💡 Add DeepSeek model + code generation

app.py

@@ -1,7 +1,42 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
 
-def greet(name):
-    return f"Hello, {name}! 👋"
+# 🔹 Load tokenizer and base model
+base_model_id = "deepseek-ai/deepseek-coder-1.3b-base"
+lora_model_id = "brijmansuriya/deepseek-lora"  # ✅ Your LoRA fine-tuned model repo
+
+tokenizer = AutoTokenizer.from_pretrained(lora_model_id, trust_remote_code=True)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+)
+
+# 🔹 Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, lora_model_id)
+
+# 🔹 Define the function
+def generate_code(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        temperature=0.7,
+        do_sample=True,
+        top_p=0.95,
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# 🔹 Gradio UI
+demo = gr.Interface(
+    fn=generate_code,
+    inputs=gr.Textbox(label="Enter your coding prompt"),
+    outputs=gr.Textbox(label="Generated Code"),
+    title="🤖 DeepSeek Code Generator (LoRA)",
+    description="This app uses DeepSeek-Coder with Brijbhai's fine-tuned LoRA model to generate code from natural language prompts."
+)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()