Create app.py

app.py

@@ -0,0 +1,42 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import streamlit as st
+
+st.title("Japanese Text Generation")
+
+tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b-instruction-ppo", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-neox-3.6b-instruction-ppo")
+
+logs = []
+
+def generate_text(input_prompt):
+    token_ids = tokenizer.encode(input_prompt, add_special_tokens=False, return_tensors="pt")
+
+    with torch.no_grad():
+        output_ids = model.generate(
+            token_ids.to("cpu"),
+            do_sample=True,
+            max_new_tokens=128,
+            temperature=0.7,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.pad_token_id,
+            bos_token_id=tokenizer.bos_token_id,
+            eos_token_id=tokenizer.eos_token_id
+        )
+
+    generated_text = tokenizer.decode(output_ids.tolist()[0][token_ids.size(1):])
+    generated_text = generated_text.replace("<NL>", "\n")
+    return generated_text
+
+prompt = st.text_area("Enter the prompt:")
+
+if st.button("Submit"):
+    generated_output = generate_text(prompt)
+    logs.append((prompt, generated_output))
+
+for log in logs:
+    with st.beta_container():
+        st.write("---")
+        st.subheader("Time: {}".format(log[0]))
+        st.write("**Input**: {}".format(log[0]))
+        st.write("**Output**: {}".format(log[1]))