Create app.py

app.py

@@ -0,0 +1,64 @@
+# Gerado com IA
+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# =========================
+# CONFIG
+# =========================
+MODEL_ID = "CromIA/MicroLM-1M"
+
+# =========================
+# LOAD MODEL
+# =========================
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
+model.eval()
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# =========================
+# GENERATE FUNCTION
+# =========================
+def generate_text(prompt, max_new_tokens, temperature, top_p):
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=int(max_new_tokens),
+            do_sample=True,
+            temperature=float(temperature),
+            top_p=float(top_p),
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# =========================
+# UI
+# =========================
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(lines=3, placeholder="Digite um prompt..."),
+        gr.Slider(10, 200, value=80, label="Max new tokens"),
+        gr.Slider(0.1, 1.5, value=0.8, label="Temperature"),
+        gr.Slider(0.5, 1.0, value=0.95, label="Top-p"),
+    ],
+    outputs=gr.Textbox(label="Output"),
+    title="MicroLM-1M",
+    description="Modelo de linguagem leve (~1M parâmetros) treinado em 500M tokens."
+)
+
+# =========================
+# RUN
+# =========================
+if __name__ == "__main__":
+    demo.launch()