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import streamlit as st
import requests
import time
def infer(prompt,
model_name,
max_new_tokens=10,
temperature=0.0,
top_p=1.0,
num_completions=1,
seed=42,):
model_name_map = {
"GPT-JT-6B-v1": "Together-gpt-JT-6B-v1",
}
my_post_dict = {
"type": "general",
"payload": {
"max_tokens": int(max_new_tokens),
"n": int(num_completions),
"temperature": float(temperature),
"top_p": float(top_p),
"model": model_name_map[model_name],
"prompt": [prompt],
"request_type": "language-model-inference",
"stop": None,
"best_of": 1,
"echo": False,
"seed": int(seed),
"prompt_embedding": False,
},
"returned_payload": {},
"status": "submitted",
"source": "dalle",
}
job_id = requests.post("https://planetd.shift.ml/jobs", json=my_post_dict).json()['id']
for i in range(100):
time.sleep(1)
ret = requests.get(f"https://planetd.shift.ml/job/{job_id}", json={'id': job_id}).json()
if ret['status'] == 'finished':
break
return ret['returned_payload']['result']['inference_result'][0]['choices'][0]['text']
st.title("TOMA Application")
col1, col2 = st.columns([1, 3])
with col1:
model_name = st.selectbox("Model", ["GPT-JT-6B-v1"])
max_new_tokens = st.text_input('Max new tokens', "10")
temperature = st.text_input('temperature', "1.0")
top_p = st.text_input('top_p', "1.0")
num_completions = st.text_input('num_completions', "1")
seed = st.text_input('seed', "42")
with col2:
s_example = "Please answer the following question:\n\nQuestion: Where is Zurich?\nAnswer:"
prompt = st.text_area(
"Prompt",
value=s_example,
max_chars=4096,
height=400,
)
generated_area = st.empty()
generated_area.markdown("(Generate here)")
button_submit = st.button("Submit")
if button_submit:
with st.spinner(text="In progress.."):
report_text = infer(
prompt, model_name=model_name, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p,
num_completions=num_completions, seed=seed,
)
generated_area.markdown(report_text)
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