Create app.py

app.py

@@ -0,0 +1,223 @@
+
+import os
+import torch
+import gradio as gr
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    AutoModelForCausalLM
+)
+
+# ==================================================
+# GOSHAWK AI — Hugging Face Space
+# ==================================================
+
+APP_NAME = "Goshawk AI"
+MODEL_PATH = os.getenv("MODEL_PATH", "./")
+
+MAX_NEW_TOKENS = 256
+MAX_CONTEXT = 2048
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if device == "cuda" else torch.float32
+
+print(f"[{APP_NAME}] Device: {device}")
+print(f"[{APP_NAME}] Model path: {MODEL_PATH}")
+
+tokenizer = None
+model = None
+load_error = None
+
+try:
+    config = AutoConfig.from_pretrained(
+        MODEL_PATH,
+        local_files_only=True,
+        trust_remote_code=False
+    )
+
+    print(f"Model architecture: {config.model_type}")
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        MODEL_PATH,
+        local_files_only=True,
+        trust_remote_code=False
+    )
+
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_PATH,
+        config=config,
+        torch_dtype=dtype,
+        low_cpu_mem_usage=True,
+        local_files_only=True,
+        trust_remote_code=False
+    )
+
+    model.to(device)
+    model.eval()
+
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    print(f"[{APP_NAME}] Model loaded successfully.")
+
+except Exception as exc:
+    load_error = f"{type(exc).__name__}: {exc}"
+    print(f"[{APP_NAME}] Loading failed: {load_error}")
+
+
+SYSTEM_PROMPT = (
+    "You are Goshawk AI, a helpful and precise AI assistant. "
+    "Answer in the user's language. Be transparent about uncertainty. "
+    "Never invent facts, live market data, or sources."
+)
+
+
+def build_prompt(message, history):
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT}
+    ]
+
+    for item in (history or [])[-8:]:
+        if isinstance(item, dict):
+            role = item.get("role")
+            content = item.get("content", "")
+
+            if role in ("user", "assistant") and isinstance(content, str):
+                messages.append({
+                    "role": role,
+                    "content": content
+                })
+
+        elif isinstance(item, (list, tuple)) and len(item) == 2:
+            if item[0]:
+                messages.append({
+                    "role": "user",
+                    "content": str(item[0])
+                })
+            if item[1]:
+                messages.append({
+                    "role": "assistant",
+                    "content": str(item[1])
+                })
+
+    messages.append({"role": "user", "content": message})
+
+    if hasattr(tokenizer, "apply_chat_template"):
+        try:
+            return tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True
+            )
+        except Exception:
+            pass
+
+    # Fallback for models without a chat template.
+    prompt = f"System: {SYSTEM_PROMPT}\n"
+    for msg in messages[1:]:
+        label = "User" if msg["role"] == "user" else "Assistant"
+        prompt += f"{label}: {msg['content']}\n"
+
+    return prompt + "Assistant:"
+
+
+def respond(message, history, temperature, max_tokens):
+    if not message or not message.strip():
+        yield "Lütfen bir mesaj yaz."
+        return
+
+    if model is None or tokenizer is None:
+        yield (
+            "Model yüklenemedi.\n\n"
+            f"Hata: {load_error}\n\n"
+            "config.json, model.safetensors ve tokenizer "
+            "dosyalarını kontrol et. Model mimarisi metin "
+            "üretimini desteklemiyor olabilir."
+        )
+        return
+
+    try:
+        prompt = build_prompt(message.strip(), history)
+
+        inputs = tokenizer(
+            prompt,
+            return_tensors="pt",
+            truncation=True,
+            max_length=MAX_CONTEXT
+        )
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+
+        input_length = inputs["input_ids"].shape[1]
+
+        if input_length >= MAX_CONTEXT:
+            yield "Girdi bağlam sınırına ulaştı. Daha kısa bir mesaj dene."
+            return
+
+        with torch.inference_mode():
+            output = model.generate(
+                **inputs,
+                max_new_tokens=int(max_tokens),
+                do_sample=float(temperature) > 0,
+                temperature=max(float(temperature), 0.01),
+                top_p=0.9,
+                repetition_penalty=1.08,
+                pad_token_id=tokenizer.pad_token_id,
+                eos_token_id=tokenizer.eos_token_id
+            )
+
+        new_tokens = output[0][input_length:]
+        answer = tokenizer.decode(
+            new_tokens,
+            skip_special_tokens=True
+        ).strip()
+
+        yield answer or "Model boş yanıt üretti."
+
+    except Exception as exc:
+        yield f"Üretim hatası: {type(exc).__name__}: {exc}"
+
+
+with gr.Blocks(title=APP_NAME) as demo:
+    gr.Markdown(
+        "# 🦅 Goshawk AI\n"
+        "### Yerel model tabanlı yapay zekâ asistanı\n"
+        f"**Cihaz:** `{device}`"
+    )
+
+    if load_error:
+        gr.Markdown(
+            "⚠️ Model yüklenemedi. Ayrıntılar sohbet alanında görünür."
+        )
+
+    chatbot = gr.ChatInterface(
+        fn=respond,
+        chatbot=gr.Chatbot(height=480),
+        textbox=gr.Textbox(
+            placeholder="Goshawk AI'ye bir soru sor...",
+            lines=2
+        ),
+        additional_inputs=[
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.2,
+                value=0.7,
+                step=0.1,
+                label="Yaratıcılık"
+            ),
+            gr.Slider(
+                minimum=32,
+                maximum=512,
+                value=MAX_NEW_TOKENS,
+                step=32,
+                label="Maksimum yeni token"
+            )
+        ]
+    )
+
+    gr.Markdown(
+        "Not: Yanıt kalitesi ve hızı kullanılan modelin "
+        "mimarisine ve donanıma bağlıdır."
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch()