add single_app()

paddlehub/serving/app_single.py

@@ -23,39 +23,7 @@ import base64
 import logging
 
 
-def get_img_output(module, base64_head, results):
-    if module.type.startswith("CV"):
-        if "semantic-segmentation" in module.type:
-            output_file = results[0].get("processed", None)
-            if output_file is not None and os.path.exists(output_file):
-                with open(output_file, "rb") as fp:
-                    output_img_base64 = base64.b64encode(fp.read())
-                os.remove(output_file)
-                results = {
-                    "desc":
-                    "Here is result.",
-                    "output_img":
-                    base64_head + "," + str(output_img_base64).replace(
-                        "b'", "").replace("'", "")
-                }
-            return {"result": results}
-        elif "object-detection" in module.type:
-            output_file = os.path.join("./output", results[0]["path"])
-            if output_file is not None and os.path.exists(output_file):
-                with open(output_file, "rb") as fp:
-                    output_img_base64 = base64.b64encode(fp.read())
-                os.remove(output_file)
-                results = {
-                    "desc":
-                    str(results[0]["data"]),
-                    "output_img":
-                    base64_head + "," + str(output_img_base64).replace(
-                        "b'", "").replace("'", "")
-                }
-            return {"result": results}
-
-
-def predict_sentiment_analysis(module, input_text):
+def predict_sentiment_analysis(module, input_text, extra=None):
     global use_gpu
     method_name = module.desc.attr.map.data['default_signature'].s
     predict_method = getattr(module, method_name)
@@ -68,7 +36,7 @@ def predict_sentiment_analysis(module, input_text):
     return results
 
 
-def predict_pretrained_model(module, input_text):
+def predict_pretrained_model(module, input_text, extra=None):
     global use_gpu
     method_name = module.desc.attr.map.data['default_signature'].s
     predict_method = getattr(module, method_name)
@@ -80,16 +48,18 @@ def predict_pretrained_model(module, input_text):
     return results
 
 
-def predict_lexical_analysis(module, input_text, extra=None):
+def predict_lexical_analysis(module, input_text, extra=[]):
     global use_gpu
     method_name = module.desc.attr.map.data['default_signature'].s
     predict_method = getattr(module, method_name)
+    data = {"text": input_text}
     try:
-        if extra is None:
-            data = {"text": input_text}
+        if extra is []:
+            results = predict_method(data=data, use_gpu=use_gpu)
         else:
-            data = {"text": input_text}
-        results = predict_method(data=data, use_gpu=use_gpu)
+            user_dict = extra[0]
+            results = predict_method(
+                data=data, user_dict=user_dict, use_gpu=use_gpu)
     except Exception as err:
         return {"result": "Please check data format!"}
     return results
@@ -231,10 +201,8 @@ def create_app():
 
     @app_instance.route("/predict/image/<module_name>", methods=["POST"])
     def predict_image(module_name):
-        # 稍后保存的文件名用id+源文件名的形式以避免冲突
         req_id = request.data.get("id")
         global use_gpu
-        # 这里是一个base64的列表
         img_base64 = request.form.getlist("input_img")
         file_name_list = []
         if img_base64 != "":
@@ -256,11 +224,7 @@ def create_app():
                 file_name = req_id + "_" + item.filename
                 item.save(file_name)
                 file_name_list.append(file_name)
-            # 到这里就把所有原始文件和文件名列表都保存了
-            # 文件名列表可用于预测
-            # 获取模型
         module = ImageModelService.get_module(module_name)
-        # 根据模型种类寻找具体预测方法，即根据名字定函数
         module_type = module.type.split("/")[-1].replace("-", "_").lower()
         predict_func = eval("predict_" + module_type)
         results = predict_func(module, file_name_list)
@@ -269,13 +233,19 @@ def create_app():
 
     @app_instance.route("/predict/text/<module_name>", methods=["POST"])
     def predict_text(module_name):
+        req_id = request.data.get("id")
         global use_gpu
-        # 应该是一个列表
         data = request.form.getlist("input_text")
+        file = request.files.getlist("user_dict")
         module = TextModelService.get_module(module_name)
         module_type = module.type.split("/")[-1].replace("-", "_").lower()
         predict_func = eval("predict_" + module_type)
-        results = predict_func(module, data)
+        file_list = []
+        for item in file:
+            file_path = req_id + "_" + item.filename
+            file_list.append(file_path)
+            item.save(file_path)
+        results = predict_func(module, data, file_list)
         return {"results": results}
 
     return app_instance