Create app.py

app.py

@@ -0,0 +1,116 @@
+
+import os
+import gradio as gr
+from llama_cpp import Llama
+
+# -----------------------------
+# FinGPT Crypto - Local GGUF
+# -----------------------------
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODEL_PATH = os.path.join(
+    BASE_DIR,
+    "models",
+    "fingpt-crypto-v5-full-test.q8_0.gguf"
+)
+
+if not os.path.isfile(MODEL_PATH):
+    raise FileNotFoundError(
+        f"Model bulunamadı: {MODEL_PATH}\n"
+        "GGUF dosyasını models klasörüne koy."
+    )
+
+print("FinGPT Crypto modeli yükleniyor...")
+
+llm = Llama(
+    model_path=MODEL_PATH,
+    n_ctx=4096,
+    n_threads=max(1, (os.cpu_count() or 4) - 1),
+    n_batch=256,
+    n_gpu_layers=0,  # CPU modu
+    verbose=False
+)
+
+SYSTEM_PROMPT = """
+You are FinGPT Crypto, a cryptocurrency market analysis assistant.
+
+Analyze crypto markets using only information provided by the user
+or available in the conversation.
+
+When appropriate, structure your analysis as:
+1. Market regime
+2. Bullish and bearish factors
+3. Risk analysis
+4. Alternative scenarios
+5. What evidence would invalidate the thesis
+
+Never invent live prices, news, whale transactions or volume data.
+Clearly distinguish facts, assumptions and speculation.
+Do not claim guaranteed profits or fabricate prediction accuracy.
+Explain that sentiment alone is not a reliable trading signal.
+Answer in the user's language.
+"""
+
+def respond(message, history):
+    if not message or not message.strip():
+        return "Lütfen analiz etmek istediğin soruyu yaz."
+
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+
+    # Son mesajları bağlam olarak kullan
+    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, (tuple, list)) 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)
+                })
+
+    messages.append({"role": "user", "content": message})
+
+    try:
+        result = llm.create_chat_completion(
+            messages=messages,
+            max_tokens=700,
+            temperature=0.2,
+            top_p=0.9,
+            repeat_penalty=1.1
+        )
+
+        answer = result["choices"][0]["message"]["content"]
+        return answer.strip() if answer else "Model boş yanıt üretti."
+
+    except Exception as exc:
+        return f"Model hatası: {type(exc).__name__}: {exc}"
+
+
+demo = gr.ChatInterface(
+    fn=respond,
+    title="FinGPT Crypto v5",
+    description=(
+        "Yerel GGUF modeliyle kripto para analizi. "
+        "Canlı piyasa verisi otomatik olarak alınmaz."
+    ),
+    examples=[
+        "BTC için yükseliş ve düşüş senaryolarını analiz et.",
+        "Whale sentiment score nasıl hesaplanır?",
+        "Finansal haberlerde fiyatlanmamış bilgi nasıl test edilir?",
+        "Bir trading stratejisinde overfitting nasıl tespit edilir?"
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()