Update app.py

app.py

@@ -1,83 +1,62 @@
-import os
-os.environ.setdefault("SYSTEM", "spaces")
-
-# ===== Monkey Patch: 修复 gradio_client 的 bool-schema Bug =====
-import gradio_client.utils as _gcu
-
-if hasattr(_gcu, "_json_schema_to_python_type"):
-    _orig = _gcu._json_schema_to_python_type
-    def _safe(schema, defs=None):
-        if isinstance(schema, bool):
-            return "Any"
-        return _orig(schema, defs)
-    _gcu._json_schema_to_python_type = _safe
-
-if hasattr(_gcu, "get_type"):
-    _orig_get_type = _gcu.get_type
-    def _safe_get_type(schema):
-        if not isinstance(schema, dict):
-            return "Any"
-        return _orig_get_type(schema)
-    _gcu.get_type = _safe_get_type
-# ===== Monkey Patch 结束 =====
-
-import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from PIL import Image
-
-# ============================================================
-# 模型初始化
-# ============================================================
-model_id = "vikhyatk/moondream2"
-revision = "2024-08-26"
-
-print("正在加载 Moondream2 模型...")
-tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    trust_remote_code=True,
-    revision=revision,
-    torch_dtype=torch.float32,
-    low_cpu_mem_usage=True,
-)
-print("模型加载完成。")
-
-# ============================================================
-# 推理函数
-# ============================================================
-def answer_question(image: Image.Image, question: str):
-    if image is None:
-        return "⚠️ 请先上传一张作业图片。"
-    if not question or not question.strip():
-        return "⚠️ 请输入你的问题。"
-
-    enc_image = model.encode_image(image)
-    answer = model.answer_question(enc_image, question, tokenizer)
-    return answer
-
-# ============================================================
-# Gradio 界面
-# ============================================================
-demo = gr.Interface(
-    fn=answer_question,
-    inputs=[
-        gr.Image(type="pil", label="📷 上传作业图片"),
-        gr.Textbox(
-            label="💬 你的问题",
-            placeholder="例如：这道题的答案是多少？请逐步解答。",
-            lines=2,
-        ),
-    ],
-    outputs=gr.Textbox(label="🤖 AI 回答", lines=8),
-    title="📚 作业识图 AI (CPU 版)",
-    description="上传作业照片，输入问题，AI 会尝试识别并给出答案。",
-    theme=gr.themes.Soft(),
-)
-
-if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True,
-    )
+from fastapi import FastAPI, UploadFile, File, Form, Depends, HTTPException
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+import auth, storage, ai_search
+
+app = FastAPI(title="云学习平台")
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+@app.on_event("startup")
+def startup():
+    auth.init_db()
+    storage.ensure_bucket()
+
+# ── 用户认证 ──
+@app.post("/auth/register")
+def register(username: str = Form(...), password: str = Form(...)):
+    conn = auth.sqlite3.connect(auth.DB_PATH)
+    try:
+        conn.execute(
+            "INSERT INTO users (username, password_hash, created_at) VALUES (?,?,?)",
+            (username, auth.hash_password(password), auth.datetime.now().isoformat())
+        )
+        conn.commit()
+    except auth.sqlite3.IntegrityError:
+        raise HTTPException(400, "用户名已存在")
+    finally:
+        conn.close()
+    token = auth.create_access_token({"sub": username})
+    return {"access_token": token, "token_type": "bearer"}
+
+@app.post("/auth/login")
+def login(username: str = Form(...), password: str = Form(...)):
+    conn = auth.sqlite3.connect(auth.DB_PATH)
+    row = conn.execute("SELECT password_hash FROM users WHERE username=?", (username,)).fetchone()
+    conn.close()
+    if not row or not auth.verify_password(password, row[0]):
+        raise HTTPException(401, "用户名或密码错误")
+    token = auth.create_access_token({"sub": username})
+    return {"access_token": token, "token_type": "bearer"}
+
+# ── 文件上传 ──
+@app.post("/files/upload")
+async def upload(file: UploadFile = File(...), user: str = Depends(auth.get_current_user)):
+    content = await file.read()
+    path = storage.upload_file(content, file.filename, user, file.content_type or "application/octet-stream")
+    return {"path": path, "filename": file.filename}
+
+@app.get("/files/list")
+def list_files(user: str = Depends(auth.get_current_user)):
+    return {"files": storage.list_user_files(user)}
+
+# ── AI 识图搜索 ──
+@app.post("/ai/search-by-image")
+async def search_by_image(file: UploadFile = File(...), user: str = Depends(auth.get_current_user)):
+    image_bytes = await file.read()
+    description = await ai_search.analyze_image(image_bytes)
+    results = await ai_search.search_in_library(description, user, storage)
+    return {"ai_description": description, "results": results}
+
+@app.get("/")
+def root():
+    return FileResponse("static/index.html")