增加SSE流式输出

发送请求时，stream参数为1请求流式输出。
发送请求格式如下：
curl -X POST "http://127.0.0.1:8000" \
     -H 'Content-Type: application/json' \
     -d '{"prompt": "你好", "history": [], "stream":1}'

回复按照SSE流式输出格式
data:{
  "response":"你好👋！我是人工智能助手 ChatGLM-6B，很高兴见到你，欢迎问我任何问题。",
  "history":[["你好","你好👋！我是人工智能助手 ChatGLM-6B，很高兴见到你，欢迎问我任何问题。"]],
  "status":200,
  "time":"2023-03-23 21:38:40"
}

api.py

@@ -2,6 +2,7 @@ from fastapi import FastAPI, Request
 from transformers import AutoTokenizer, AutoModel
 import uvicorn, json, datetime
 import torch
+from sse_starlette.sse import EventSourceResponse
 
 DEVICE = "cuda"
 DEVICE_ID = "0"
@@ -18,17 +19,23 @@ def torch_gc():
 app = FastAPI()
 
 
-@app.post("/")
-async def create_item(request: Request):
-    global model, tokenizer
-    json_post_raw = await request.json()
-    json_post = json.dumps(json_post_raw)
-    json_post_list = json.loads(json_post)
-    prompt = json_post_list.get('prompt')
-    history = json_post_list.get('history')
-    max_length = json_post_list.get('max_length')
-    top_p = json_post_list.get('top_p')
-    temperature = json_post_list.get('temperature')
+def predict_stream(tokenizer, prompt, history, max_length, top_p, temperature):
+    for response, history in model.stream_chat(tokenizer, prompt, history, max_length=max_length, top_p=top_p,
+                                               temperature=temperature):
+        now = datetime.datetime.now()
+        time = now.strftime("%Y-%m-%d %H:%M:%S")
+        yield json.dumps({
+            'response': response,
+            'history': history,
+            'status': 200,
+            'time': time
+        })
+    log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
+    print(log)
+    return torch_gc()
+
+
+def predict(tokenizer, prompt, history, max_length, top_p, temperature):
     response, history = model.chat(tokenizer,
                                    prompt,
                                    history=history,
@@ -48,6 +55,25 @@ async def create_item(request: Request):
     torch_gc()
     return answer
 
+@app.post("/")
+async def create_item(request: Request):
+    global model, tokenizer
+    json_post_raw = await request.json()
+    json_post = json.dumps(json_post_raw)
+    json_post_list = json.loads(json_post)
+    prompt = json_post_list.get('prompt')
+    history = json_post_list.get('history')
+    max_length = json_post_list.get('max_length')
+    top_p = json_post_list.get('top_p')
+    temperature = json_post_list.get('temperature')
+    stream = json_post_list.get('stream')
+    if stream:
+        res = predict_stream(tokenizer, prompt, history, max_length, top_p, temperature)
+        return EventSourceResponse(res)
+    else:
+        answer = predict(tokenizer, prompt, history, max_length, top_p, temperature)
+        return answer
+
 
 if __name__ == '__main__':
     tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)