Create app.py

app.py

@@ -0,0 +1,46 @@
+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+app = FastAPI()
+
+# Laad jouw eigen getrainde model in
+model_path = "./SpaceStar-0.01-Final"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path).to("cpu")
+
+class ChatRequest(BaseModel):
+    message: str
+    system_prompt: str = "Je bent SpaceStar 0.01, een geavanceerde AI-assistent die gespecialiseerd is in programmeren, wiskunde en communicatie in het Nederlands en Engels."
+
+@app.post("/chat")
+async def chat(request: ChatRequest):
+    # Formatteer het gesprek in de Qwen-stijl
+    messages = [
+        {"role": "system", "content": request.system_prompt},
+        {"role": "user", "content": request.message}
+    ]
+    
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    model_inputs = tokenizer([text], return_tensors="pt").to("cpu")
+    
+    # Genereer het antwoord
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=256,
+        temperature=0.7,
+        top_p=0.9
+    )
+    
+    # Filter de input-tokens weg zodat we alleen het antwoord overhouden
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return {"response": response}
+
+@app.get("/")
+def home():
+    return {"status": "SpaceStar 0.01 is online and ready!"}