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Update app.py
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app.py
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@@ -141,7 +141,11 @@ class TextToSQLSystem:
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def huggingface_api_call(self, prompt: str) -> str:
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"""呼叫 Hugging Face Inference API"""
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headers = {"Authorization": f"Bearer {HF_TOKEN}"}
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payload = {
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"inputs": prompt,
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@@ -151,14 +155,33 @@ class TextToSQLSystem:
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}
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}
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try:
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self._log("正在呼叫 Hugging Face API...")
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response = requests.post(API_URL, headers=headers, json=payload, timeout=
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response.raise_for_status()
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self._log("✅ API 成功回應。")
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return response.json()[0]['generated_text']
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except requests.exceptions.RequestException as e:
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self._log(f"❌ API 呼叫失敗: {e}", "ERROR")
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# === 修改開始: 重寫核心處理邏輯 ===
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def _build_prompt_for_generation(self, user_question: str, examples: List[Dict]) -> str:
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def huggingface_api_call(self, prompt: str) -> str:
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"""呼叫 Hugging Face Inference API"""
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# === 修正開始 ===
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# 確保 API_URL 是一個乾淨的字串,不包含任何 Markdown "[ ]" 或其他特殊字元
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API_URL = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1"
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# === 修正結束 ===
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headers = {"Authorization": f"Bearer {HF_TOKEN}"}
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payload = {
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"inputs": prompt,
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}
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}
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try:
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# === 新增除錯日誌 ===
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# 在發送請求前,打印出最終要使用的 URL,以供檢查
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self._log(f"準備向 API 端點發送請求: {API_URL}")
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self._log("正在呼叫 Hugging Face API...")
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response = requests.post(API_URL, headers=headers, json=payload, timeout=90) # 延長超時時間
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response.raise_for_status() # 如果 API 回傳錯誤碼 (如 4xx, 5xx),會在此拋出例外
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self._log("✅ API 成功回應。")
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return response.json()[0]['generated_text']
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except requests.exceptions.RequestException as e:
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self._log(f"❌ API 呼叫失敗: {e}", "ERROR")
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# 嘗試解析回應內容,看是否是模型載入中的常見錯誤
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try:
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# 即使請求失敗,有時回應本文中仍有 JSON 錯誤訊息
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error_content = e.response.json() if e.response else {}
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if "error" in error_content and "estimated_time" in error_content["error"]:
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loading_time = error_content["error"]["estimated_time"]
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self._log(f" - 提示: 模型可能正在載入中,預計需要 {loading_time:.1f} 秒。請稍後重試。", "WARNING")
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return f"API 錯誤: 模型正在載入中,請稍後再試一次。"
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except (ValueError, AttributeError):
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# 如果回應不是 JSON 或沒有回應本文,就忽略
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pass
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return f"API 連線錯誤: {e}"
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# === 修改開始: 重寫核心處理邏輯 ===
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def _build_prompt_for_generation(self, user_question: str, examples: List[Dict]) -> str:
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