adding html code gen

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load model
+base_model = "unsloth/Llama-3.2-1B-Instruct"
+adapter = "jinesh90/llama-3.2-ft-html-generator"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model, dtype=torch.float32, device_map="cpu"
+)
+model = PeftModel.from_pretrained(model, adapter)
+model.eval()
+
+def generate(instruction, input_text=""):
+    prompt = f"""Generate valid HTML code based on the following instructions. Return only the HTML code.
+
+### Instruction
+{instruction}
+
+### Input
+{input_text if input_text else "(none)"}"""
+
+    messages = [{"role": "user", "content": prompt}]
+    text = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs, max_new_tokens=512,
+            do_sample=False, temperature=0
+        )
+    input_len = inputs["input_ids"].shape[1]
+    return tokenizer.decode(outputs[0, input_len:], skip_special_tokens=True)
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(label="Instruction", placeholder="Create an HTML login form..."),
+        gr.Textbox(label="Input code (optional)", placeholder="Paste existing HTML..."),
+    ],
+    outputs=gr.Code(label="Generated HTML", language="html"),
+    title="💻 HTML Code Generator",
+    description="Fine-tuned Llama 3.2 for HTML generation",
+)
+
+demo.launch()

requirements.txt

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+torch
+transformers
+peft
+accelerate
+gradio