Update app.py

app.py

@@ -0,0 +1,85 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+def load_model_with_lora(base_model_name, lora_path):
+    """
+    Load base model and merge it with LoRA adapter
+    """
+    # Load base model
+    base_model = AutoModelForCausalLM.from_pretrained(
+        base_model_name,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    
+    # Load and merge LoRA adapter
+    model = PeftModel.from_pretrained(base_model, lora_path)
+    model = model.merge_and_unload() # Merge adapter weights with base model
+    
+    return model
+
+def load_tokenizer(base_model_name):
+    """
+    Load tokenizer for the base model
+    """
+    return AutoTokenizer.from_pretrained(base_model_name)
+
+def generate_code(prompt, model, tokenizer, max_length=512, temperature=0.7):
+    """
+    Generate code based on the prompt
+    """
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        temperature=temperature,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Initialize model and tokenizer
+BASE_MODEL_NAME = "unsloth/Llama-3.2-3B-bnb-4bit"  # Replace with your base model name
+LORA_PATH = "EmTpro01/Llama-3.2-3B-peft"  # Replace with your LoRA adapter path
+
+model = load_model_with_lora(BASE_MODEL_NAME, LORA_PATH)
+tokenizer = load_tokenizer(BASE_MODEL_NAME)
+
+# Create Gradio interface
+def gradio_generate(prompt, temperature, max_length):
+    return generate_code(prompt, model, tokenizer, max_length, temperature)
+
+demo = gr.Interface(
+    fn=gradio_generate,
+    inputs=[
+        gr.Textbox(
+            lines=5,
+            placeholder="Enter your code generation prompt here...",
+            label="Prompt"
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.7,
+            step=0.1,
+            label="Temperature"
+        ),
+        gr.Slider(
+            minimum=64,
+            maximum=2048,
+            value=512,
+            step=64,
+            label="Max Length"
+        )
+    ],
+    outputs=gr.Code(language="python", label="Generated Code"),
+    title="Code Generation with LoRA",
+    description="Enter a prompt to generate code using a fine-tuned model with LoRA adapters",
+)
+
+if __name__ == "__main__":
+    demo.launch()