| import gradio as gr |
| from faster_whisper import WhisperModel |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import torch |
| import requests |
| import base64 |
| import tempfile |
| import os |
| import logging |
| import time |
| from datetime import datetime |
| from concurrent.futures import ThreadPoolExecutor |
| from html.parser import HTMLParser |
| from fastapi import FastAPI, Request |
| from fastapi.responses import JSONResponse |
| import uvicorn |
|
|
| |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s') |
| logger = logging.getLogger(__name__) |
|
|
| |
| logger.info("Loading Whisper-tiny...") |
| whisper_model = WhisperModel("tiny", device="cpu", compute_type="int8") |
|
|
| logger.info("Loading SmolLM2-360M-Instruct...") |
| model_name = "HuggingFaceTB/SmolLM2-360M-Instruct" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype=torch.float32, |
| device_map="cpu", |
| low_cpu_mem_usage=True |
| ) |
|
|
| logger.info("All models loaded!") |
|
|
| TAVILY_API_KEY = os.getenv('TAVILY_API_KEY', '') |
| BRAVE_API_KEY = os.getenv('BRAVE_API_KEY', '') |
|
|
| def search_parallel(query): |
| """Simplified search - just DuckDuckGo for speed""" |
| logger.info("[SEARCH] Starting...") |
| try: |
| response = requests.get( |
| 'https://html.duckduckgo.com/html/', |
| params={'q': query}, |
| headers={'User-Agent': 'Mozilla/5.0'}, |
| timeout=1.5 |
| ) |
| if response.status_code == 200: |
| class DDGParser(HTMLParser): |
| def __init__(self): |
| super().__init__() |
| self.results = [] |
| self.in_result = False |
| self.current_text = "" |
| |
| def handle_starttag(self, tag, attrs): |
| if tag == 'a' and any(k == 'class' and 'result__a' in v for k, v in attrs): |
| self.in_result = True |
| |
| def handle_data(self, data): |
| if self.in_result and data.strip(): |
| self.current_text += data.strip() + " " |
| |
| def handle_endtag(self, tag): |
| if tag == 'a' and self.in_result: |
| if self.current_text: |
| self.results.append(self.current_text.strip()[:120]) |
| self.current_text = "" |
| self.in_result = False |
| |
| parser = DDGParser() |
| parser.feed(response.text) |
| result = "\n".join([f"• {r}" for r in parser.results[:2]]) if parser.results else "No results" |
| logger.info("[SEARCH] ✓") |
| return result, "DuckDuckGo" |
| except: |
| pass |
| return "No search results", "None" |
|
|
| def generate_answer(text_input): |
| """Main answer generation""" |
| logger.info(f"[AI] Question: {text_input[:60]}...") |
| |
| try: |
| if not text_input or not text_input.strip(): |
| return "No input provided" |
| |
| current_date = datetime.now().strftime("%B %d, %Y") |
| |
| |
| search_start = time.time() |
| search_results, search_engine = search_parallel(text_input) |
| logger.info(f"[AI] Search: {time.time()-search_start:.2f}s") |
| |
| |
| messages = [ |
| {"role": "system", "content": f"Today is {current_date}. Answer briefly using search results (60-80 words)."}, |
| {"role": "user", "content": f"Search:\n{search_results}\n\nQ: {text_input}\nA:"} |
| ] |
| |
| prompt = f"<|im_start|>system\n{messages[0]['content']}<|im_end|>\n<|im_start|>user\n{messages[1]['content']}<|im_end|>\n<|im_start|>assistant\n" |
| |
| gen_start = time.time() |
| inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=800) |
| |
| with torch.no_grad(): |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=80, |
| temperature=0.7, |
| do_sample=True, |
| top_p=0.9, |
| top_k=40, |
| repetition_penalty=1.15, |
| pad_token_id=tokenizer.eos_token_id |
| ) |
| |
| answer = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip() |
| logger.info(f"[AI] Gen: {time.time()-gen_start:.2f}s | ✓") |
| |
| return f"{answer}\n\n**Source:** {search_engine}" |
| |
| except Exception as e: |
| logger.error(f"[AI] Error: {str(e)}") |
| return f"Error: {str(e)}" |
|
|
| def transcribe_audio_base64(audio_base64): |
| """Transcribe audio""" |
| logger.info("[STT] Start") |
| try: |
| audio_bytes = base64.b64decode(audio_base64) |
| |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio: |
| temp_audio.write(audio_bytes) |
| temp_path = temp_audio.name |
| |
| segments, _ = whisper_model.transcribe(temp_path, language="en", beam_size=1) |
| transcription = " ".join([seg.text for seg in segments]) |
| os.unlink(temp_path) |
| |
| logger.info("[STT] ✓") |
| return transcription.strip() |
| |
| except Exception as e: |
| logger.error(f"[STT] Error: {str(e)}") |
| return "" |
|
|
| |
| app = FastAPI() |
|
|
| @app.post("/api/stt") |
| async def api_stt(request: Request): |
| """Direct STT endpoint for Pluely""" |
| try: |
| body = await request.json() |
| logger.info(f"[API STT] Received: {body}") |
| |
| audio_base64 = body.get("audio", "") |
| if not audio_base64: |
| return JSONResponse({"error": "No audio data"}, status_code=400) |
| |
| text = transcribe_audio_base64(audio_base64) |
| return JSONResponse({"text": text}) |
| |
| except Exception as e: |
| logger.error(f"[API STT] Error: {str(e)}") |
| return JSONResponse({"error": str(e)}, status_code=500) |
|
|
| @app.post("/api/ai") |
| async def api_ai(request: Request): |
| """Direct AI endpoint for Pluely""" |
| try: |
| body = await request.json() |
| logger.info(f"[API AI] Received: {body}") |
| |
| question = body.get("text", "") |
| if not question: |
| return JSONResponse({"error": "No text provided"}, status_code=400) |
| |
| answer = generate_answer(question) |
| return JSONResponse({"answer": answer}) |
| |
| except Exception as e: |
| logger.error(f"[API AI] Error: {str(e)}") |
| return JSONResponse({"error": str(e)}, status_code=500) |
|
|
| @app.get("/health") |
| async def health(): |
| """Health check""" |
| return {"status": "ok", "model": "SmolLM2-360M"} |
|
|
| |
| with gr.Blocks(title="Fast Q&A", theme=gr.themes.Soft()) as demo: |
| gr.Markdown(""" |
| # ⚡ Ultra-Fast Q&A System |
| **SmolLM2-360M** + **Direct REST API** for Pluely |
| |
| ## Pluely Configuration: |
| |
| ### STT Endpoint: |
| ``` |
| curl -X POST https://archcoder-basic-app.hf.space/api/stt -H "Content-Type: application/json" -d '{"audio": "{{AUDIO_BASE64}}"}' |
| ``` |
| **Response Path:** `text` |
| |
| ### AI Endpoint: |
| ``` |
| curl -X POST https://archcoder-basic-app.hf.space/api/ai -H "Content-Type: application/json" -d '{"text": "{{TEXT}}"}' |
| ``` |
| **Response Path:** `answer` |
| """) |
| |
| with gr.Tab("Test"): |
| with gr.Row(): |
| test_input = gr.Textbox(label="Question", placeholder="Ask anything...") |
| test_btn = gr.Button("🚀 Test") |
| test_output = gr.Textbox(label="Answer", lines=8) |
| |
| test_btn.click(fn=generate_answer, inputs=[test_input], outputs=[test_output]) |
|
|
| |
| app = gr.mount_gradio_app(app, demo, path="/") |
|
|
| if __name__ == "__main__": |
| uvicorn.run(app, host="0.0.0.0", port=7860) |
|
|
