Update evo_plugin_example.py

evo_plugin_example.py

@@ -1,15 +1,18 @@
-# evo_plugin_example.py — FLAN-T5 stand-in (better instruction following)
+# evo_plugin_example.py — small, instruction-following stand-in generator
+# The app will use YOUR evo_plugin.py if present; otherwise it falls back to this.
 import torch
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 class _HFSeq2SeqGenerator:
     def __init__(self, model_name: str = "google/flan-t5-small"):
+        # CPU is fine for demos; no GPU required on HF Spaces basic CPU.
         self.device = torch.device("cpu")
         self.tok = AutoTokenizer.from_pretrained(model_name)
         self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(self.device).eval()
 
     @torch.no_grad()
     def generate(self, prompt: str, max_new_tokens: int = 200, temperature: float = 0.4) -> str:
+        # Seq2Seq models generate responses that follow instructions better than GPT-2 here.
         inputs = self.tok(prompt, return_tensors="pt").to(self.device)
         out = self.model.generate(
             **inputs,
@@ -21,4 +24,5 @@ class _HFSeq2SeqGenerator:
         return self.tok.decode(out[0], skip_special_tokens=True).strip()
 
 def load_model():
+    # The app calls this to obtain a generator instance.
     return _HFSeq2SeqGenerator()