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import cv2 |
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import time |
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import csv |
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import gradio as gr |
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import numpy as np |
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import sounddevice as sd |
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import soundfile as sf |
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from deepface import DeepFace |
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import tempfile |
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import os |
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from scipy.io.wavfile import write as write_wav |
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SAMPLE_RATE = 16000 |
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DURATION = 30 |
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def record_audio(filename="audio_recording.wav"): |
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print("Recording audio for 30 seconds...") |
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audio = sd.rec(int(SAMPLE_RATE * DURATION), samplerate=SAMPLE_RATE, channels=1, dtype='int16') |
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sd.wait() |
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write_wav(filename, SAMPLE_RATE, audio) |
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return filename |
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def map_emotions_to_metrics(emotion): |
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valence = emotion.get("happy", 0) / 100 |
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arousal = (emotion.get("angry", 0) + emotion.get("surprise", 0)) / 200 |
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stress_index = emotion.get("fear", 0) / 100 |
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engagement_level = 1 - emotion.get("neutral", 0) / 100 |
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dominance = (emotion.get("angry", 0) + emotion.get("disgust", 0)) / 200 |
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return valence, arousal, dominance, stress_index, engagement_level |
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def analyze_inputs(): |
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cap = cv2.VideoCapture(0) |
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start_time = time.time() |
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data = [] |
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audio_file_path = os.path.join(tempfile.gettempdir(), "temp_audio.wav") |
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record_audio(audio_file_path) |
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for i in range(DURATION): |
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ret, frame = cap.read() |
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if not ret: |
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continue |
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try: |
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result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False) |
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emotion = result[0]['emotion'] |
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valence, arousal, dominance, stress_index, engagement_level = map_emotions_to_metrics(emotion) |
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data.append([ |
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round(time.time() - start_time, 2), |
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round(valence, 3), |
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round(arousal, 3), |
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round(dominance, 3), |
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round(stress_index, 3), |
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round(engagement_level, 3) |
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]) |
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except Exception as e: |
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print("Error analyzing frame:", e) |
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time.sleep(1) |
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cap.release() |
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csv_path = os.path.join(tempfile.gettempdir(), "emotional_metrics.csv") |
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with open(csv_path, mode='w', newline='') as f: |
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writer = csv.writer(f) |
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writer.writerow(["timestamp", "valence", "arousal", "dominance", "stress_index", "engagement_level"]) |
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writer.writerows(data) |
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return csv_path |
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def demo_interface(campaign_text): |
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csv_output = analyze_inputs() |
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return csv_output |
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demo = gr.Interface( |
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fn=demo_interface, |
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inputs=gr.Textbox(label="Enter Campaign Description", lines=2, placeholder="Describe your campaign..."), |
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outputs=gr.File(label="Download Emotional Metrics (CSV Output)"), |
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title="📊 Complex Campaign Emotion Response Engine", |
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description="This demo captures webcam and microphone input for 30 seconds. It analyzes facial expressions using DeepFace and records audio. The output is a downloadable CSV of emotional metrics over time." |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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