add initial implementation of T5 Mini Reply model with Gradio interface and requirements

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# Modelo leve que roda em CPU (bom p/ demo). Se preferir PT “de verdade”, troque por:
+# MODEL_ID = "unicamp-dl/ptt5-small-portuguese-vocab"
+MODEL_ID = "google/flan-t5-small"
+
+tok = AutoTokenizer.from_pretrained(MODEL_ID)
+mdl = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
+
+pipe = pipeline(
+    "text2text-generation",
+    model=mdl,
+    tokenizer=tok
+)
+
+def gen(prompt: str):
+    if not prompt or not prompt.strip():
+        return ""
+    out = pipe(
+        prompt,
+        max_new_tokens=120,
+        do_sample=True,
+        top_p=0.9,
+        temperature=0.7,
+        repetition_penalty=1.15,
+        num_return_sequences=1,
+    )
+    return out[0]["generated_text"]
+
+# Gradio já expõe /api/predict automaticamente
+demo = gr.Interface(
+    fn=gen,
+    inputs=gr.Textbox(label="Prompt"),
+    outputs=gr.Textbox(label="Saída"),
+    title="T5 Mini Reply",
+    description="Geração de respostas curtas (CPU)."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers==4.43.3
+sentencepiece==0.2.0
+accelerate==0.33.0