app.py

import torch
import torchaudio
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import gradio as gr

# 1️⃣ Load Whisper model for Speech-to-Text
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small")

# 2️⃣ Load Qwen-style LLM for text response
model_name = "Qwen/Qwen1.5-0.5B-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto",torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)

# Bot reply generator
def generate_response(user_text):
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": user_text}
    ]
    
    # Use chat template formatting
    input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
    
    with torch.no_grad():
        output = model.generate(input_ids, max_new_tokens=150, pad_token_id=tokenizer.eos_token_id)
    
    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
    
    # Return only the assistant's message
    response = decoded.split("assistant")[-1].strip().replace(":", "").strip()
    
    return response


# Complete pipeline: Audio → Text → Response
def audio_to_bot_response(audio_path):
    print("[INFO] Transcribing audio...")
    result = asr_pipe(audio_path)
    user_text = result['text']
    
    
    print(f"[INFO] Transcribed: {user_text}")
    response = generate_response(user_text)
    
    return f"👤 You said: {user_text}\n🤖 Bot: {response}"

interface = gr.Interface(
    fn=audio_to_bot_response,
    inputs=gr.Audio(sources=["microphone"], type="filepath"),
    outputs="text",
    title="🎙 Voice to AI Bot Response",
    description="Speak into the mic. The AI will transcribe and respond."
)

interface.launch()