Create app.py

app.py

@@ -0,0 +1,45 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+app = FastAPI(title="Assistant IA Education Marocaine")
+
+# Chargement du modèle
+MODEL_NAME = "unsloth/mistral-7b-v0.3"
+LORA_NAME = "dohael/mistral-7b-education-maroc"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(model, LORA_NAME)
+
+class Question(BaseModel):
+    question: str
+
+@app.get("/")
+def root():
+    return {"message": "Assistant IA Education Marocaine 🇲🇦", "status": "running"}
+
+@app.post("/ask")
+def ask(q: Question):
+    prompt = f"<s>[INST] {q.question} [/INST]"
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.7,
+        do_sample=True,
+        repetition_penalty=1.1
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = response.split("[/INST]")[-1].strip()
+    return {"question": q.question, "reponse": response}
+
+@app.get("/health")
+def health():
+    return {"status": "healthy"}