add app

.gitignore

@@ -0,0 +1 @@
+venv/

app.py

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+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import torch
+
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-270m")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-3-270m")
+
+
+def predict(message, history):
+    chat_history = []
+    for user, bot in history:
+        chat_history.append(user)
+        chat_history.append(bot)
+    chat_history.append(message)
+
+    input_ids = tokenizer(chat_history, padding="longest", return_tensors="pt")['input_ids']
+    chat_history_ids = model.generate(
+        input_ids,
+        max_length=512,
+        num_return_sequences=1,  # Number of response sequences to generate
+        early_stopping=False,
+        do_sample=True,
+    )
+    response = tokenizer.decode(chat_history_ids[-1], skip_special_tokens=True)
+    yield response
+
+
+if __name__=="__main__":
+    gr.ChatInterface(predict).queue().launch()

requirements.txt

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+gradio
+transformers
+# -i https://download.pytorch.org/whl/cpu
+torch
+sentencepiece