Add application file

app.py

@@ -0,0 +1,37 @@
+from transformers import AutoModelWithLMHead, AutoTokenizer
+import torch
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-medium')
+model = AutoModelWithLMHead.from_pretrained('output-medium')
+
+chat_history_ids = None
+step = 0
+
+
+def predict(input, chat_history_ids=chat_history_ids, step=step):
+    # encode the new user input, add the eos_token and return a tensor in Pytorch
+    new_user_input_ids = tokenizer.encode(
+        input + tokenizer.eos_token, return_tensors='pt')
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat(
+        [chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+
+    # generated a response while limiting the total chat history to 1000 tokens,
+    chat_history_ids = model.generate(
+        bot_input_ids, max_length=1000,
+        pad_token_id=tokenizer.eos_token_id,
+        no_repeat_ngram_size=3,
+        do_sample=True,
+        top_k=100,
+        top_p=0.7,
+        temperature=0.8
+    )
+    step = step + 1
+    output = tokenizer.decode(
+        chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return output
+
+
+gr.Interface(fn=predict, inputs="text", outputs="text").launch(debug=True)