Create app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from huggingface_hub import login
+import re
+
+login(token=os.getenv("HF_TOKEN"))
+
+
+# Load the model and tokenizer
+model_name = "ranggafermata/Fermata-v1.2-lightcoder"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
+model.eval()
+
+def generate_code(prompt, max_tokens, temperature, top_p):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=generate_code,
+    inputs=[
+        gr.Textbox(lines=5, label="Prompt", placeholder="Write your prompt here..."),
+        gr.Slider(minimum=10, maximum=512, value=128, step=8, label="Max Tokens"),
+        gr.Slider(minimum=0.1, maximum=1.5, value=0.8, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+    ],
+    outputs=gr.Textbox(lines=20, label="Generated Code"),
+    title="Fermata v1.2 LightCoder",
+    description="Code generator powered by TinyLlama fine-tuned on math/code tasks."
+)
+
+iface.launch(mcp_server=True)