Update app.py

app.py

@@ -1,69 +1,203 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+
+MODEL_REPO = "Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF"
+
+# Önce daha pratik quant dosyalarını dene
+MODEL_CANDIDATES = [
+    "Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-Q4_K_M.gguf",
+    "Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-Q4_K_S.gguf",
+    "Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-Q3_K_M.gguf",
+    "Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-Q2_K.gguf",
+]
+
+llm = None
+loaded_model_file = None
+
+
+def download_first_available_model(token: str | None):
+    last_error = None
+
+    for filename in MODEL_CANDIDATES:
+        try:
+            model_path = hf_hub_download(
+                repo_id=MODEL_REPO,
+                filename=filename,
+                token=token,
+            )
+            return model_path, filename
+        except Exception as e:
+            last_error = e
+
+    raise RuntimeError(
+        "Uygun GGUF dosyası indirilemedi. "
+        f"Denenen dosyalar: {', '.join(MODEL_CANDIDATES)}. "
+        f"Son hata: {last_error}"
+    )
+
+
+def build_model(model_path: str):
+    cpu_count = os.cpu_count() or 2
+
+    # CPU Space için daha temkinli ayarlar
+    n_threads = max(1, min(8, cpu_count))
+
+    return Llama(
+        model_path=model_path,
+        n_ctx=4096,
+        n_threads=n_threads,
+        n_batch=128,
+        n_gpu_layers=0,
+        verbose=False,
+    )
+
+
+def get_model(hf_token: gr.OAuthToken | None):
+    global llm, loaded_model_file
+
+    if llm is not None:
+        return llm
+
+    token = hf_token.token if hf_token is not None else None
+
+    model_path, filename = download_first_available_model(token)
+    llm = build_model(model_path)
+    loaded_model_file = filename
+    return llm
+
+
+def normalize_history(history):
+    messages = []
+
+    for item in history or []:
+        if isinstance(item, dict):
+            role = item.get("role")
+            content = item.get("content", "")
+            if role in ("user", "assistant", "system"):
+                messages.append({"role": role, "content": str(content)})
+        elif isinstance(item, (list, tuple)) and len(item) == 2:
+            user_msg, assistant_msg = item
+            if user_msg:
+                messages.append({"role": "user", "content": str(user_msg)})
+            if assistant_msg:
+                messages.append({"role": "assistant", "content": str(assistant_msg)})
+
+    return messages
 
 
 def respond(
     message,
-    history: list[dict[str, str]],
+    history,
     system_message,
     max_tokens,
     temperature,
     top_p,
-    hf_token: gr.OAuthToken,
+    hf_token: gr.OAuthToken | None,
 ):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
+    global loaded_model_file
 
-    messages.extend(history)
+    try:
+        model = get_model(hf_token)
+    except Exception as e:
+        yield (
+            "Model yüklenemedi.\n\n"
+            f"Hata: {e}\n\n"
+            "Olası nedenler:\n"
+            "- Space RAM kapasitesi yetersiz\n"
+            "- GGUF dosya adı değişmiş\n"
+            "- Model erişimi için yetkili Hugging Face hesabı gerekiyor\n"
+            "- llama-cpp-python bu ortamda düzgün kurulmadı"
+        )
+        return
 
-    messages.append({"role": "user", "content": message})
+    messages = [{"role": "system", "content": str(system_message)}]
+    messages.extend(normalize_history(history))
+    messages.append({"role": "user", "content": str(message)})
 
     response = ""
+    header = f"[Model: {loaded_model_file}]\n\n"
+
+    try:
+        stream = model.create_chat_completion(
+            messages=messages,
+            max_tokens=int(max_tokens),
+            temperature=float(temperature),
+            top_p=float(top_p),
+            stream=True,
+        )
+
+        first_token = True
+        for chunk in stream:
+            token = ""
+            choices = chunk.get("choices", [])
+            if choices:
+                delta = choices[0].get("delta", {})
+                token = delta.get("content", "") or ""
+
+            if token:
+                response += token
+                if first_token:
+                    yield header + response
+                    first_token = False
+                else:
+                    yield header + response
+
+        if not response:
+            yield header + "(Model yanıt üretmedi.)"
+
+    except Exception as e:
+        partial = header + response if response else header
+        yield (
+            partial
+            + "\n\nÜretim sırasında hata oluştu.\n"
+            f"Hata: {e}\n\n"
+            "Daha düşük max_tokens veya daha küçük quant dosyası deneyebilirsin."
+        )
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
 with gr.Blocks() as demo:
+    gr.Markdown("# GGUF Chat Demo (Fallback)")
+
     with gr.Sidebar():
         gr.LoginButton()
-    chatbot.render()
+        gr.Markdown(
+            "Model private veya gated ise giriş yapman gerekebilir. "
+            "Uygun GGUF dosyası otomatik seçilmeye çalışılır."
+        )
 
+    chatbot = gr.ChatInterface(
+        fn=respond,
+        additional_inputs=[
+            gr.Textbox(
+                value="You are a friendly Chatbot.",
+                label="System message",
+            ),
+            gr.Slider(
+                minimum=1,
+                maximum=1024,
+                value=256,
+                step=1,
+                label="Max new tokens",
+            ),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.5,
+                value=0.7,
+                step=0.1,
+                label="Temperature",
+            ),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.9,
+                step=0.05,
+                label="Top-p",
+            ),
+        ],
+    )
+    chatbot.render()
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()