Update app.py

app.py

@@ -1,104 +1,22 @@
-import os
-import sys
-import time
-import json
-import logging
-import traceback
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel, Field
-from contextlib import asynccontextmanager
-from typing import Dict, Any
-
-# ======= ЛОГИРОВАНИЕ =======
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(message)s",
-    handlers=[logging.StreamHandler(sys.stderr)],
+import gradio as gr
+import huggingface_hub
+from transformers import pipeline
+
+# Загружаем модель для примера (можно заменить на свою)
+generator = pipeline("text-generation", model="distilgpt2")
+
+def chat_fn(prompt):
+    response = generator(prompt, max_length=50, num_return_sequences=1)[0]["generated_text"]
+    return f"🤖 Ответ модели:\n{response}\n\n📦 huggingface-hub версия: {huggingface_hub.__version__}"
+
+# Интерфейс Gradio
+demo = gr.Interface(
+    fn=chat_fn,
+    inputs="text",
+    outputs="text",
+    title="Eroha AgentAPI — стабильный Space",
+    description="Исправленный runtime с фиксированной версией huggingface-hub==0.23.4"
 )
-logger = logging.getLogger("eroha-api")
-
-# ======= ИМПОРТЫ =======
-try:
-    from transformers import pipeline
-    from langdetect import detect
-except Exception as e:
-    logger.error("[ImportError] transformers/langdetect not available: %s", e, exc_info=True)
-    pipeline = None
-    def detect(text): return "en"
-
-# ======= НАСТРОЙКИ =======
-HF_HOME = "/tmp/huggingface"
-os.environ["HF_HOME"] = HF_HOME
-os.makedirs(HF_HOME, exist_ok=True)
-
-# ======= МОДЕЛИ =======
-_model_cache: Dict[str, Any] = {}
-
-def get_model(lang: str):
-    if pipeline is None:
-        raise RuntimeError("Transformers pipeline is not available")
-    if lang in _model_cache:
-        return _model_cache[lang]
-    model_map = {
-        "ru": "IlyaGusev/mbart_ru_sum_gazeta",
-        "en": "facebook/bart-large-cnn",
-    }
-    model_name = model_map.get(lang, "facebook/bart-large-cnn")
-    model = pipeline("summarization", model=model_name, device=-1)
-    _model_cache[lang] = model
-    return model
-
-# ======= FastAPI =======
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    start = time.time()
-    logger.info("[Startup] warming up models...")
-    for lang in ("en", "ru"):
-        try: get_model(lang)
-        except Exception as e: logger.error("Warmup failed: %s", e)
-    yield
-    logger.info("[Shutdown] done")
-
-app = FastAPI(title="Eroha Agent API", version="v3.5", lifespan=lifespan)
-app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
-
-# ======= МОДЕЛИ ЗАПРОСОВ =======
-class SummarizeRequest(BaseModel):
-    text: str = Field(..., min_length=3, max_length=1_000_000)
-
-class MemoryRequest(BaseModel):
-    key: str
-    content: str
-
-# ======= ЭНДПОИНТЫ =======
-@app.get("/")
-async def root():
-    return {"status": "ok", "version": "v3.5"}
-
-@app.get("/ping")
-async def ping():
-    return {"status": "healthy", "cache": list(_model_cache.keys())}
-
-@app.post("/summarize")
-async def summarize(req: SummarizeRequest):
-    lang = "ru" if "а" in req.text.lower() else "en"
-    model = get_model(lang)
-    result = model(req.text[:2000], max_length=180, min_length=50, do_sample=False)
-    return {"summary": result[0]["summary_text"].strip(), "lang": lang}
-
-# ======= MEMORY API =======
-@app.post("/memorize")
-async def memorize(req: MemoryRequest):
-    with open("memory.json", "a") as f:
-        f.write(json.dumps(req.dict(), ensure_ascii=False) + "\\n")
-    return {"status": "saved"}
 
-@app.post("/retrieve")
-async def retrieve(req: MemoryRequest):
-    if not os.path.exists("memory.json"):
-        return {"found": []}
-    with open("memory.json", "r") as f:
-        lines = [json.loads(l) for l in f]
-    found = [l for l in lines if req.key.lower() in l["key"].lower()]
-    return {"found": found}
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)