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from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from pydantic import BaseModel
from fastapi.responses import JSONResponse
import requests
import json
class Text(BaseModel):
content: str = ""
app = FastAPI()
headers = {
'Content-Type': 'application/json',
'Authorization': 'Bearer ' + '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'
}
@app.get("/")
def home():
html_content = open('index.html').read()
return HTMLResponse(content=html_content, status_code=200)
@app.post("/qa_maker")
def sentiment_analysis_ep(content: Text = None):
url = 'https://api.openai.com/v1/chat/completions'
prompt = '根据下面的文章，生成的“问题和回答”QA对，大于5个，以一行一个json格式（{“question”:"xxx","answer":"xxx"}）生成：\n'
messages = [{"role": "user", "content": prompt + content.content}]
data = {
"model": "gpt-3.5-turbo",
"messages": messages
}
print("messages = \n", messages)
result = requests.post(url=url,
data=json.dumps(data),
headers=headers
)
res = str(result.json()['choices'][0]['message']['content']).strip()
print('res:', res)
res = {'content': res}
return JSONResponse(content=res)
@app.post("/chatpdf")
def chat_pdf_ep(content: Text = None):
url = 'https://api.openai.com/v1/chat/completions'
messages = [
{
"role": "system",
"content": "你是一个有用的助手，可以使用文章内容准确地回答问题。使用提供的文章来形成你的答案，但避免逐字复制文章。尽可能使用自己的话。准确、有用、简洁、清晰。"
}
]
obj = json.loads(content.content)
messages.append({"role": "system", "content": "文章内容：\n"+obj['doc']})
history = obj['history']
for his in history:
messages.append({"role": "user", "content": his[0]})
messages.append({"role": "assistant", "content": his[1]})
messages.append({"role": "user", "content": obj['question']})
data = {
"model": "gpt-3.5-turbo",
"messages": messages
}
print("messages = \n", messages)
result = requests.post(url=url,
data=json.dumps(data),
headers=headers
)
res = str(result.json()['choices'][0]['message']['content']).strip()
content = {'content': res}
print('content:', content)
return JSONResponse(content=content)
@app.post("/embeddings")
def embeddings_ep(content: Text = None):
url = 'https://api.openai.com/v1/embeddings'
data = {
"model": "text-embedding-ada-002",
"input": content.content
}
result = requests.post(url=url,
data=json.dumps(data),
headers=headers
)
return JSONResponse(content=result.json())
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