, inputs="text", outputs="text")
+
+if __name__ == "__main__":
+    iface.launch()