Ollama+llama3.2完成智慧成績助教系統

本程式是由小鎮智能黃俊毓提供的程式碼改編而成，並經由ChatGPT協助除錯。

1.在ollama啟動llama3.2。

2.智慧成績助教系統的程式

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import ollama from openpyxl import Workbook, load_workbook import json import os # === Function Calling Schema === functions = [ { "type":"function", "function": { "name":"suggest_pass_adjustment", "description": "對於 59 分學生，詢問是否補到 60 分", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "學生姓名(name)" } }, "required": ["name"] } } }, { "type": "function", "function": { "name":"suggest_remedial_adjustment", "description": "對於低於 59 分學生，詢問是否補分，以及新分數", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "學生姓名(name)" }, "current_score": {"type": "integer"} }, "required": ["name", "current_score"] } } }, { "type": "function", "function": { "name":"suggest_edit_for_passed", "description": "詢問是否要修改分數大於60分，及格學生的分數", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "學生姓名(name)" }, "current_score": {"type": "integer"} }, "required": ["name", "current_score"] } } }, { "type": "function", "function": { "name":"suggest_apply_adjustment", "description": "將學生分數更新為指定的新分數", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "學生姓名(name)" }, "new_score": {"type": "integer"} }, "required": ["name", "new_score"] } } }, ] # === 初始化成績表（如不存在）=== filename = "grades.xlsx" if not os.path.exists(filename): wb = Workbook() ws = wb.active ws.title = "成績表" ws.append(["姓名", "分數"]) ws.append(["王小明", 59]) ws.append(["林小美", 73]) ws.append(["張大文", 45]) wb.save(filename) print(f"✅ 已建立初始成績檔：{filename}") else: print(f"📥 已載入成績檔：{filename}") # === GPT 對應的處理函式 === # 以下四個函式為 GPT 根據不同情況呼叫的處理邏輯 # 建議學生逐步閱讀與理解：輸入、邏輯、Excel 修改方式 def handle_pass_adjustment(row, name): ans = input(f"{name} 的分數是 59，是否補及格至 60？（Y/n）：") if ans.lower() in ["y", "yes", ""]: row[1].value = 60 print(f"✅ {name} 已補及格（60 分）") return f"{name} 補及格至 60 分 建議多加強" else: print("🟡 未調整") return f"{name} 未補及格" def handle_remedial_adjustment(row, name, score): ans = input(f"{name} 的分數是 {score}，是否補分？（Y/n）：") if ans.lower() in ["y", "yes", ""]: try: new_score = int(input("👉 請輸入補分後的新分數：")) row[1].value = new_score print(f"✅ 已將 {name} 改為 {new_score} 分") return f"{name} 補分為 {new_score} 分 建議多加強" except: print("⚠️ 格式錯誤，略過") return f"{name} 補分輸入錯誤，未調整" else: print("🟡 未調整") return f"{name} 未補分" def handle_edit_for_passed(row, name, score): ans = input(f"{name} 成績為 {score} 分，是否要修改？（輸入新分數或按 Enter 跳過）：") if ans.strip(): try: new_score = int(ans) row[1].value = new_score print(f"✅ 已將 {name} 改為 {new_score} 分") return f"{name} 改為 {new_score} 分" except: print("⚠️ 格式錯誤，略過") return f"{name} 修改輸入錯誤，未調整" else: print("✅ 保留原分數") return f"{name} 保留原分數 {score} 分" def handle_apply_adjustment(row, name, new_score): row[1].value = new_score print(f"✅ GPT 決定將 {name} 的分數改為 {new_score}") return f"{name} 的分數改為 {new_score}" # === 以下請同學補寫 main loop === # - 使用 for row in ws.iter_rows(min_row=2): 遍歷每位學生 # - 呼叫 GPT 的 chat.completions.create 傳入使用者輸入與 function schema # - 根據 GPT 回傳的 function_call.name 判斷要呼叫哪一個處理函式 # - 最後將分數儲存為 grades_final.xlsx # 提示：請將回傳的 arguments 用 json.loads(...) 解析後取出參數 wb = load_workbook(filename) ws = wb.active for row in ws.iter_rows(min_row=2): name = row[0].value score = row[1].value user_input = f"{name} 的分數是 {score}" response = ollama.chat( model="llama3.2", messages=[{"role": "user", "content": user_input}], tools=functions, ) if hasattr(response, "message") and hasattr(response.message, "tool_calls") and response.message.tool_calls: tool_call = response.message.tool_calls[0] func_name = tool_call['function']['name'] args = tool_call['function']['arguments'] if func_name == "suggest_pass_adjustment": result = handle_pass_adjustment(row, name) elif func_name == "suggest_remedial_adjustment": result = handle_remedial_adjustment(row, name, args['current_score']) elif func_name == "suggest_edit_for_passed": result = handle_edit_for_passed(row, name, args['current_score']) elif func_name == "suggest_apply_adjustment": result = handle_apply_adjustment(row, name, args['new_score']) else: result = "❌ 無對應函式" # 後續回應 follow_up = ollama.chat( model="llama3.2", messages=[ {"role": "user", "content": user_input}, response.message, {"role": "tool", "name": func_name, "content": result} ] ) print(f"💬 GPT 回覆：{follow_up.message['content']}") else: print("🤖 GPT 沒有呼叫任何函式") # === 儲存檔案 === final_name = "grades_final.xlsx" wb.save(final_name) print(f"\n🎉 所有成績處理完成，已儲存為 {final_name}")

3.執行結果：