import gradio as gr import requests import json import os from pyvis.network import Network import networkx as nx from openai import OpenAI # Env Vars METABASE_USERNAME = os.getenv('METABASE_USERNAME') METABASE_PASSWORD = os.getenv('METABASE_PASSWORD') OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') # # get by local_config.json file # with open("local_config.json") as f: # config = json.load(f) # METABASE_USERNAME = config['METABASE_USERNAME'] # METABASE_PASSWORD = config['METABASE_PASSWORD'] # OPENAI_API_KEY = config['OPENAI_API_KEY'] # OpenAI API OPENAI_API_CLIENT = OpenAI(api_key=OPENAI_API_KEY) # Original elements elements = [ {'data': {'id': 'jnc-4-05-1-1', 'label': '真分數、假分數與帶分數的命名及說、讀、聽、寫、做'}}, {'data': {'id': 'jnc-4-05-2-1', 'label': '假分數與帶分數的互換'}}, {'data': {'id': 'jnc-4-05-3-1', 'label': '同分母分數的大小比較'}}, {'data': {'id': 'jnc-4-05-3-2', 'label': '同分母分數的加減'}}, {'data': {'id': 'jnc-4-05-3-3', 'label': '分數的整數倍'}}, {'data': {'id': 'jnc-4-06-1-1', 'label': '認識等值分數'}}, {'data': {'id': 'jnc-4-06-1-2', 'label': '找出等值分數'}}, {'data': {'id': 'jnc-4-06-2-1', 'label': '簡單異分母分數的比較'}}, {'data': {'id': 'jnc-4-06-2-2', 'label': '簡單異分母分數的加減'}}, {'data': {'id': 'jnc-4-06-3-1', 'label': '分數與一位小數的互換'}}, {'data': {'id': 'jnc-4-06-3-2', 'label': '分數與二位小數的互換'}}, {'data': {'id': 'jnc-4-08-2-1', 'label': '認識分數數線'}}, {'data': {'id': 'jnc-4-08-2-2', 'label': '數線的整數、分數、小數'}}, {'data': {'source': 'jnc-4-05-1-1', 'target': 'jnc-4-05-2-1'}}, {'data': {'source': 'jnc-4-05-1-1', 'target': 'jnc-4-05-3-1'}}, {'data': {'source': 'jnc-4-05-2-1', 'target': 'jnc-4-05-3-1'}}, {'data': {'source': 'jnc-4-05-1-1', 'target': 'jnc-4-05-3-2'}}, {'data': {'source': 'jnc-4-05-2-1', 'target': 'jnc-4-05-3-2'}}, {'data': {'source': 'jnc-4-05-2-1', 'target': 'jnc-4-05-3-3'}}, {'data': {'source': 'jnc-4-05-3-2', 'target': 'jnc-4-05-3-3'}}, {'data': {'source': 'jnc-4-05-1-1', 'target': 'jnc-4-06-1-1'}}, {'data': {'source': 'jnc-4-06-1-1', 'target': 'jnc-4-06-1-2'}}, {'data': {'source': 'jnc-4-06-1-1', 'target': 'jnc-4-06-2-1'}}, {'data': {'source': 'jnc-4-06-1-2', 'target': 'jnc-4-06-2-1'}}, {'data': {'source': 'jnc-4-06-1-1', 'target': 'jnc-4-06-2-2'}}, {'data': {'source': 'jnc-4-06-1-2', 'target': 'jnc-4-06-2-2'}}, {'data': {'source': 'jnc-4-06-3-1', 'target': 'jnc-4-06-3-2'}}, {'data': {'source': 'jnc-4-05-1-1', 'target': 'jnc-4-08-2-1'}}, {'data': {'source': 'jnc-4-08-2-1', 'target': 'jnc-4-08-2-2'}}, {'data': {'source': 'jnc-4-05-1-1', 'target': 'jnc-4-06-3-1'}} ] # Create a NetworkX graph nx_graph = nx.DiGraph() # Add nodes and edges for element in elements: if 'source' not in element['data']: # It's a node nx_graph.add_node(element['data']['id'], title=element['data']['label']) else: # It's an edge nx_graph.add_edge(element['data']['source'], element['data']['target']) def update_node_colors(result_data): color_mapping = {'1': 'green', '0': 'red', 'default': 'orange'} for node in nx_graph.nodes: if node in result_data: score = str(result_data[node]) color = color_mapping.get(score, color_mapping['default']) else: color = color_mapping['default'] nx_graph.nodes[node]['color'] = color # Function to generate the graph in hierarchical layout def needs_analysis(topic_result=None, exercise_quiz_result=None): if topic_result: update_node_colors(topic_result) nt = Network(directed=True) nt.from_nx(nx_graph) nt.repulsion(node_distance=120, central_gravity=0.0, spring_length=100, spring_strength=0.05, damping=0.09) nt.set_options(""" { "layout": { "hierarchical": { "enabled": false, "levelSeparation": 150, "nodeSpacing": 200, "treeSpacing": 500, "blockShifting": true, "edgeMinimization": true, "parentCentralization": true, "direction": "UD", "sortMethod": "directed" } } } """) map_html = nt.generate_html() # Replace single quotes with double quotes in HTML map_html = map_html.replace("'", "\"") html = f"""""" topic_table = generate_html_table(topic_result, exercise_quiz_result) html += f"

Topic Table

{topic_table}" return html def query_metabase_topic(class_code, topic_card_id, user_id, username=METABASE_USERNAME, password=METABASE_PASSWORD): try: # 获取会话令牌 session_response = requests.post( 'https://metabase.cloud.junyiacademy.org/api/session', headers={'Content-Type': 'application/json'}, json={'username': username, 'password': password} ) session_response.raise_for_status() session_token = session_response.json()['id'] print(f"Session token: {session_token}") # 打印请求信息 request_payload = { "parameters": [ { "type": "category", "target": ["variable", ["template-tag", "class_code"]], "value": class_code }, { "type": "category", "target": ["variable", ["template-tag", "user_id"]], "value": user_id } ] } print(f"Request payload: {json.dumps(request_payload, indent=2)}") # 使用提供的 card_id 查询 Metabase 卡片 query_response = requests.post( f'https://metabase.cloud.junyiacademy.org/api/card/{topic_card_id}/query/json', headers={ 'Content-Type': 'application/json', 'X-Metabase-Session': session_token }, json=request_payload ) query_response.raise_for_status() # filter class_code and user_id query_response = query_response.json() query_response = [item for item in query_response if item['class_code'] == class_code and item['user_id'] == user_id] return query_response[0] if query_response else {} except requests.RequestException as e: print(f"Failed to query Metabase card: {str(e)}") return {"error": f"Failed to query Metabase card: {str(e)}"} except Exception as e: print(f"Error: {str(e)}") return {"error": str(e)} def query_metabase_exercise_quiz(class_code, exercise_card_id, user_id, username=METABASE_USERNAME, password=METABASE_PASSWORD): try: # 获取会话令牌 session_response = requests.post( 'https://metabase.cloud.junyiacademy.org/api/session', headers={'Content-Type': 'application/json'}, json={'username': username, 'password': password} ) session_response.raise_for_status() session_token = session_response.json()['id'] print(f"Session token: {session_token}") # 打印请求信息 request_payload = { "parameters": [ { "type": "category", "target": ["variable", ["template-tag", "class_code"]], "value": class_code }, { "type": "category", "target": ["variable", ["template-tag", "user_id"]], "value": user_id } ] } print(f"Request payload: {json.dumps(request_payload, indent=2)}") # 使用提供的 card_id 查询 Metabase 卡片 query_response = requests.post( f'https://metabase.cloud.junyiacademy.org/api/card/{exercise_card_id}/query/json', headers={ 'Content-Type': 'application/json', 'X-Metabase-Session': session_token }, json=request_payload ) query_response.raise_for_status() # filter class_code and user_id query_response = query_response.json() print(f"query_response: {query_response}") query_response = [item for item in query_response if item['class_code'] == class_code and item['user_id'] == user_id] return query_response except requests.RequestException as e: print(f"Failed to query Metabase card: {str(e)}") return {"error": f"Failed to query Metabase card: {str(e)}"} except Exception as e: print(f"Error: {str(e)}") return {"error": str(e)} def generate_html_table(result_data, exercise_quiz_result): color_mapping = {'1': 'green', '0': 'red', 'default': 'orange'} table_html = """ """ for node in nx_graph.nodes: name = nx_graph.nodes[node].get('title', node) if node in result_data: score = str(result_data[node]) color = color_mapping.get(score, color_mapping['default']) else: color = color_mapping['default'] exercise_results = [ f"
  • Quiz ID: {quiz['quiz_id']}, Correct: {quiz['is_correct']}, Time Taken: {quiz['total_time_taken']}, Hint Used: {quiz['is_hint_used']}
    • " for quiz in exercise_quiz_result if quiz['exercise_title'] == name and quiz['user_id'] == result_data['user_id'] and quiz['class_code'] == result_data['class_code'] ] exercise_results_html = '' row_id = f"row-{node}" table_html += f""" """ table_html += "
    ID Name Exercise Results
    {node} {name} {exercise_results_html}
    " return table_html def get_ai_suggestion(topic_result, exercise_quiz_result): # 使用 OPenAI API 获取建议 model = "gpt-4o" sys_content = f""" You are a professional teaching expert. I am a classroom teacher. Please provide user (he/she is a teacher) a personalized analysis """ user_content = f""" Based on the data, topic: {topic_result}, exercise_quiz_result: {exercise_quiz_result} Please provide suggestions for the course. rules: - The suggestions should be based on the data provided. - The suggestions should be actionable and specific. - The suggestions should be relevant to the course content. - use ZH-TW language, this is very important. - return markdown format - user will get a knowledge graph and data, 1 will be the best, 0 will be the worst. then 1 will show color green, 0 will show color red, others will show color orange. so you can just transfer the number to color to explain the user's performance. restrictions: - don't use any personal information - don't use any sensitive information - don't use any offensive language - don't use any inappropriate language - don't use any 簡體字，or 大陸用語，ex: 視頻請替換成影片、練習冊請替換成練習本、等等 for this student based on the given knowledge graph and data: 1. How to interpret this knowledge graph. 2. How to assess the student's strengths and weaknesses. 3. How I can help this student improve. for example: # 針對 emdob01 學生的課程建議 ### 1. 如何解讀此知識圖與數據 知識圖呈現學生在不同知識點上的掌握程度，用數字顯示熟悉度(0-1)。例如： - `jnc-4-05-3-1` : 1(綠色) 代表完全掌握 - `jnc-4-06-2-2` : 0（紅色） 代表未掌握 該學生的表現數據顯示各個練習題的回答正確性與所花費的時間。這有助於了解學生對不同題目的理解程度和答題速度。 # 2. 如何評估學生的強項與弱項 ### 強項 - 學生在某些特定的練習題中得到了正確答案。例如： - `認識等值分數` 題目 `quiz_id` 108363，答對且用時62秒 - `真分數、假分數與帶分數的命名及說、讀、聽、寫、做` 題目 `quiz_id` 116882，答對且用時123秒 ### 弱項 - 學生在大多數練習題中答錯問題，特別在以下主題： - `認識等值分數`：多次答錯 - `簡單異分母分數的比較`：多次答錯，且用時較長 - `假分數與帶分數的互換`：多次答錯 # 3. 如何幫助學生改進 基於以上分析，給予以下改善建議： ### 針對弱點加強練習 1. **認識等值分數**： - 提供更多視覺化的教學資源，如分數圖表和視頻講解。 - 設計一系列有針對性的互動練習，幫助學生理解分數等值的概念。 - 安排小組討論，讓學生分享他們的解題思路並相互學習。 2. **簡單異分母分數的比較**： - 強化基本概念教學，如找共同分母的方法。 - 設計漸進增難度的習題，從簡單到複雜，逐步提高學生的理解和應用能力。 - 鼓勵學生使用實物模型或數線來理解異分母分數的比較。 3. **假分數與帶分數的互換**： - 重點教學如何將假分數轉換為帶分數，並展示具體步驟。 - 制作練習題目集，讓學生反復練習，並提供及時的反饋和指導。 - 透過遊戲或互動活動來增強學生對該概念的興趣和記憶。 # 總結與提點建議 - 在課後提供補充資料（如影片、練習冊）供學生複習。 - 設計定期的小測試來檢驗學生的進步情況。 - 鼓勵學生對於每次錯誤進行反思，分析失誤原因並加以改正。 - 建立個性化學習計劃，根據每次測試結果調整練習重點和方法。 希望這些建議能夠幫助該學生提升學習效果並更好地掌握課程內容。 """ print(f"user_content: {user_content}") messages = [ {"role": "system", "content": sys_content}, {"role": "user", "content": user_content} ] request_payload = { "model": model, "messages": messages, "max_tokens": 4000, } response = OPENAI_API_CLIENT.chat.completions.create(**request_payload) suggestion = response.choices[0].message.content return suggestion with gr.Blocks() as app: gr.Markdown("# Metabase Query and Visualization") with gr.Row(): topic_card_id = gr.Textbox(label="Topic Card ID", value="6267") exercise_card_id = gr.Textbox(label="Exercis Quiz Card ID", value="6284") class_code = gr.Textbox(label="Class Code", value="CAFSR") user_id = gr.Textbox(label="User ID", value="emdob01") all_in_one_button = gr.Button("Fetch Data and Generate Graph") with gr.Accordion(open=False, label="Raw Data"): topic_result = gr.JSON(label="Query Result") exercise_quiz_result = gr.JSON(label="Quiz Query Result") with gr.Row(): graph_html = gr.HTML() with gr.Row(): gr.Markdown("# AI suggestion Powered by Junyi Academy") with gr.Row(): ai_suggestion = gr.Markdown() all_in_one_button.click( fn=query_metabase_topic, inputs=[class_code, topic_card_id, user_id], outputs=topic_result ).then( fn=query_metabase_exercise_quiz, inputs=[class_code, exercise_card_id, user_id], outputs=exercise_quiz_result ).then( fn=needs_analysis, inputs=[topic_result, exercise_quiz_result], outputs=graph_html ).then( fn=get_ai_suggestion, inputs=[topic_result, exercise_quiz_result], outputs=ai_suggestion ) app.launch()