Update app.py

app.py

@@ -1,7 +1,103 @@
-import gradio as gr
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+os.system('nvcc --version')
+os.system('python --version')
+
+
+
+
+model_url = 'http://localhost:8000/v1'
+model = "sijieaaa/CodeModel-V1-3B-2024-02-07"
+stop_token_ids = ''
+
+
+
+import subprocess
+
+# Setup LLM API
+# -- llama-factory
+def start_api():
+    # 以非阻塞方式启动 API
+    process = subprocess.Popen(
+        ["lmf", "api", "--model_name", "sijieaaa/CodeModel-V1-3B-2024-02-07", "--template", "qwen"],
+        stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
+    )
+    # 监听 API 启动日志，检测关键字
+    while True:
+        line = process.stdout.readline()
+        if not line:
+            break  # 没有新输出，继续等待
+        print(line.strip())  # 打印 API 启动日志
+        if "Running on local URL:" in line or "API started" in line or "http://localhost:" in line:
+            print("✅ API 启动成功！")
+            return process  # 返回 API 进程
+    print("❌ API 启动失败！")
+    process.terminate()
+    return None
+api_process = start_api()
+# API 启动后执行下一步
+if api_process:
+    print("🎯 执行后续操作……")
+else:
+    raise Exception("API 启动失败！")
+
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+
+
+# Setup OpenAI API client
+from openai import OpenAI
+openai_api_key = '0'
+openai_api_base = model_url
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+
+def predict(message, history):
+    # Convert chat history to OpenAI format
+    history_openai_format = [{
+        "role": "system",
+        "content": "You are a great ai assistant."
+    }]
+    for human, assistant in history:
+        history_openai_format.append({"role": "user", "content": human})
+        history_openai_format.append({
+            "role": "assistant",
+            "content": assistant
+        })
+    history_openai_format.append({"role": "user", "content": message})
+
+    # Create a chat completion request and send it to the API server
+    stream = client.chat.completions.create(
+        model=model,  # Model name to use
+        messages=history_openai_format,  # Chat history
+        temperature=0.95,  # Temperature for text generation
+        stream=True,  # Stream response
+        top_p=0.7,
+        extra_body={
+            'repetition_penalty':
+            1,
+            'stop_token_ids': [
+                int(id.strip()) for id in stop_token_ids.split(',')
+                if id.strip()
+            ] if stop_token_ids else []
+        })
+
+    # Read and return generated text from response stream
+    partial_message = ""
+    for chunk in stream:
+        partial_message += (chunk.choices[0].delta.content or "")
+        yield partial_message
+
+
+
+import gradio as gr
+# Create and launch a chat interface with Gradio
+gr.ChatInterface(predict).queue().launch(
+    # server_name=host,
+    # server_port=port,
+    share=True
+    )