new version with flask

app.py

@@ -1,16 +1,24 @@
-import streamlit as st
+#from fastapi import FastAPI
+from flask import Flask
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import numpy as np 
 
+#app = FastAPI()
+app = Flask(__name__)
 
 model = GPT2LMHeadModel.from_pretrained('gpt2')
 tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
 
-def score(tokens_tensor):
+@app.get("/score/<text>")
+def score(text):
+    tokens_tensor = tokenizer.encode( text, add_special_tokens=False, return_tensors="pt")
     loss=model(tokens_tensor, labels=tokens_tensor)[0]
-    return np.exp(loss.cpu().detach().numpy())
+    print(text,loss)
+    return str(np.exp(loss.cpu().detach().numpy()))
 
-sentence = st.text_input("Enter the sentence","this is a well formed sentence.")
-tokens_tensor = tokenizer.encode( sentence, add_special_tokens=False, return_tensors="pt")
-res = score(tokens_tensor)
-st.write('score is', res)
+#sentence = st.text_input("Enter the sentence","this is a well formed sentence.")
+#res = score(sentence)
+#st.write('score is', res)
+
+if __name__ == "__main__":
+    app.run(debug=True)

requirements.txt

@@ -1,5 +1,4 @@
 torch
 numpy
 transformers
-streamlit
-fastapi
+Flask==2.2.2