Create ai.py

ai.py

@@ -0,0 +1,40 @@
+from flask import Flask, request ,jsonify
+from gevent import pywsgi
+import json
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+pipeline = pipeline(Tasks.faq_question_answering, 'damo/nlp_faq-question-answering_multilingual-base')
+
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+from modelscope import GenerationConfig
+tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.1',trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.1',device_map="auto", trust_remote_code=True).eval()
+model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat",revision = 'v1.0.1', trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
+
+
+app = Flask(__name__)
+@app.route('/nlp_faq', methods=['GET', 'POST'])
+def nlp_faq():
+    if request.method == 'GET':
+        input = request.args.get('input')
+    else:
+         input = request.json.get('input')
+
+    print(input)
+    output = pipeline(input)
+    return output
+
+@app.route('/qwen', methods=['get','post'])
+def qwen():
+    if request.method == 'GET':
+        input = request.args.get('input')
+    else:
+        input = request.json.get('input')
+    response, history = model.chat(tokenizer,input, history=None)
+    print(response)
+    return response
+
+
+server = pywsgi.WSGIServer(('127.0.0.1', 5001), app)
+server.serve_forever()