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import streamlit as st
import requests
import time
def infer(prompt, max_new_tokens=10, temperature=0.0, top_p=1.0):
my_post_dict = {
"type": "general",
"payload": {
"max_tokens": max_new_tokens,
"n": 1,
"temperature": float(temperature),
"top_p": float(top_p),
"model": "Together-gpt-J-6B-ProxAdam-50x",
"prompt": [prompt],
"request_type": "language-model-inference",
"stop": None,
"best_of": 1,
"echo": False,
"seed": 42,
"prompt_embedding": False,
},
"returned_payload": {},
"status": "submitted",
"source": "dalle",
}
res = requests.post("https://planetd.shift.ml/jobs", json=my_post_dict).json()
job_id = res['id']
while True:
ret = requests.get(f"https://planetd.shift.ml/job/{job_id}", json={'id': job_id}).json()
if ret['status'] == 'finished':
break
time.sleep(1)
return ret['returned_payload']['result']['inference_result'][0]['choices'][0]['text']
st.title("TOMA Application")
s_example = "Please answer the following question:\n\nQuestion: Where is Zurich?\nAnswer:"
prompt = st.text_area(
"Prompt",
value=s_example,
max_chars=1000,
height=400,
)
generated_area = st.empty()
generated_area.markdown("(Generate here)")
button_submit = st.button("Submit")
max_new_tokens = st.number_input('Max new tokens', 1, 1024, 10)
temperature = st.number_input('temperature', 0.0, 10.0, 0.0, step=0.1, format="%.2f")
top_p = st.number_input('top_p', 0.0, 1.0, 1.0, step=0.1, format="%.2f")
if button_submit:
with st.spinner(text="In progress.."):
report_text = infer(prompt, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p)
generated_area.markdown(report_text) |