Delete AT.py

AT.py

@@ -1,55 +0,0 @@
-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()