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438c749 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | from grammar_analysis import grammar_score
from video_processing import extract_frames
from audio_extraction import extract_audio
from speech_to_text import transcribe_audio
from filler_detection import count_filler_words
from emotion_detection import analyze_emotions
from scoring import calculate_emotion_score, calculate_filler_score, final_score
from answer_analysis import calculate_answer_similarity
from scoring import communication_score
from speech_analysis import speech_rate
from keyword_analysis import keyword_score
from eye_contact_analysis import eye_contact_score
from feedback_generator import generate_feedback
from topic_analysis import topic_relevance
from report_generator import generate_interview_report
def run_analysis():
# Step 1: Extract frames
extract_frames("sample.mp4")
# Step 2: Extract audio
extract_audio("sample.mp4")
# Step 3: Speech to text
text = transcribe_audio("output/audio.wav")
keyword, keyword_matches = keyword_score(text)
grammar, grammar_errors = grammar_score(text)
# Step 4: Filler detection
filler_count = count_filler_words(text)
# Step 5: Emotion detection
emotion_counts, emotion_timeline = analyze_emotions()
eye_score, face_frames, total_frames = eye_contact_score()
# Step 6: Scores
emotion_score = calculate_emotion_score(emotion_counts)
filler_score = calculate_filler_score(filler_count, text)
answer_score = calculate_answer_similarity(
text,
"Python is a high-level programming language used for web development, data science, and automation."
)
comm_score = communication_score(text)
rate = speech_rate("output/audio.wav", text)
topic_score = topic_relevance(text, "Explain Python programming language")
feedback = []
if filler_count > 5:
feedback.append("Reduce filler words")
if emotion_score < 50:
feedback.append("Improve facial expressions")
if answer_score < 60:
feedback.append("Improve answer quality")
if comm_score < 60:
feedback.append("Improve communication clarity")
if not feedback:
feedback.append("Excellent performance!")
final = final_score(emotion_score, filler_score)
feedback_report = generate_feedback({
"grammar_score": grammar,
"filler_count": filler_count,
"speech_rate": rate,
"eye_contact_score": eye_score,
"answer_score": answer_score,
})
report = generate_interview_report({
"final_score": final,
"speech_rate": rate,
"grammar_score": grammar,
"eye_contact_score": eye_score,
"answer_score": answer_score,
"filler_count": filler_count,
"topic_score": topic_score
})
return {
"text": text,
"filler_count": filler_count,
"emotions": emotion_counts,
"emotion_score": emotion_score,
"emotion_timeline": emotion_timeline,
"filler_score": filler_score,
"answer_score": answer_score,
"communication_score": comm_score,
"speech_rate": rate,
"grammar_score": grammar,
"grammar_errors": grammar_errors,
"keyword_score": keyword,
"keyword_matches": keyword_matches,
"eye_contact_score": eye_score,
"face_frames": face_frames,
"total_frames": total_frames,
"final_score": final,
"feedback": feedback,
"ai_feedback": feedback_report,
"topic_score": topic_score,
"report": report
}
if __name__ == "__main__":
run_analysis()
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