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import os
import numpy as np
import pandas as pd
import openai
from haystack.schema import Document
import streamlit as st
from tenacity import retry, stop_after_attempt, wait_random_exponential
from huggingface_hub import InferenceClient
# Get openai API key
hf_token = os.environ["HF_API_KEY"]
# define a special function for putting the prompt together (as we can't use haystack)
def get_prompt(context, label):
base_prompt="Summarize the following context efficiently in bullet points, the less the better - but keep concrete goals. \
Summarize only elements of the context that address vulnerability of "+label+" to climate change. \
If there is no mention of "+label+" in the context, return nothing. \
Do not include an introduction sentence, just the bullet points as per below. \
Formatting example: \
- Bullet point 1 \
- Bullet point 2 \
"
prompt = base_prompt+"; Context: "+context+"; Answer:"
return prompt
# # exception handling for issuing multiple API calls to openai (exponential backoff)
# @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
# def completion_with_backoff(**kwargs):
# return openai.ChatCompletion.create(**kwargs)
# construct query, send to HF API and process response
def run_query(context, label):
'''
For non-streamed completion, enable the following 2 lines and comment out the code below
'''
chatbot_role = """You are an analyst specializing in climate change impact assessments and producing insights from policy documents."""
messages = [{"role": "system", "content": chatbot_role},{"role": "user", "content": get_prompt(context, label)}]
# Initialize the client, pointing it to one of the available models
client = InferenceClient("meta-llama/Meta-Llama-3.1-405B-Instruct", token = hf_token)
chat_completion = client.chat.completions.create(
messages=messages,
stream=True
)
# iterate through the streamed output
report = []
res_box = st.empty()
for chunk in chat_completion:
# extract the object containing the text (totally different structure when streaming)
chunk_message = chunk.choices[0].delta
# test to make sure there is text in the object (some don't have)
if 'content' in chunk_message:
report.append(chunk_message['content']) # extract the message
# add the latest text and merge it with all previous
result = "".join(report).strip()
# res_box.success(result) # output to response text box
res_box.success(result)
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