Update app.py

app.py

@@ -1,63 +1,105 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    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})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 加载 xLAM 模型和 tokenizer
+model_name = "Salesforce/xLAM-7b-r"
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# 定义任务提示和格式提示
+task_instruction = """
+Based on the previous context and API request history, generate an API request or a response as an AI assistant.
+""".strip()
+
+format_instruction = """
+The output should be of the JSON format, which specifies a list of generated function calls. If no function call is needed, please make tool_calls an empty list "[]".
+""".strip()
+
+# 定义工具信息
+get_weather_api = {
+    "name": "get_weather",
+    "description": "Get the current weather for a location",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, New York"
+            },
+            "unit": {
+                "type": "string",
+                "enum": ["celsius", "fahrenheit"],
+                "description": "The unit of temperature to return"
+            }
+        },
+        "required": ["location"]
+    }
+}
+
+search_api = {
+    "name": "search",
+    "description": "Search for information on the internet",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": "The search query, e.g. 'latest news on AI'"
+            }
+        },
+        "required": ["query"]
+    }
+}
+
+# 转换工具为 xLAM 的格式
+def convert_to_xlam_tool(tools):
+    if isinstance(tools, dict):
+        return {
+            "name": tools["name"],
+            "description": tools["description"],
+            "parameters": {k: v for k, v in tools["parameters"].get("properties", {}).items()}
+        }
+    elif isinstance(tools, list):
+        return [convert_to_xlam_tool(tool) for tool in tools]
+    else:
+        return tools
+
+xlam_format_tools = convert_to_xlam_tool([get_weather_api, search_api])
+
+# 生成提示
+def build_prompt(task_instruction, format_instruction, tools, query):
+    prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
+    prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{tools}\n[END OF AVAILABLE TOOLS]\n\n"
+    prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
+    prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
+    return prompt
+
+# 定义模型推理函数
+def generate_response(query):
+    # 构建输入提示
+    content = build_prompt(task_instruction, format_instruction, xlam_format_tools, query)
+    messages = [{'role': 'user', 'content': content}]
+    
+    # 编码输入
+    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+    
+    # 生成输出
+    outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
+    
+    # 解码输出
+    response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
+    return response
+
+# 使用 Gradio 创建简单的 Web 应用
+with gr.Blocks() as demo:
+    gr.Markdown("## 使用 xLAM 模型进行智能对话")
+    
+    query = gr.Textbox(label="输入您的问题", placeholder="请输入您的问题")
+    output = gr.Textbox(label="模型响应")
+    
+    submit_btn = gr.Button("提交")
+    submit_btn.click(fn=generate_response, inputs=query, outputs=output)
+
+# 启动 Gradio 应用
+demo.launch()