Update app.py

app.py

@@ -1,77 +1,19 @@
-### app.py
-import socket
-import gradio as gr
-from openai import OpenAI
+# app.py
+from fastapi import FastAPI
+from pydantic import BaseModel
 
-def get_local_ip():
-    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
-    try:
-        s.connect(("8.8.8.8", 80))
-        ip = s.getsockname()[0]
-    except Exception:
-        ip = "127.0.0.1"
-    finally:
-        s.close()
-    return ip
+app = FastAPI()
 
-print("本機 IP:", get_local_ip())
+# 定義輸入/輸出資料格式
+class Item(BaseModel):
+    text: str
 
-# ✅ 設定 base URL 連接本地 llama.cpp API
-client = OpenAI(
-    base_url="http://0.0.0.0:8000/v1",
-    api_key="sk-local",  # llama.cpp 不檢查內容，只要有就行
-    timeout=600
-)
+@app.get("/")
+def root():
+    return {"message": "Hello from FastAPI on HF Space!"}
 
-# ✅ 回應函式 (流式 generator)
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    messages = [{"role": "system", "content": system_message}]
-    messages.extend(history)
-    messages.append({"role": "user", "content": message})
-
-    try:
-        stream = client.chat.completions.create(
-            model="qwen3",
-            messages=messages,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            reasoning_effort="low",
-            stream=True,
-        )
-
-        output = ""
-        skip_tokens = ['<|channel|>', 'analysis']
-        skipped = {token: False for token in skip_tokens}  # 追蹤每個 token 是否已忽略過
-
-        for chunk in stream:
-            print("[DEBUG] chunk:", chunk)
-            delta = chunk.choices[0].delta
-            if delta and delta.content:
-                content = delta.content.strip()
-                # 如果這個 token 是要跳過的，且還沒被跳過過
-                if content in skip_tokens and not skipped[content]:
-                    skipped[content] = True
-                    continue
-                output += delta.content  # 正常加入輸出
-
-            yield {"role": "assistant", "content": output}
-
-    except Exception as e:
-        print(f"[Error] {e}")
-        yield {"role": "assistant", "content": "⚠️ Llama.cpp server 沒有回應，請稍後再試。"}
-
-# ✅ Gradio 介面 (新版必須用 type="messages")
-demo = gr.ChatInterface(
-    respond,
-    type="messages",  # 🔑 使用 OpenAI 風格訊息格式
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly assistant.", label="System message"),
-        gr.Slider(minimum=1, maximum=16383, value=4096, 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()
+@app.post("/predict")
+def predict(item: Item):
+    # 模擬一個簡單推論邏輯
+    result = item.text.upper()
+    return {"input": item.text, "result": result}