import whisper
import gradio as gr
from TTS.api import TTS
from groq import Groq

# ✅ Hardcoded Groq API key (for local use only)
GROQ_API_KEY = "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"

# ✅ Initialize Groq API client
client = Groq(api_key=GROQ_API_KEY)

# ✅ Load Whisper model (Speech-to-Text)
whisper_model = whisper.load_model("base")

# ✅ Load Coqui TTS model (Text-to-Speech)
tts = TTS("tts_models/en/ljspeech/speedy-speech")

# ✅ Chatbot logic
def chatbot(audio_file):
    if not audio_file:
        return "No audio provided.", None

    # 1. Transcribe audio to text
    result = whisper_model.transcribe(audio_file)
    user_text = result["text"]

    # 2. Get chatbot response from Groq (🛠️ Fix incorrect model name)
    chat_completion = client.chat.completions.create(
        messages=[{"role": "user", "content": user_text}],
        model="llama-3-8b-instruct",  # ✅ Valid model name
    )
    response_text = chat_completion.choices[0].message.content

    # 3. Convert response to speech
    audio_output_path = "output.wav"
    tts.tts_to_file(text=response_text, file_path=audio_output_path)

    return response_text, audio_output_path

# ✅ Gradio UI
iface = gr.Interface(
    fn=chatbot,
    inputs=gr.Audio(type="filepath"),
    outputs=[gr.Textbox(label="Chatbot Response"), gr.Audio(label="Voice Output")],
    title="🎙️ Voice Chatbot with Whisper + Groq LLaMA-3",
    description="Upload a voice file, and the chatbot will generate a response in both text and voice.",
)

# ✅ Launch
iface.launch()