Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from groq import Groq | |
| import os | |
| import time | |
| import whisper | |
| from gtts import gTTS | |
| api_key = os.getenv('GROQ_API_KEY') | |
| # Initialize Groq client | |
| client = Groq(api_key=api_key) | |
| # Load Whisper model | |
| whisper_model = whisper.load_model("base") # You can use "tiny", "base", "small", "medium", or "large" | |
| # Function to convert audio to text | |
| def audio_to_text(audio_file): | |
| # Use Whisper to transcribe audio | |
| result = whisper_model.transcribe(audio_file) | |
| return result['text'] | |
| # Function to convert text to audio | |
| def text_to_audio(text): | |
| tts = gTTS(text) | |
| audio_file = "output_audio.mp3" | |
| tts.save(audio_file) | |
| return audio_file | |
| # Function to generate responses with error handling | |
| def generate_response(user_input, chat_history: list): | |
| try: | |
| # Prepare messages with chat history | |
| messages = [{"role": "system", "content": "You are a mental health assistant. Your responses should be empathetic, non-judgmental, and provide helpful advice based on mental health principles. Always encourage seeking professional help when needed. Your responses should look human as well."}] | |
| # Iterate through chat history and add user and assistant messages | |
| for message in chat_history: | |
| # Ensure that each message contains only 'role' and 'content' keys | |
| if 'role' in message and 'content' in message: | |
| messages.append({"role": message["role"], "content": message["content"]}) | |
| else: | |
| print(f"Skipping invalid message: {message}") | |
| messages.append({"role": "user", "content": user_input}) # Add the current user message | |
| # Call Groq API to get a response from LLaMA | |
| chat_completion = client.chat.completions.create( | |
| messages=messages, | |
| model='llama-3.1-70b-versatile' | |
| ) | |
| # Extract response | |
| response = chat_completion.choices[0].message.content | |
| return response, chat_history # Ensure you return both response and chat_history | |
| except Exception as e: | |
| print(f"Error occurred: {e}") # Print error to console for debugging | |
| return "An error occurred while generating the response. Please try again.", chat_history | |
| def gradio_interface(): | |
| with gr.Blocks() as demo: | |
| # Initialize chat history | |
| chat_history = [] | |
| # Create input components | |
| gr.Markdown("## A Mental Health Chatbot") | |
| chatbot = gr.Chatbot(type="messages") | |
| msg = gr.Textbox(placeholder="Type your message here...") | |
| audio_input = gr.Audio(type="filepath", label="Speak your message") | |
| clear = gr.Button("Clear") | |
| # User message submission function | |
| def user(user_message, history: list): | |
| history.append({"role": "user", "content": user_message}) | |
| return "", history | |
| def bot(history: list): | |
| if len(history) > 0: | |
| user_input = history[-1]["content"] | |
| response, updated_history = generate_response(user_input, history) | |
| history = updated_history | |
| history.append({"role": "assistant", "content": ""}) | |
| for character in response: | |
| history[-1]['content'] += character | |
| time.sleep(0.02) | |
| yield history | |
| # Speech-to-text processing | |
| def process_audio(audio_file, history): | |
| if audio_file: | |
| transcription = audio_to_text(audio_file) # Convert audio to text | |
| if transcription: | |
| history.append({"role": "user", "content": transcription}) | |
| else: | |
| history.append({"role": "assistant", "content": "I couldn't understand your audio. Please try again."}) | |
| return history | |
| # Set up interaction flow: | |
| msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot) | |
| audio_input.change(process_audio, [audio_input, chatbot], chatbot).then(bot, chatbot, chatbot) # Ensure bot is triggered | |
| clear.click(lambda: [], None, chatbot, queue=False) | |
| demo.launch() | |
| # Run the interface | |
| gradio_interface() |