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| import gradio as gr | |
| import requests | |
| import os | |
| # 設定 Hugging Face API | |
| API_URL = "https://api-inference.huggingface.co/models/cardiffnlp/twitter-xlm-roberta-base-sentiment" | |
| # 從環境變數讀取 API 金鑰（建議這樣做以保護隱私） | |
| HF_API_KEY = os.getenv("HF_API_KEY") # 你可以在 .bashrc 或 .zshrc 設定環境變數 | |
| HEADERS = {"Authorization": f"Bearer {HF_API_KEY}"} | |
| # 轉換英文分類為中文 | |
| def translate_sentiment(label): | |
| if "positive" in label.lower(): | |
| return "😃 **正向**" | |
| elif "neutral" in label.lower(): | |
| return "😐 **中立**" | |
| else: | |
| return "😡 **負向**" | |
| # 轉換信心度為更直觀的等級 | |
| def convert_confidence(score): | |
| percentage = round(score * 100) # 轉換為百分比 | |
| if score >= 0.90: | |
| return f"🌟 **極高信心** ({percentage}%)" | |
| elif score >= 0.75: | |
| return f"✅ **高信心** ({percentage}%)" | |
| elif score >= 0.50: | |
| return f"⚠️ **中等信心** ({percentage}%)" | |
| elif score >= 0.30: | |
| return f"❓ **低信心** ({percentage}%)" | |
| else: | |
| return f"❌ **極低信心（建議忽略）** ({percentage}%)" | |
| # 調用 Hugging Face API 進行情緒分析 | |
| def analyze_sentiment(text): | |
| try: | |
| response = requests.post(API_URL, headers=HEADERS, json={"inputs": text}) | |
| result = response.json() | |
| if isinstance(result, list) and len(result) > 0: | |
| sentiment = translate_sentiment(result[0]["label"]) # 翻譯情緒 | |
| confidence = result[0]["score"] | |
| confidence_label = convert_confidence(confidence) # 轉換信心度 | |
| return f"**情緒分類**: {sentiment}\n**信心度**: {confidence_label}" | |
| else: | |
| return "⚠️ **無法分析文本，請稍後再試**" | |
| except Exception as e: | |
| return f"❌ **錯誤**: {str(e)}" | |
| # 建立 Gradio 介面 | |
| iface = gr.Interface( | |
| fn=analyze_sentiment, | |
| inputs=gr.Textbox(lines=2, placeholder="請輸入文本（支援多語言）..."), | |
| outputs=gr.Markdown(label="分析結果"), | |
| title="多語言情緒分析 AI", | |
| description="請輸入一段話，AI 會分析它的情緒（正向 / 中立 / 負向），並提供信心度。", | |
| theme="compact" | |
| ) | |
| # 啟動 Web 應用 | |
| iface.launch() | |