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| import gradio as gr | |
| import whisper | |
| from groq import Groq | |
| from gtts import gTTS | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # Load Whisper model | |
| whisper_model = whisper.load_model("small") | |
| # Initialize Groq client | |
| client = Groq(api_key=os.getenv("GROQ_API_KEY").strip()) | |
| # Emotion to color and emoji mapping | |
| emotion_ui = { | |
| "happy": ("#FFF9C4", "😃"), | |
| "sad": ("#BBDEFB", "😔"), | |
| "angry": ("#FFCDD2", "😡"), | |
| "confused": ("#E1BEE7", "🤔"), | |
| "fearful": ("#D1C4E9", "😱"), | |
| "surprised": ("#C8E6C9", "😮"), | |
| "neutral": ("#F5F5F5", "😐"), | |
| } | |
| def get_emotion_from_text(text): | |
| response = client.chat.completions.create( | |
| model="llama3-70b-8192", | |
| messages=[ | |
| {"role": "system", "content": "You are an emotion detection assistant. Respond with only one emotion like: happy, sad, angry, fearful, surprised, confused, or neutral."}, | |
| {"role": "user", "content": text} | |
| ] | |
| ) | |
| return response.choices[0].message.content.strip().lower() | |
| def voice_assistant(audio): | |
| # Step 1: Transcribe | |
| result = whisper_model.transcribe(audio) | |
| transcription = result["text"] | |
| # Step 2: Detect emotion | |
| emotion = get_emotion_from_text(transcription) | |
| color, emoji = emotion_ui.get(emotion, ("#FFFFFF", "🤷")) | |
| # Step 3: Response based on emotion | |
| responses = { | |
| "angry": "I'm really sorry you're facing trouble. Let’s sort this out right away.", | |
| "happy": "I'm so glad to hear that! Let me know how I can help further.", | |
| "sad": "I’m here for you. Let’s see how I can assist.", | |
| "confused": "No worries, I’ll try to explain things more clearly.", | |
| "fearful": "It’s okay to be scared. I’m here to help.", | |
| "surprised": "Wow, that’s unexpected! Let’s explore more.", | |
| "neutral": "Got it! How can I assist you today?", | |
| } | |
| response = responses.get(emotion, "Got it! How can I assist you today?") | |
| # Step 4: Generate voice response | |
| tts = gTTS(text=response) | |
| tts_path = "response.mp3" | |
| tts.save(tts_path) | |
| # Return values | |
| return ( | |
| transcription, | |
| f"{emoji} {emotion.capitalize()}", | |
| response, | |
| color, | |
| tts_path | |
| ) | |
| # Gradio Blocks with dynamic background | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🤖 Emotion-Adaptive Voice Assistant") | |
| audio_input = gr.Audio(type="filepath", label="🎤 Speak Now") | |
| with gr.Row(): | |
| transcription_output = gr.Textbox(label="📝 Transcription") | |
| emotion_output = gr.Textbox(label="🧠 Emotion") | |
| response_output = gr.Textbox(label="💡 Assistant Reply") | |
| voice_output = gr.Audio(label="🎧 Assistant Voice", interactive=False) | |
| color_box = gr.HTML() | |
| def wrapped(audio): | |
| transcription, emotion, response, color, voice = voice_assistant(audio) | |
| box_style = f'<div style="background-color:{color}; padding:20px; border-radius:10px">Emotion color applied: {emotion}</div>' | |
| return transcription, emotion, response, voice, box_style | |
| audio_input.change(fn=wrapped, inputs=audio_input, | |
| outputs=[transcription_output, emotion_output, response_output, voice_output, color_box]) | |
| if __name__ == "__main__": | |
| demo.launch() | |