Initial: AI Job Interview Bot for Computer Science with 496+ questions

app.py

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+import gradio as gr
+import random
+import json
+from datasets import load_dataset
+
+try:
+    ds = load_dataset('Aiman1234/Interview-questions')
+    questions_data = ds['train']
+except:
+    questions_data = None
+
+class InterviewBot:
+    def __init__(self):
+        self.current_question = None
+        self.score = 0
+        self.total_questions = 0
+        self.performance_notes = []
+        self.answers_given = []
+        
+    def get_next_question(self, difficulty='Easy'):
+        if questions_data is None:
+            return "Dataset not available", "N/A", "N/A"
+        
+        filtered = [q for q in questions_data if q['level'] == difficulty]
+        if not filtered:
+            filtered = questions_data
+            
+        self.current_question = random.choice(filtered)
+        self.total_questions += 1
+        
+        return (
+            self.current_question['Questions'],
+            self.current_question['language'],
+            self.current_question['level']
+        )
+    
+    def evaluate_answer(self, user_answer):
+        if not self.current_question:
+            return "Please get a question first!"
+        
+        answer_length = len(user_answer.split())
+        correct_answer_length = len(self.current_question['Answers'].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, Your answer: {answer_length} words"
+            self.score += 1
+        else:
+            feedback = f"Consider expanding your answer. Your response should be more detailed.\nExpected length: ~{correct_answer_length} words, Your answer: {answer_length} words"
+        
+        self.answers_given.append({
+            'question': self.current_question['Questions'],
+            'your_answer': user_answer,
+            'similarity': similarity,
+            'correct_answer': self.current_question['Answers']
+        })
+        
+        return feedback
+    
+    def get_performance_analysis(self):
+        if self.total_questions == 0:
+            return "No questions answered yet!"
+        
+        accuracy = (self.score / self.total_questions) * 100
+        
+        analysis = f"""
+        === PERFORMANCE ANALYSIS ===
+        
+        Total Questions: {self.total_questions}
+        Correct Answers: {self.score}
+        Accuracy: {accuracy:.1f}%
+        
+        STRENGTHS:
+        - You completed {self.total_questions} interview questions
+        - Your accuracy rate is {accuracy:.1f}%
+        
+        AREAS FOR IMPROVEMENT:
+        - Focus on providing detailed and structured answers
+        - Practice technical concepts and definitions
+        - Improve answer articulation and clarity
+        
+        RECOMMENDATIONS:
+        1. Review the expected answers for failed questions
+        2. Practice similar questions in your weak areas
+        3. Work on concise but comprehensive responses
+        4. Practice explaining complex concepts simply
+        """
+        
+        return analysis
+
+bot = InterviewBot()
+
+def start_interview(difficulty):
+    question, language, level = bot.get_next_question(difficulty)
+    return f"Question ({language} - {level}): {question}"
+
+def submit_answer(answer_text):
+    if not answer_text.strip():
+        return "Please provide an answer!"
+    evaluation = bot.evaluate_answer(answer_text)
+    return evaluation
+
+def show_analysis():
+    return bot.get_performance_analysis()
+
+with gr.Blocks(title="AI Job Interview Bot - Computer Science") as demo:
+    gr.Markdown("""
+    # AI Job Interview Bot for Computer Science
+    
+    Practice technical interview questions with AI-powered feedback!
+    
+    **Features:**
+    - 496+ real interview questions
+    - Multiple difficulty levels
+    - Performance analysis and feedback
+    - Identified strengths and weaknesses
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Interview Settings")
+            difficulty = gr.Radio(
+                choices=['Easy', 'Medium', 'Hard'],
+                value='Easy',
+                label="Select Difficulty Level"
+            )
+            start_btn = gr.Button("Get Next Question", variant="primary")
+            
+        with gr.Column():
+            gr.Markdown("### Current Question")
+            question_display = gr.Textbox(
+                label="Question",
+                interactive=False,
+                lines=4
+            )
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Your Answer")
+            answer_input = gr.Textbox(
+                label="Type your answer here",
+                lines=6,
+                placeholder="Provide your answer to the question..."
+            )
+            submit_btn = gr.Button("Submit Answer", variant="primary")
+            
+        with gr.Column():
+            gr.Markdown("### AI Feedback")
+            feedback_display = gr.Textbox(
+                label="Feedback",
+                interactive=False,
+                lines=6
+            )
+    
+    with gr.Row():
+        analysis_btn = gr.Button("Show Performance Analysis", variant="secondary")
+        analysis_display = gr.Textbox(
+            label="Performance Analysis",
+            interactive=False,
+            lines=15
+        )
+    
+    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()