Create app.py

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Nama model dasar dan lokasi adapter kamu di Hugging Face
+base_model_id = "microsoft/phi-2"
+adapter_model_id = "username_kamu/Deeper-Logic-Phi2" # Ganti dengan repo kamu
+
+# Load Tokenizer dan Model
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id, 
+    torch_dtype=torch.float16, 
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Gabungkan dengan hasil fine-tuning kamu
+model = PeftModel.from_pretrained(model, adapter_model_id)
+
+def predict(message, history):
+    prompt = f"Instruct: {message}\nOutput:"
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs, 
+            max_new_tokens=200, 
+            temperature=0.7, 
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response.split("Output:")[-1].strip()
+
+# Membuat Interface Chat dengan Gradio
+demo = gr.ChatInterface(
+    fn=predict, 
+    title="Deeper-Logic AI", 
+    description="Asisten Riset & Produktivitas Berbasis Phi-2 (Fine-tuned)",
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    demo.launch()