Upload yehia_interface.py

model/yehia_interface.py

@@ -0,0 +1,52 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import os
+
+# === Model setup ===
+model_name = "Navid-AI/Yehia-7B-preview"
+
+# Use token if private model
+hf_token = os.getenv("HUGGINGFACE_TOKEN", None)
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=hf_token)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    use_auth_token=hf_token
+)
+
+# === Inference function ===
+def query_yehia(user_message: str, history=None):
+    if history is None:
+        history = []
+
+    messages = [
+        {"role": "system", "content": "أنت يحيى، وكيل مبيعات عقاري ذكي متخصص في تقديم المشورة للعملاء باللغة العربية."},
+        *history,
+        {"role": "user", "content": user_message}
+    ]
+
+    # Apply chat template and move to model device
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    ).to(model.device)
+
+    # Generate output
+    outputs = model.generate(
+        inputs,
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.95,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    # Decode only the new tokens (skip the prompt)
+    response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
+
+    return response.strip()