initial commit

Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,12 +1,11 @@
 ---
-title: API
-emoji: 👀
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
+title: Fastapi Hello World
+emoji: 📉
+colorFrom: green
+colorTo: green
+sdk: docker
 pinned: false
+duplicated_from: souljoy/my_api
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from fastapi import FastAPI, status
+from fastapi.responses import HTMLResponse
+from pydantic import BaseModel
+from fastapi.responses import JSONResponse, StreamingResponse
+import requests
+import json
+import openai
+import time
+
+
+class Text(BaseModel):
+    content: str = ""
+
+
+app = FastAPI()
+key = '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'
+openai.api_key = key
+headers = {
+    'Content-Type': 'application/json',
+    'Authorization': 'Bearer ' + key
+}
+
+
+@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。"
+        }
+    ]
+    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("/sale")
+def sale_ep(content: Text = None):
+    url = 'https://api.openai.com/v1/chat/completions'
+    messages = [
+        {
+            "role": "system",
+            "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。"
+        }
+    ]
+    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("/chatgpt")
+def chat_gpt_ep(content: Text = None):
+    url = 'https://api.openai.com/v1/chat/completions'
+    obj = json.loads(content.content)
+    data = {
+        "model": "gpt-3.5-turbo",
+        "messages": obj['messages']
+    }
+    print("data = \n", data)
+    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)
+
+
+async def chat_gpt_stream_fun(content: Text = None):
+    start_time = time.time()
+    obj = json.loads(content.content)
+    response = openai.ChatCompletion.create(
+        model='gpt-3.5-turbo',
+        messages=obj['messages'],
+        stream=True,  # this time, we set stream=True
+    )
+    # create variables to collect the stream of chunks
+    collected_chunks = []
+    collected_messages = []
+    # iterate through the stream of events
+    for chunk in response:
+        chunk_time = time.time() - start_time  # calculate the time delay of the chunk
+        collected_chunks.append(chunk)  # save the event response
+        chunk_message = chunk['choices'][0]['delta']  # extract the message
+        collected_messages.append(chunk_message)  # save the message
+        print(f"Message received {chunk_time:.2f} seconds after request: {chunk_message}")  # print the delay and text
+        full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
+        print(f"Full conversation received: {full_reply_content}")
+        content = {'content': full_reply_content}
+        print('content:', content)
+        yield json.dumps(content) + '\n'
+
+
+@app.post("/chatgptstream", status_code=status.HTTP_200_OK)
+async def get_random_numbers(content: Text = None):
+    return StreamingResponse(chat_gpt_stream_fun(content), media_type='application/json')
+
+
+@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())
+
+
+@app.post("/create_image")
+def create_image_ep(content: Text = None):
+    url = 'https://api.openai.com/v1/images/generations'
+    obj = json.loads(content.content)
+    data = {
+        "prompt": obj["prompt"],
+        "n": obj["n"],
+        "size": obj["size"]
+    }
+    print("data = \n", data)
+    result = requests.post(url=url,
+                           data=json.dumps(data),
+                           headers=headers
+                           )
+    return JSONResponse(content=result.json())

index.html

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+<html>
+    <!-- Title -->
+    <head>
+        <title>FastAPI Hello World</title>
+    </head>
+
+    <!-- Stylesheet -->
+    <style>
+        body {
+            font-family: Arial, Helvetica, sans-serif;
+            font-size: 16px;
+            line-height: 1.5;
+            margin: 0;
+            padding: 0;
+        }
+        h1 {
+            font-size: 2em;
+            margin: 0;
+            padding: 0;
+            /* Center */
+            text-align: center;
+        }
+        h3 {
+            margin: 0;
+            padding: 0;
+            /* Center */
+            text-align: center;
+        }
+
+    </style>
+
+    <!-- Body -->
+    <body>
+       
+
+    </body>
+</html>

requirements.txt

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+flask
+fastapi==0.74.*
+requests==2.27.*
+uvicorn[standard]==0.17.*
+openai