Update app.py

app.py

@@ -29,7 +29,108 @@ MODEL_CONFIGS = {
     }
 }
 
-[... 其他函數保持不變 ...]
+def get_llm_response(prompt, provider, model):
+    if provider == "openai":
+        response = openai_client.chat.completions.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": "你是一位資深的國文作文評閱委員，請依據提供的評分規準進行評分。"},
+                {"role": "user", "content": prompt}
+            ],
+            **MODEL_CONFIGS["openai"][model]
+        )
+        return response.choices[0].message.content
+    else:  # groq
+        completion = groq_client.chat.completions.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": "你是一位資深的國文作文評閱委員，請依據提供的評分規準進行評分。"},
+                {"role": "user", "content": prompt}
+            ],
+            **MODEL_CONFIGS["groq"][model],
+            stream=False,
+            top_p=1,
+            stop=None
+        )
+        return completion.choices[0].message.content
+
+def evaluate_essay(message, additional_prompt, provider, model):
+    if not message.strip():
+        return [], gr.Markdown("### 請輸入作文內容進行評分")
+    
+    criteria = {
+        '題旨發揮': {'weight': 0.4, 'max_score': 40},
+        '資料掌握': {'weight': 0.2, 'max_score': 20},
+        '結構安排': {'weight': 0.2, 'max_score': 20},
+        '字句運用': {'weight': 0.2, 'max_score': 20}
+    }
+    
+    grade_scores = {
+        'A+': 95, 'A': 90, 'A-': 85,
+        'B+': 80, 'B': 75, 'B-': 70,
+        'C+': 65, 'C': 60, 'C-': 55,
+        '0': 0
+    }
+
+    try:
+        history = []
+        total_score = 0
+        history.append(("作文內容：", message))
+        history.append(("", f"正在使用 {provider} ({model}) 進行評分分析..."))
+        
+        all_feedback = {}
+        
+        for criterion, details in criteria.items():
+            prompt = f"""評估以下作文的{criterion}（權重{details['weight']*100}%）：
+
+作文內容：
+{message}
+
+{additional_prompt if additional_prompt else ''}
+
+請依三等九級制（A+、A、A-、B+、B、B-、C+、C、C-）評分，並提供簡短評語。
+如果是缺考、未作答、完全文不對題或作答內容完全照抄試題，請給予0分。
+
+請按以下格式回覆：
+等第：
+評語："""
+
+            result = get_llm_response(prompt, provider, model)
+            lines = result.lower().split('\n')
+            grade = '0'
+            comment = ""
+            
+            for line in lines:
+                if '等第：' in line or '等第:' in line:
+                    grade_text = line.split('：')[-1].strip().upper()
+                    if grade_text in grade_scores:
+                        grade = grade_text
+                elif '評語：' in line or '評語:' in line:
+                    comment = line.split('：')[-1].strip()
+            
+            weighted_score = (grade_scores[grade] / 100) * details['max_score']
+            total_score += weighted_score
+            
+            feedback = f"### {criterion}\n"
+            feedback += f"- **等第**：{grade}\n"
+            feedback += f"- **得分**：{weighted_score:.1f}/{details['max_score']}\n"
+            feedback += f"- **評語**：{comment}\n"
+            
+            all_feedback[criterion] = feedback
+
+        for criterion in criteria:
+            history.append(("", all_feedback[criterion]))
+        
+        total_score_display = f"""
+# 總評分結果
+## 使用模型：{provider} ({model})
+## 總分：{total_score:.1f}/100
+"""
+        
+        return history, gr.Markdown(total_score_display)
+        
+    except Exception as e:
+        return [("", f"評分過程發生錯誤：{str(e)}")], gr.Markdown("### ❌ 評分失敗")
 
 # 模型選擇切換函數
 def update_model_choices(provider):