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import gradio as gr
from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFaceHub
template_by_step = """Question: {question}
Answer: Let's think step by step."""
def run(
question: gr.Textbox = None,
repo_id: gr.Dropdown = "google/flan-t5-xxl",
temperature: gr.Slider = 0.5,
max_length: gr.Slider = 64,
by_steq: gr.Checkbox = False,
):
template = template_by_step if by_steq else "{question}"
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = HuggingFaceHub(
repo_id=repo_id,
model_kwargs={"temperature": temperature, "max_length": max_length}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
result = llm_chain.run(question)
print(result)
return result
inputs = [
gr.Textbox(label="Question"),
gr.Dropdown(["google/flan-t5-xxl", "google/flan-t5-base"],
value="google/flan-t5-xxl", label="Model", allow_custom_value=True),
gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="Temperature"),
gr.Slider(20, 1000, value=64, label="Max Length"),
gr.Checkbox(label="Think step by step", value=False),
]
examples = [
["What is the capital of France?"],
["What's the Earth total population?"],
["Who won the FIFA World Cup in the year 1994?"],
["What NFL team won the Super Bowl in the year Justin Bieber was born?"],
["Translate the following to French: There are so many plans"],
["Write an article to introduce machine learning"],
]
title = "Langchain w/ HF Models"
gr.Interface(
fn=run,
inputs=inputs,
outputs='label',
title=title,
examples=examples,
).launch()
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