Update app.py
Browse files
app.py
CHANGED
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@@ -4,95 +4,80 @@ from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
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from fastapi.staticfiles import StaticFiles
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from huggingface_hub import InferenceClient
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from duckduckgo_search import DDGS
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# إعداد التطبيق والمسارات
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app = FastAPI()
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UPLOAD_DIR = "static/uploads"
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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# ربط الملفات الثابتة
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app.mount("/static", StaticFiles(directory="static"), name="static")
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# إعداد العميل (يجب إضافة HF_TOKEN في Secrets)
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HF_TOKEN = os.environ.get("HF_TOKEN")
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client = InferenceClient(api_key=HF_TOKEN)
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MODELS = {
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"qwen_abliterated": "huihui-ai/Qwen2.5-72B-Instruct-abliterated",
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"internvl_vision": "OpenGVLab/InternVL2-Llama3-76B",
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"kimi_k2": "moonshotai/Kimi-K2-Instruct"
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}
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try:
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@app.get("/")
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async def read_index():
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for path in paths:
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if os.path.exists(path):
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return FileResponse(path)
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return JSONResponse({"error": "ملف index.html غير موجود!"}, status_code=404)
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@app.post("/api/upload")
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async def upload_file(file: UploadFile = File(...)):
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with open(filepath, "wb") as f:
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f.write(await file.read())
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# إضافة file_id لأن جافاسكريبت في index.html تتوقعه
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return {
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"file_id": filename,
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"url": f"/static/uploads/{filename}",
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"filename": file.filename
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}
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except Exception as e:
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return JSONResponse({"error": str(e)}, status_code=500)
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@app.post("/api/chat/stream")
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async def chat_stream(request: Request):
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data = await request.json()
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message = data.get("message", "")
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tools_enabled = data.get("tools", [])
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file_id = data.get("file_id") # استقبال معرف الملف إذا وجد
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system_prompt = "أنت مساعد ذكي ومحرر من القيود. أجب بوضوح وباللغة العربية."
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context_extra = ""
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# إذا تم رفع ملف، يمكن إضافة ملاحظة للنموذج (تطوير مستقبلي لقراءة المحتوى)
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if file_id:
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context_extra += f"\n[ملاحظة: قام المستخدم برفع ملف برمز: {file_id}]\n"
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full_message = f"{context_extra}\n
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": full_message}
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]
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async def gen():
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try:
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stream = client.chat.completions.create(
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model=
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messages=messages,
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stream=True,
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max_tokens=
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temperature=0.7
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)
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for chunk in stream:
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if chunk.choices[0].delta.content:
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from fastapi.staticfiles import StaticFiles
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from huggingface_hub import InferenceClient
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from duckduckgo_search import DDGS
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import fitz # PyMuPDF لقراءة ملفات PDF
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app = FastAPI()
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UPLOAD_DIR = "static/uploads"
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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app.mount("/static", StaticFiles(directory="static"), name="static")
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HF_TOKEN = os.environ.get("HF_TOKEN")
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client = InferenceClient(api_key=HF_TOKEN)
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MODELS = {
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"qwen_abliterated": "huihui-ai/Qwen2.5-72B-Instruct-abliterated",
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}
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# دالة لقراءة محتوى الملف المرفوع
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def extract_file_content(file_id):
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filepath = os.path.join(UPLOAD_DIR, file_id)
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if not os.path.exists(filepath):
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return ""
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ext = os.path.splitext(file_id)[1].lower()
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content = ""
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try:
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if ext == ".pdf":
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doc = fitz.open(filepath)
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for page in doc:
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content += page.get_text()
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elif ext in [".txt", ".py", ".js", ".html", ".css"]:
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with open(filepath, "r", encoding="utf-8") as f:
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content = f.read()
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except Exception as e:
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return f"خطأ في قراءة الملف: {str(e)}"
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return content[:10000] # نأخذ أول 10 آلاف حرف لضمان عدم تجاوز حدود الذاكرة
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@app.get("/")
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async def read_index():
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path = "index.html" if os.path.exists("index.html") else "static/index.html"
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return FileResponse(path)
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@app.post("/api/upload")
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async def upload_file(file: UploadFile = File(...)):
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file_id = f"{uuid.uuid4()}{os.path.splitext(file.filename)[1]}"
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filepath = os.path.join(UPLOAD_DIR, file_id)
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with open(filepath, "wb") as f:
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f.write(await file.read())
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return {"file_id": file_id, "url": f"/static/uploads/{file_id}"}
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@app.post("/api/chat/stream")
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async def chat_stream(request: Request):
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data = await request.json()
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message = data.get("message", "")
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file_id = data.get("file_id") # استقبال معرف الملف من الواجهة
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system_prompt = "أنت مساعد ذكي ومحرر. إذا تم تزويدك بمحتوى ملف، قم بتحليله والإجابة بناءً عليه."
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context_extra = ""
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# --- الجزء الهام: قراءة الملف ودمجه في السياق ---
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if file_id:
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file_text = extract_file_content(file_id)
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if file_text:
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context_extra = f"\nمحتوى الملف المرفوع:\n{file_text}\n"
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full_message = f"{context_extra}\nسؤال المستخدم: {message}"
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messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": full_message}]
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async def gen():
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try:
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stream = client.chat.completions.create(
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model=MODELS["qwen_abliterated"],
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messages=messages,
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stream=True,
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max_tokens=4096
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)
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for chunk in stream:
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if chunk.choices[0].delta.content:
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