meftaul · February 6, 2025 06:12
diff --git a/evaluation-script.py b/evaluation-script.py
 import sys

 def get_score(category, max_score, options):
    print(f"\n{category} ({max_score} points)")
    for score, description in options.items():
        print(f"{score} – {description}")
    while True:
        try:
            score = int(input("Enter score: "))
            if score in options:
                return score
            else:
                print(f"Invalid input. Please enter one of {list(options.keys())}.")
        except ValueError:
            print("Invalid input. Please enter a number.")

 def main():
    print("\n=== Student Project Evaluation CLI ===")
    student_name = input("Enter Student Name: ")
    student_id = input("Enter Student ID: ")

    categories = {
        "Project Understanding & Clarity": (10, {
            10: "Clearly understands and explains the problem, objective, and approach.",
            8: "Mostly clear, with minor gaps in explanation.",
            6: "Some understanding, but key elements are missing.",
            4: "Lacks clarity and coherence in defining the project.",
            2: "No clear understanding of the project."
        }),
        "Data Handling & Preparation": (10, {
            10: "Properly cleaned, preprocessed, and justified all data choices.",
            8: "Good data preparation with minor issues.",
            6: "Some preprocessing done but lacks key steps.",
            4: "Data handling is incomplete or incorrect.",
            2: "No proper data cleaning or preprocessing."
        }),
        "Methodology & Model Selection": (15, {
            15: "Well-justified model choices, including comparisons and tuning.",
            12: "Good selection but lacks explanation for alternative models.",
            9: "Basic model choice with minimal justification.",
            6: "Poor model selection or incorrect methodology.",
            3: "No logical model selection."
        }),
        "Model Performance & Evaluation": (15, {
            15: "Excellent evaluation with proper metrics, analysis, and insights.",
            12: "Good evaluation but lacks detailed analysis.",
            9: "Basic evaluation with limited metrics or interpretation.",
            6: "Poor performance analysis or incorrect evaluation.",
            3: "No proper performance evaluation."
        }),
        "Implementation & Code Quality": (15, {
            15: "Clean, well-structured, and well-commented code.",
            12: "Good code but could improve readability.",
            9: "Functional but lacks structure and comments.",
            6: "Messy or difficult to understand.",
            3: "Non-functional or copied code."
        }),
        "Interpretation & Business Impact": (10, {
            10: "Clearly explains findings and their real-world impact.",
            8: "Good insights but lacks depth.",
            6: "Some insights but minimal connection to the problem.",
            4: "Weak or unclear interpretations.",
            2: "No meaningful insights provided."
        }),
        "Originality & Effort": (10, {
            10: "Fully original work with exceptional effort.",
            8: "Good effort with minor originality concerns.",
            6: "Some effort but lacks innovation.",
            4: "Minimal effort or partial plagiarism.",
            2: "Copied or AI-generated work."
        }),
        "Presentation & Documentation": (10, {
            10: "Well-structured report and/or presentation with clear explanations.",
            8: "Good documentation with minor improvements needed.",
            6: "Basic presentation but lacks details.",
            4: "Poor documentation or missing key sections.",
            2: "No proper presentation or documentation."
        })
    }

    total_score = 0
    scores = {}

    for category, (max_score, options) in categories.items():
        score = get_score(category, max_score, options)
        scores[category] = score
        total_score += score

    print("\n=== Evaluation Summary ===")
    print(f"Student Name: {student_name}")
    print(f"Student ID: {student_id}")
    print("-----------------------------")
    for category, score in scores.items():
        print(f"{category}: {score} points")
    print("-----------------------------")
    print(f"Total Score: {total_score}/100")

 if __name__ == "__main__":
    main()
	import sys

	def get_score(category, max_score, options):
	print(f"\n{category} ({max_score} points)")
	for score, description in options.items():
	print(f"{score} – {description}")
	while True:
	try:
	score = int(input("Enter score: "))
	if score in options:
	return score
	else:
	print(f"Invalid input. Please enter one of {list(options.keys())}.")
	except ValueError:
	print("Invalid input. Please enter a number.")

	def main():
	print("\n=== Student Project Evaluation CLI ===")
	student_name = input("Enter Student Name: ")
	student_id = input("Enter Student ID: ")

	categories = {
	"Project Understanding & Clarity": (10, {
	10: "Clearly understands and explains the problem, objective, and approach.",
	8: "Mostly clear, with minor gaps in explanation.",
	6: "Some understanding, but key elements are missing.",
	4: "Lacks clarity and coherence in defining the project.",
	2: "No clear understanding of the project."
	}),
	"Data Handling & Preparation": (10, {
	10: "Properly cleaned, preprocessed, and justified all data choices.",
	8: "Good data preparation with minor issues.",
	6: "Some preprocessing done but lacks key steps.",
	4: "Data handling is incomplete or incorrect.",
	2: "No proper data cleaning or preprocessing."
	}),
	"Methodology & Model Selection": (15, {
	15: "Well-justified model choices, including comparisons and tuning.",
	12: "Good selection but lacks explanation for alternative models.",
	9: "Basic model choice with minimal justification.",
	6: "Poor model selection or incorrect methodology.",
	3: "No logical model selection."
	}),
	"Model Performance & Evaluation": (15, {
	15: "Excellent evaluation with proper metrics, analysis, and insights.",
	12: "Good evaluation but lacks detailed analysis.",
	9: "Basic evaluation with limited metrics or interpretation.",
	6: "Poor performance analysis or incorrect evaluation.",
	3: "No proper performance evaluation."
	}),
	"Implementation & Code Quality": (15, {
	15: "Clean, well-structured, and well-commented code.",
	12: "Good code but could improve readability.",
	9: "Functional but lacks structure and comments.",
	6: "Messy or difficult to understand.",
	3: "Non-functional or copied code."
	}),
	"Interpretation & Business Impact": (10, {
	10: "Clearly explains findings and their real-world impact.",
	8: "Good insights but lacks depth.",
	6: "Some insights but minimal connection to the problem.",
	4: "Weak or unclear interpretations.",
	2: "No meaningful insights provided."
	}),
	"Originality & Effort": (10, {
	10: "Fully original work with exceptional effort.",
	8: "Good effort with minor originality concerns.",
	6: "Some effort but lacks innovation.",
	4: "Minimal effort or partial plagiarism.",
	2: "Copied or AI-generated work."
	}),
	"Presentation & Documentation": (10, {
	10: "Well-structured report and/or presentation with clear explanations.",
	8: "Good documentation with minor improvements needed.",
	6: "Basic presentation but lacks details.",
	4: "Poor documentation or missing key sections.",
	2: "No proper presentation or documentation."
	})
	}

	total_score = 0
	scores = {}

	for category, (max_score, options) in categories.items():
	score = get_score(category, max_score, options)
	scores[category] = score
	total_score += score

	print("\n=== Evaluation Summary ===")
	print(f"Student Name: {student_name}")
	print(f"Student ID: {student_id}")
	print("-----------------------------")
	for category, score in scores.items():
	print(f"{category}: {score} points")
	print("-----------------------------")
	print(f"Total Score: {total_score}/100")

	if __name__ == "__main__":
	main()
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