Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+model_id = "Lunar2441/deepseek-coder-mine"
+
+print("Loading model...")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32,
+    device_map="cpu",
+    low_cpu_mem_usage=True,
+)
+print("Model ready!")
+
+SYS = "You are an expert Python developer. You write clean, efficient, well-commented Python code. You follow PEP8, use Pythonic patterns, and always explain your reasoning."
+
+def chat(message, history):
+    prompt = "<|im_start|>system\n" + SYS + "\n<|im_end|>\n"
+    for user, assistant in history:
+        prompt += "<|im_start|>user\n" + user + "\n<|im_end|>\n"
+        prompt += "<|im_start|>assistant\n" + assistant + "\n<|im_end|>\n"
+    prompt += "<|im_start|>user\n" + message + "\n<|im_end|>\n<|im_start|>assistant\n"
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+    return response
+
+gr.ChatInterface(
+    fn=chat,
+    title="DeepSeek Python Pro",
+    description="Fine-tuned Python coding assistant by Lunar2441",
+    examples=[
+        "Write a Python function to reverse a linked list",
+        "Explain decorators with an example",
+        "Write a binary search in Python",
+    ],
+).launch()