import gradio as gr
import random
from datasets import load_dataset

questions_data = None

def load_questions():
    global questions_data
    if questions_data is None:
        try:
            ds = load_dataset('Aiman1234/Interview-questions')
            questions_data = ds['train']
            return True
        except Exception as e:
            print(f"Error loading dataset: {e}")
            return False
    return True

class InterviewBot:
    def __init__(self):
        self.current_question = None
        self.score = 0
        self.total_questions = 0
        self.answers_given = []
        
    def get_next_question(self, difficulty='Easy'):
        if not load_questions():
            return "Failed to load questions. Please try again.", "N/A", "N/A"
        
        try:
            filtered = list(questions_data)
            random_q = random.choice(filtered)
            self.current_question = random_q
            self.total_questions += 1
            
            lang = random_q.get('language', 'Unknown')
            level = random_q.get('level', 'Not specified')
            question = random_q.get('Questions', 'No question')
            
            return (
                f"{question}",
                str(lang),
                str(level)
            )
        except Exception as e:
            return f"Error getting question: {str(e)}", "Error", "Error"
    
    def evaluate_answer(self, user_answer):
        if not self.current_question:
            return "Please get a question first!"
        
        try:
            answer_length = len(user_answer.split())
            correct_answer = self.current_question.get('Answers', '')
            correct_answer_length = len(correct_answer.split())
            
            similarity = min(answer_length, correct_answer_length) / max(answer_length, correct_answer_length) if max(answer_length, correct_answer_length) > 0 else 0
            
            if similarity > 0.5:
                feedback = f"Good effort! Your answer covers key points.\nExpected answer length: ~{correct_answer_length} words\nYour answer: {answer_length} words"
                self.score += 1
            else:
                feedback = f"Consider expanding your answer.\nExpected length: ~{correct_answer_length} words\nYour answer: {answer_length} words"
            
            self.answers_given.append({
                'question': self.current_question.get('Questions', ''),
                'your_answer': user_answer,
                'similarity': similarity
            })
            
            return feedback
        except Exception as e:
            return f"Error evaluating answer: {str(e)}"
    
    def get_performance_analysis(self):
        if self.total_questions == 0:
            return "No questions answered yet!"
        
        try:
            accuracy = (self.score / self.total_questions) * 100
            
            analysis = f"""
PERFORMANCE ANALYSIS
{'='*50}
Total Questions Attempted: {self.total_questions}
Correct Answers: {self.score}
Accuracy Rate: {accuracy:.1f}%

STRENGTHS:
- Successfully completed {self.total_questions} interview questions
- Current accuracy rate: {accuracy:.1f}%

AREAS FOR IMPROVEMENT:
- Focus on providing detailed answers
- Practice technical concepts
- Improve clarity in explanations

RECOMMENDATIONS:
1. Review expected answers for questions
2. Practice similar questions
3. Work on comprehensive responses
4. Explain concepts clearly
"""
            return analysis
        except Exception as e:
            return f"Error generating analysis: {str(e)}"

bot = InterviewBot()

def start_interview(difficulty):
    try:
        question, language, level = bot.get_next_question(difficulty)
        return f"{question}\n\n(Language: {language} | Level: {level})"
    except Exception as e:
        return f"Error: {str(e)}"

def submit_answer(answer_text):
    if not answer_text.strip():
        return "Please provide an answer!"
    return bot.evaluate_answer(answer_text)

def show_analysis():
    return bot.get_performance_analysis()

with gr.Blocks(title="AI Interview Bot CS") as demo:
    gr.Markdown("# AI Job Interview Bot - Computer Science")
    gr.Markdown("Practice 496+ technical interview questions with AI feedback")
    
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Settings")
            difficulty = gr.Radio(
                choices=['Easy', 'Medium', 'Hard'],
                value='Easy',
                label="Difficulty"
            )
            start_btn = gr.Button("Get Next Question", variant="primary")
            
        with gr.Column():
            gr.Markdown("### Question")
            question_display = gr.Textbox(
                label="Question",
                interactive=False,
                lines=5
            )
    
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Your Answer")
            answer_input = gr.Textbox(
                lines=6,
                placeholder="Your answer..."
            )
            submit_btn = gr.Button("Submit Answer", variant="primary")
            
        with gr.Column():
            gr.Markdown("### Feedback")
            feedback_display = gr.Textbox(
                interactive=False,
                lines=6
            )
    
    analysis_btn = gr.Button("Performance Analysis", variant="secondary")
    analysis_display = gr.Textbox(
        interactive=False,
        lines=12
    )
    
    start_btn.click(
        fn=start_interview,
        inputs=[difficulty],
        outputs=[question_display]
    )
    
    submit_btn.click(
        fn=submit_answer,
        inputs=[answer_input],
        outputs=[feedback_display]
    )
    
    analysis_btn.click(
        fn=show_analysis,
        outputs=[analysis_display]
    )

if __name__ == "__main__":
    demo.launch()