Create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import json
+import random
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+
+# 模型初始化（Hugging Face Spaces會跑）
+model_name = "mistralai/Mistral-7B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# 資料夾路徑
+DATA_DIR = "./data"
+
+# 核心函數：抽單字＋造句
+def get_words_with_sentences(source="common3000", n=10):
+    try:
+        # 動態讀取指定資料檔
+        data_path = os.path.join(DATA_DIR, f"{source}.json")
+        with open(data_path, 'r', encoding='utf-8') as f:
+            words = json.load(f)
+
+        # 隨機抽取
+        selected_words = random.sample(words, n)
+        results = []
+
+        # 每個單字請 GPT 造句
+        for word_data in selected_words:
+            word = word_data['word']
+            prompt = f"Write a simple English sentence using the word '{word}' suitable for beginners."
+
+            inputs = tokenizer(prompt, return_tensors="pt")
+            outputs = model.generate(**inputs, max_new_tokens=30)
+            sentence = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+            results.append({
+                "word": word,
+                "phonetic": word_data["phonetic"],
+                "sentence": sentence
+            })
+
+        return results
+
+    except Exception as e:
+        return [{"error": f"發生錯誤: {str(e)}"}]
+
+# Gradio 介面設定
+demo = gr.Interface(
+    fn=get_words_with_sentences,
+    inputs=[
+        gr.Textbox(value="common3000", label="選擇單字庫"),
+        gr.Number(value=10, label="抽幾個單字")
+    ],
+    outputs="json"
+)
+
+demo.launch()