report.py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import os
import datetime
import matplotlib.pyplot as plt
from docx import Document
from docx.shared import Inches
from docx.shared import RGBColor
import google.generativeai as genai
import json
import pandas as pd

def generate_radar_chart(correct_rates, output_path):
    categories = list(correct_rates.keys())
    values = list(correct_rates.values())

    # Complete the radar chart by closing the loop
    values += values[:1]
    categories += categories[:1]

    angles = [n / float(len(categories) - 1) * 2 * 3.14159 for n in range(len(categories))]

    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw={"polar": True})
    ax.fill(angles, values, color="blue", alpha=0.25)
    ax.plot(angles, values, color="blue", linewidth=2)
    ax.set_yticks([0.2, 0.4, 0.6, 0.8, 1.0])
    ax.set_yticklabels(["20%", "40%", "60%", "80%", "100%"], color="grey", size=10)
    ax.set_xticks(angles[:-1])
    ax.set_xticklabels(categories[:-1], size=10)
    plt.savefig(output_path)
    plt.close()

def get_gemini_feedback(score, correct_rates):
    model = genai.GenerativeModel('gemini-pro')
    chat = model.start_chat()
   
    report_example = """**Passage:**
        
            TOEFL Practice Report
    Summary
    Score: 28
     
    Performance Summary:
    ## TOEFL Performance Summary & Learning Suggestions
    **Performance Summary:**
    - **Strengths:** Demonstrated strong performance in identifying speaker implications, as seen in correctly answering "What does the speaker imply?" with Option C.
    - **Areas for Improvement:** Main idea questions were a challenge, as illustrated by selecting Option B instead of the correct Option A. The misunderstanding likely stemmed from not fully grasping the passage's primary focus.

    **Future Learning Suggestions:**
    1. **Enhance Main Idea Identification Skills:**
       - Focus on summarizing passages in your own words after reading or listening.
       - Practice identifying topic sentences and distinguishing main ideas from supporting details.
       - Utilize TOEFL preparation resources that emphasize comprehension of main ideas.

    2. **Improve Reading Strategies:**
       - Skim passages to get a sense of structure before reading in detail.
       - Pay attention to transitional phrases that indicate the author’s intent or key points.

    3. **Regular Practice:**
       - Use practice questions that mimic TOEFL formats to become more familiar with question phrasing.
       - Review explanations for both correct and incorrect answers to deepen understanding.
            """

    # 合併提示到單一請求中
    if correct_rates!=-1:  
        prompt = (
            f"Please generate a TOEFL performance report based on the example format below:\n\n{report_example}\n\n"
            f"Score: {score}. Correct rates by question type: {correct_rates}.\n"
            "Provide a performance summary and actionable advice."
        )
    elif correct_rates==-1:
        prompt = (
            f"Please generate a TOEFL performance report based on the example format below:\n\n{report_example}\n\n"
            f"Score: {score}.\n"
            "Provide a performance summary and actionable advice."
        )
    
    try:
        response = chat.send_message(prompt)
        if response.text:
            # 使用更靈活的解析邏輯
            summary_start = response.text.find("Summary:")
            suggestions_start = response.text.find("Suggestions:")
            
            summary = (
                response.text[summary_start:suggestions_start].strip()
                if summary_start != -1 and suggestions_start != -1
                else "No summary available."
            )
            suggestions = (
                response.text[suggestions_start:].strip()
                if suggestions_start != -1
                else "No suggestions available."
            )
            return {"summary": summary, "suggestions": suggestions}
        else:
            raise ValueError("No response text from model.")
    except Exception as e:
        return {"summary": "Error generating summary.", "suggestions": f"Error: {str(e)}"}

def listening_report(l_df_get, user_ans):
    def check_listening(l_df_get, user_ans):
        l_df_get = pd.read_csv("listening.csv")
        for key, value in user_ans.items(): 
            l_df_get.loc[l_df_get["number"] == int(key), 'user_ans'] = value
        
        l_df_get["correct"] = (l_df_get["answer"] == l_df_get["user_ans"]).astype(int)
        score = (l_df_get["correct"].sum())/11*30
        type_scores = l_df_get.groupby("question_type")["correct"].mean().to_dict()
        l_score_get = {"score" : score , "type_scores" : type_scores}
        return [l_df_get,l_score_get]
    check=check_listening(l_df_get, user_ans)
    data=check[1]
    df=check[0]
    radar_chart_path = "radar_chart.png"
    generate_radar_chart(data["type_scores"], radar_chart_path)
    feedback = get_gemini_feedback(data["score"], data["type_scores"])
    # Step 4: Create DOCX File
    def create_docx(data, feedback, radar_chart_path):
        doc = Document()
        doc.add_heading("TOEFL Listening Practice Report", level=1)

        # Add Summary
        doc.add_heading("Summary", level=2)
        doc.add_paragraph(f"Score: {data['score']}")
        doc.add_picture(radar_chart_path, width=Inches(4))
        doc.add_paragraph("Performance Summary:")
        doc.add_paragraph(feedback.get("summary", "No summary available."))
        doc.add_paragraph("Future Learning Suggestions:")
        doc.add_paragraph(feedback.get("suggestions", "No suggestions available."))

        # Add Question Details
    # Iterate through the data and add to the document
    
        for index, row in df.iterrows():
            doc.add_paragraph(f"Number: {row['number']}")
            doc.add_paragraph(f"Speech: {row['speech']}")
            doc.add_paragraph(f"Question: {row['question']}")
            doc.add_paragraph(f"Option A: {row['A']}")
            doc.add_paragraph(f"Option B: {row['B']}")
            doc.add_paragraph(f"Option C: {row['C']}")
            doc.add_paragraph(f"Option D: {row['D']}")
            doc.add_paragraph(f"Correct Answer: {row['answer']}")

            doc.add_paragraph(f"Question Type: {row['question_type']}")
            doc.add_paragraph("---")

        # Save the Document()
        timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
        file_name = f"TOEFL_Listening_Report_{timestamp}.docx"
        doc.save(file_name)
        return file_name

    docx_file = create_docx(data, feedback, radar_chart_path)

    print(f"Report generated: {docx_file}")

def reading_report(r_df_get, user_ans):
    def check_reading(r_df_get, user_ans):
       for key, value in user_ans.items(): 
           r_df_get.loc[r_df_get["number"] == int(key), 'user_ans'] = value
       r_df_get["correct"] = (r_df_get["Ans"] == r_df_get["user_ans"]).astype(int)
       r_score = (r_df_get["correct"].sum())
       return [r_df_get,r_score]
    check=check_reading(r_df_get, user_ans)
    data=check[1]
    df=check[0]
    feedback = get_gemini_feedback(data, -1)
    # Step 4: Create DOCX File
    def create_docx(data, feedback):
        doc = Document()
        doc.add_heading("TOEFL Reading Practice Report", level=1)

        # Add Summary
        doc.add_heading("Summary", level=2)
        doc.add_paragraph(f"Score: {data}")
        doc.add_paragraph("Performance Summary:")
        doc.add_paragraph(feedback.get("summary", "No summary available."))
        doc.add_paragraph("Future Learning Suggestions:")
        doc.add_paragraph(feedback.get("suggestions", "No suggestions available."))

        # Add Question Details
    # Iterate through the data and add to the document
    
        for index, row in df.iterrows():
            doc.add_paragraph(f"Number: {row['number']}")
            doc.add_paragraph(f"Article: {row['Article']}")
            doc.add_paragraph(f"Question: {row['Question']}")
            doc.add_paragraph(f"Option A: {row['A']}")
            doc.add_paragraph(f"Option B: {row['B']}")
            doc.add_paragraph(f"Option C: {row['C']}")
            doc.add_paragraph(f"Option D: {row['D']}")
            doc.add_paragraph(f"Correct Answer: {row['Ans']}")

        # Save the Document()
        timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
        file_name = f"TOEFL_Reading_Report_{timestamp}.docx"
        doc.save(file_name)
        return file_name

    docx_file = create_docx(data, feedback)
    print(f"Report generated: {docx_file}")

def speaking_report(s_list_get):
    # Create a new Word document
    doc = Document()

    # Add the title
    doc.add_heading("TOEFL Speaking Practice Report", level=1)

    # Get and display the score
    score = s_score_get()
    doc.add_heading("Score", level=2)
    doc.add_paragraph(f"Your Score: {score}")

    # Get and display the comment
    comment = s_com_get()
    doc.add_heading("Comments", level=2)
    doc.add_paragraph(comment)

    # Get the questions and answers
    questions = s_q_list_get()
    answers = s_ans_list_get()

    doc.add_heading("Question Details", level=2)

    # Add each question and its corresponding answer
    for i, question in enumerate(questions):
        q_paragraph = doc.add_paragraph()
        q_paragraph.add_run(f"Question {i + 1}: ").bold = True
        q_paragraph.add_run(question)

        a_paragraph = doc.add_paragraph()
        a_paragraph.add_run("Reponses: ").bold = True
        
        user_response = answers[i]
        a_paragraph.add_run(user_response)
    
    # Save the Word document
    timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
    file_name = f"TOEFL_Speaking_Report_{timestamp}.docx"
    doc.save(file_name)
    print(f"Report saved to {file_name}")

def writing_report():
    # Create a new Word document
    doc = Document()

    # Add the title
    doc.add_heading("TOEFL Writing Practice Report", level=1)

    # Get and display the score
    score = w_score_get()
    doc.add_heading("Score", level=2)
    doc.add_paragraph(f"Your Score: {score}")

    # Get and display the comment
    comment = w_com_get()
    doc.add_heading("Comments", level=2)
    doc.add_paragraph(comment)

    # Get the questions and answers
    questions = w_q_list_get()
    answers = w_ans_list_get()

    doc.add_heading("Question Details", level=2)

    # Add each question and its corresponding answer
    for i, question in enumerate(questions):
        q_paragraph = doc.add_paragraph()
        q_paragraph.add_run(f"Question {i + 1}: ").bold = True
        q_paragraph.add_run(question)

        a_paragraph = doc.add_paragraph()
        a_paragraph.add_run("Reponses: ").bold = True
        
        user_response = answers[i]
        a_paragraph.add_run(user_response)

    # Save the Word document
    timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
    file_name = f"TOEFL_Writing_Report_{timestamp}.docx"
    doc.save(file_name)
    print(f"Report saved to {file_name}")