Delete qwen.py

qwen.py

@@ -1,95 +0,0 @@
-import logging
-from typing import List, Dict, Optional, Tuple
-
-import torch
-# from transformers import pipeline
-from huggingface_hub import InferenceClient
-
-from config.config import token, SYSTEM_PROMPT
-from services.whisper import generate_speech, transcribe
-from services.search import WebSearcher
-
-logger = logging.getLogger(__name__)
-
-searcher = WebSearcher()
-
-# Qwen Configuration
-model_kwargs = {
-    "low_cpu_mem_usage": True,
-    "torch_dtype": torch.float32,
-    'use_cache': True
-}
-client = InferenceClient(
-    model="Qwen/Qwen2.5-0.5B-Instruct",
-    token=token
-    # trust_remote_code=True,
-    # device=device,
-    # model_kwargs=model_kwargs
-)
-
-async def respond(
-        audio: Optional[str] = None,
-        text: Optional[str] = None,
-        do_search: bool = False,
-        history: List[Dict] = None
-    ) -> Tuple[Optional[str], str]:
-    try:
-        if text:
-            user_text = text.strip()
-        elif audio:
-            user_text = await transcribe(audio)
-        else:
-            return None, "No input provided"
-
-        # Build conversation context
-        messages = []
-        messages.append({"role": "system", "content": SYSTEM_PROMPT})
-        
-        if history:
-            messages.extend(history)
-        
-        # Format message history for Qwen
-        prompt = ""
-        for msg in messages:
-            role = msg["role"]
-            content = msg["content"]
-            prompt += f"<|im_start|>{role}\n{content}<|im_end|>\n"
-
-        # Add current user message
-        prompt += f"<|im_start|>user\n{user_text}<|im_end|>\n<|im_start|>assistant\n"
-
-        # Add web-search context if enabled
-        if do_search:
-            results = searcher.search(user_text)
-            if results:
-                search_context = "Based on search results:\n"
-                for result in results:
-                    snippet = result['content'][:5000].strip()
-                    search_context += f"{snippet}\n"
-                prompt = prompt.replace(SYSTEM_PROMPT, f"{SYSTEM_PROMPT}\n{search_context}")
-
-        # Generate response
-        reply = client.text_generation(
-            prompt,
-            max_new_tokens=300,
-            do_sample=True,
-            temperature=0.7,
-            top_p=0.9,
-            return_full_text=False
-        )
-
-        # Extract and clean assistant response
-        assistant_response = reply  # Reply is already the generated text string
-        if "<|im_start|>assistant\n" in assistant_response:
-            assistant_response = assistant_response.split("<|im_start|>assistant\n")[-1]
-        if "<|im_end|>" in assistant_response:
-            assistant_response = assistant_response.split("<|im_end|>")[0]
-        assistant_response = assistant_response.strip()
-
-        # Convert response to speech
-        audio_path = await generate_speech(assistant_response)
-        return audio_path, assistant_response
-
-    except Exception as e:
-        logger.error(f"Error in respond: {str(e)}")
-        return None, "Sorry, I encountered an error"