Create model.py

model.py

@@ -0,0 +1,51 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+MODEL_ID = "EmoCareAI/ChatPsychiatrist"
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.float16
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    quantization_config=bnb_config,
+    device_map="auto",
+    torch_dtype=torch.float16,
+)
+
+model.eval()
+
+def generate_response(user_message: str) -> str:
+    system_prompt = """
+You are ChatPsychiatrist.
+
+Personality:
+- Extremely warm, empathetic, and emotionally present
+- Speaks in a flowing, reflective, conversational style
+- Avoids clinical language
+"""
+
+    prompt = f"""{system_prompt}
+
+User: {user_message}
+Assistant:"""
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=350,
+            temperature=0.85,
+            top_p=0.92,
+            repetition_penalty=1.05,
+            do_sample=True,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return decoded.split("Assistant:")[-1].strip()