Update app.py

app.py

@@ -1,73 +1,73 @@
-import gradio as gr
-import json
-from openai import OpenAI
-
-
-def stream_chat(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    base_url,
-    api_key,
-    model_name,
-    ):
-
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    reply = ""
-
-    client = OpenAI(
-        base_url=base_url,
-        api_key=api_key
-    )
-
-    # 发送带有流式输出的请求
-    for chunk in client.chat.completions.create(
-        model=model_name,
-        messages=messages,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        stream=True  # 启用流式输出
-    ):
-        chunk_message = chunk.choices[0].delta.content
-        if chunk_message is not None:
-            reply += chunk_message
-        else:
-            reply += ""
-        yield reply
-
-
-chatapp = gr.ChatInterface(
-        stream_chat,
-        additional_inputs=[
-            gr.Textbox(value="你是一个乐于助人的AI助手.", label="System message"),
-            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-            gr.Slider(
-                minimum=0.1,
-                maximum=1.0,
-                value=0.95,
-                step=0.05,
-                label="Top-p (nucleus sampling)",
-            ),
-            gr.Textbox(value="", label="基础URL", type="text"),
-            gr.Textbox(value="", label="API Key", type="password"),
-            gr.Textbox(value="", label="模型名称", type="text"),
-        ]
-    )
-
-if __name__ == "__main__":
-    chatapp.launch()
-
+import gradio as gr
+import json
+from openai import OpenAI
+
+
+def stream_chat(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    base_url,
+    api_key,
+    model_name,
+    ):
+
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    reply = ""
+
+    client = OpenAI(
+        base_url=base_url,
+        api_key=api_key
+    )
+
+    # 发送带有流式输出的请求
+    for chunk in client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        stream=True  # 启用流式输出
+    ):
+        chunk_message = chunk.choices[0].delta.content
+        if chunk_message is not None:
+            reply += chunk_message
+        else:
+            reply += ""
+        yield reply
+
+
+chatapp = gr.ChatInterface(
+        stream_chat,
+        additional_inputs=[
+            gr.Textbox(value="你是一个乐于助人的AI助手.", label="System message"),
+            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.95,
+                step=0.05,
+                label="Top-p (nucleus sampling)",
+            ),
+            gr.Textbox(value="", label="基础URL", type="text"),
+            gr.Textbox(value="", label="API Key", type="password"),
+            gr.Textbox(value="", label="模型名称", type="text"),
+        ]
+    )
+
+if __name__ == "__main__":
+    chatapp.launch(share=True)
+