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from fuzzy_json import loads
from half_json.core import JSONFixer
from together import Together
from retry import retry
import re
from dotenv import load_dotenv
import os
from fastapi import FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
# Retrieve environment variables
TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")
SysPromptDefault = "You are an expert AI, complete the given task. Do not add any additional comments."
SysPromptList = "You are now in the role of an expert AI who can extract structured information from user request. All elements must be in double quotes. You must respond ONLY with a valid python List. Do not add any additional comments."
@retry(tries=3, delay=1)
def together_response(message, model = "meta-llama/Llama-3-8b-chat-hf", SysPrompt = SysPromptDefault):
client = Together(api_key=TOGETHER_API_KEY)
messages=[{"role": "system", "content": SysPrompt},{"role": "user", "content": message}]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=0.2,
)
return response.choices[0].message.content
def json_from_text(text):
"""
Extracts JSON from text using regex and fuzzy JSON loading.
"""
match = re.search(r'\{[\s\S]*\}', text)
if match:
json_out = match.group(0)
else:
json_out = text
try:
# Using fuzzy json loader
return loads(json_out)
except Exception:
# Using JSON fixer/ Fixes even half json/ Remove if you need an exception
fix_json = JSONFixer()
return loads(fix_json.fix(json_out).line)
def generate_topics(user_input,num_topics,previous_query):
prompt = f"""create a list of {num_topics} subtopics to follow for conducting {user_input} in the context of {previous_query}, RETURN VALID PYTHON LIST"""
response_topics = together_response(prompt, model = "meta-llama/Llama-3-8b-chat-hf", SysPrompt = SysPromptList)
subtopics = json_from_text(response_topics)
return subtopics
# Define the app
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Create a Pydantic model to handle the input data
class TopicInput(BaseModel):
user_input: str
num_topics: int
previous_query: str
@app.get("/",tags=["Home"])
def api_home():
return {'detail': 'Welcome to FastAPI Subtopics API! /n visit https://pvanand-generate-subtopics.hf.space/docs to test'}
@app.post("/generate_topics/")
async def create_topics(input: TopicInput):
topics = generate_topics(input.user_input, input.num_topics, input.num_topics.previous_query)
return {"topics": topics}
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