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from fastapi import FastAPI |
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from fastapi.responses import HTMLResponse |
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from pydantic import BaseModel |
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from fastapi.responses import JSONResponse |
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import requests |
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import json |
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class Text(BaseModel): |
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content: str = "" |
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app = FastAPI() |
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headers = { |
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'Content-Type': 'application/json', |
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'Authorization': 'Bearer ' + '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' |
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} |
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@app.get("/") |
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def home(): |
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html_content = open('index.html').read() |
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return HTMLResponse(content=html_content, status_code=200) |
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@app.post("/qa_maker") |
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def sentiment_analysis_ep(content: Text = None): |
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url = 'https://api.openai.com/v1/chat/completions' |
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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' |
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messages = [{"role": "user", "content": prompt + content.content}] |
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data = { |
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"model": "gpt-3.5-turbo", |
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"messages": messages |
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} |
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print("messages = \n", messages) |
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result = requests.post(url=url, |
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data=json.dumps(data), |
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headers=headers |
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) |
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res = str(result.json()['choices'][0]['message']['content']).strip() |
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print('res:', res) |
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res = {'content': res} |
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return JSONResponse(content=res) |
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@app.post("/chatpdf") |
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def chat_pdf_ep(content: Text = None): |
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url = 'https://api.openai.com/v1/chat/completions' |
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messages = [ |
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{ |
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"role": "system", |
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"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。使用提供的文章来生成你的答案,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。" |
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} |
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] |
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obj = json.loads(content.content) |
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messages.append({"role": "system", "content": "文章内容:\n" + obj['doc']}) |
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history = obj['history'] |
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for his in history: |
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messages.append({"role": "user", "content": his[0]}) |
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messages.append({"role": "assistant", "content": his[1]}) |
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messages.append({"role": "user", "content": obj['question']}) |
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data = { |
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"model": "gpt-3.5-turbo", |
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"messages": messages |
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} |
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print("messages = \n", messages) |
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result = requests.post(url=url, |
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data=json.dumps(data), |
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headers=headers |
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) |
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res = str(result.json()['choices'][0]['message']['content']).strip() |
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content = {'content': res} |
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print('content:', content) |
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return JSONResponse(content=content) |
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@app.post("/sale") |
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def sale_ep(content: Text = None): |
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url = 'https://api.openai.com/v1/chat/completions' |
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messages = [ |
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{ |
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"role": "system", |
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"content": "你是小鹏汽车资深的销售,可以参考文章内容来生成你的答案,但避免逐字复制文章,尽可能使用自己的话,突出小鹏汽车的优点、说服用户来店试驾或购买小鹏汽车。准确、有用、有销售技巧、口语化、有亲和力。" |
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} |
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] |
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obj = json.loads(content.content) |
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messages.append({"role": "system", "content": "文章内容:\n" + obj['doc']}) |
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history = obj['history'] |
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for his in history: |
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messages.append({"role": "user", "content": his[0]}) |
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messages.append({"role": "assistant", "content": his[1]}) |
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messages.append({"role": "user", "content": obj['question']}) |
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data = { |
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"model": "gpt-3.5-turbo", |
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"messages": messages |
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} |
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print("messages = \n", messages) |
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result = requests.post(url=url, |
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data=json.dumps(data), |
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headers=headers |
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) |
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res = str(result.json()['choices'][0]['message']['content']).strip() |
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content = {'content': res} |
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print('content:', content) |
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return JSONResponse(content=content) |
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@app.post("/chatgpt") |
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def chat_gpt_ep(content: Text = None): |
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url = 'https://api.openai.com/v1/chat/completions' |
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obj = json.loads(content.content) |
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data = { |
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"model": "gpt-3.5-turbo", |
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"messages": obj['messages'] |
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} |
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print("data = \n", data) |
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result = requests.post(url=url, |
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data=json.dumps(data), |
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headers=headers |
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) |
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res = str(result.json()['choices'][0]['message']['content']).strip() |
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content = {'content': res} |
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print('content:', content) |
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return JSONResponse(content=content) |
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@app.post("/embeddings") |
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def embeddings_ep(content: Text = None): |
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url = 'https://api.openai.com/v1/embeddings' |
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data = { |
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"model": "text-embedding-ada-002", |
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"input": content.content |
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} |
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result = requests.post(url=url, |
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data=json.dumps(data), |
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headers=headers |
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) |
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return JSONResponse(content=result.json()) |
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@app.post("/create_image") |
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def create_image_ep(content: Text = None): |
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url = 'https://api.openai.com/v1/images/generations' |
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obj = json.loads(content.content) |
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data = { |
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"prompt": obj["prompt"], |
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"n": obj["n"], |
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"size": obj["size"] |
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} |
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print("data = \n", data) |
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result = requests.post(url=url, |
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data=json.dumps(data), |
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headers=headers |
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) |
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return JSONResponse(content=result.json()) |
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