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import openai | |
import gradio as gr | |
import os | |
#OpenAi call | |
def gpt3(texts): | |
openai.api_key = os.environ["Secret"] | |
response = openai.Completion.create( | |
engine="text-davinci-002", | |
prompt= texts, | |
temperature=0, | |
max_tokens=750, | |
top_p=1, | |
frequency_penalty=0.0, | |
presence_penalty=0.0, | |
stop = (";", "/*", "</code>") | |
) | |
x = response.choices[0].text | |
return x | |
# Function to elicit sql response from model | |
def greet(prompt): | |
food= (f'''Here we will share a best guess for the predominant ingredients in the following food /*Food: {prompt}*/ \n β-Here is a list of the ingredients in the food item:\n''') | |
ingred = gpt3(food) | |
total = (f'''Expert one sentence summary and rating (on a scale of 1/10, with 10 having no impact) of the food and ingredients comparative climate impact, from rationalist climate perspective /*Food, ingredients: {prompt},{ingred}*/ \n β-Impact Summary and Climate Score:\n''') | |
score = gpt3(total) | |
return score | |
#Code to set up Gradio UI | |
iface = gr.Interface(greet, inputs = ["text"], outputs = ["text"],title="Food Impact Analysis", description="Get a climate analysis back from any food!") | |
iface.launch() |