import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline

# Load LLM
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Load Speech-to-Text (STT) model
stt_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-base")

# Load Text-to-Speech (TTS) model (using a public model without token requirements)
tts_pipeline = pipeline("text-to-speech", model="facebook/mms-tts-eng")

def respond(audio, message, history, system_message, max_tokens, temperature, top_p):
    # Convert speech to text if audio input is provided
    if audio is not None:
        message = stt_pipeline(audio)["text"]

    # Prepare conversation history
    messages = [{"role": "system", "content": system_message}]
    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})
    
    messages.append({"role": "user", "content": message})

    # Generate response from LLM
    response = ""
    for msg in client.chat_completion(
        messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p
    ):
        token = msg.choices[0].delta.content
        response += token

    # Convert chatbot response to speech
    speech = tts_pipeline(response)

    return history + [(message, response)], speech["audio"]

# Gradio Interface using Blocks
with gr.Blocks() as demo:
    gr.Markdown("# 🎙️ Chatbot with Speech & Text")

    with gr.Row():
        audio_input = gr.Audio(type="filepath", label="🎤 Speak (or type below)")
        text_input = gr.Textbox(label="💬 Or type your message")

    chatbot = gr.Chatbot(label="Chat History")

    with gr.Row():
        system_msg = gr.Textbox(value="You are a friendly AI chatbot.", label="System Message")
        max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens")
        temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
        top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")

    audio_output = gr.Audio(label="🔊 AI Response")

    submit = gr.Button("Send")

    submit.click(
        respond,
        inputs=[audio_input, text_input, chatbot, system_msg, max_tokens, temperature, top_p],
        outputs=[chatbot, audio_output]
    )

if __name__ == "__main__":
    demo.launch()