new

request_llms/com_sparkapi.py

@@ -1,4 +1,4 @@
-from toolbox import get_conf
+from toolbox import get_conf, get_pictures_list, encode_image
 import base64
 import datetime
 import hashlib
@@ -65,18 +65,19 @@ class SparkRequestInstance():
         self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
         self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
         self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat"
+        self.gpt_url_img = "wss://spark-api.cn-huabei-1.xf-yun.com/v2.1/image"
 
         self.time_to_yield_event = threading.Event()
         self.time_to_exit_event = threading.Event()
 
         self.result_buf = ""
 
-    def generate(self, inputs, llm_kwargs, history, system_prompt):
+    def generate(self, inputs, llm_kwargs, history, system_prompt, use_image_api=False):
         llm_kwargs = llm_kwargs
         history = history
         system_prompt = system_prompt
         import _thread as thread
-        thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt))
+        thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt, use_image_api))
         while True:
             self.time_to_yield_event.wait(timeout=1)
             if self.time_to_yield_event.is_set():
@@ -85,14 +86,20 @@ class SparkRequestInstance():
                 return self.result_buf
 
 
-    def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt):
+    def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt, use_image_api):
         if llm_kwargs['llm_model'] == 'sparkv2':
             gpt_url = self.gpt_url_v2
         elif llm_kwargs['llm_model'] == 'sparkv3':
             gpt_url = self.gpt_url_v3
         else:
             gpt_url = self.gpt_url
-
+        file_manifest = []
+        if use_image_api and llm_kwargs.get('most_recent_uploaded'):
+            if llm_kwargs['most_recent_uploaded'].get('path'):
+                file_manifest = get_pictures_list(llm_kwargs['most_recent_uploaded']['path'])
+                if len(file_manifest) > 0:
+                    print('正在使用讯飞图片理解API')
+                    gpt_url = self.gpt_url_img
         wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
         websocket.enableTrace(False)
         wsUrl = wsParam.create_url()
@@ -101,9 +108,8 @@ class SparkRequestInstance():
         def on_open(ws):
             import _thread as thread
             thread.start_new_thread(run, (ws,))
-
         def run(ws, *args):
-            data = json.dumps(gen_params(ws.appid, *ws.all_args))
+            data = json.dumps(gen_params(ws.appid, *ws.all_args, file_manifest))
             ws.send(data)
 
         # 收到websocket消息的处理
@@ -142,9 +148,18 @@ class SparkRequestInstance():
         ws.all_args = (inputs, llm_kwargs, history, system_prompt)
         ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
 
-def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
+def generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest):
     conversation_cnt = len(history) // 2
-    messages = [{"role": "system", "content": system_prompt}]
+    messages = []
+    if file_manifest:
+        base64_images = []
+        for image_path in file_manifest:
+            base64_images.append(encode_image(image_path))
+        for img_s in base64_images:
+            if img_s not in str(messages):
+                messages.append({"role": "user", "content": img_s, "content_type": "image"})
+    else:
+        messages = [{"role": "system", "content": system_prompt}]
     if conversation_cnt:
         for index in range(0, 2*conversation_cnt, 2):
             what_i_have_asked = {}
@@ -167,7 +182,7 @@ def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
     return messages
 
 
-def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
+def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest):
     """
     通过appid和用户的提问来生成请参数
     """
@@ -176,6 +191,8 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
         "sparkv2": "generalv2",
         "sparkv3": "generalv3",
     }
+    domains_select = domains[llm_kwargs['llm_model']]
+    if file_manifest: domains_select = 'image'
     data = {
         "header": {
             "app_id": appid,
@@ -183,7 +200,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
         },
         "parameter": {
             "chat": {
-                "domain": domains[llm_kwargs['llm_model']],
+                "domain": domains_select,
                 "temperature": llm_kwargs["temperature"],
                 "random_threshold": 0.5,
                 "max_tokens": 4096,
@@ -192,7 +209,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
         },
         "payload": {
             "message": {
-                "text": generate_message_payload(inputs, llm_kwargs, history, system_prompt)
+                "text": generate_message_payload(inputs, llm_kwargs, history, system_prompt, file_manifest)
             }
         }
     }