Create app.py

app.py

@@ -0,0 +1,67 @@
+import gradio as gr
+import ctranslate2
+from transformers import AutoModel
+
+# xl size run out of memory on 16GB VM
+model_name =  'google/flan-t5-large'
+#model_name = 'jncraton/fastchat-t5-3b-v1.0-ct2-int8'
+
+# Load model directly
+#from transformers import AutoModel
+#model = AutoModel.from_pretrained(model_name)
+
+translator = ctranslate2.Translator("t5-small-ct2")
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+#model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+title = ""
+
+def get_examples ():
+    return [
+    ["Peter goes to the store to buy a soda. The soda costs $.25 an ounce. \
+He brought $2 with him and leaves with $.50. How many ounces of soda did he buy?",
+    "How much did Peter spend on soda? ** He spend $1.5 on soda because 2 - .5 = <<2-.5=1.5>>1.5 \
+How many ounces of soda did Peter buy? ** He bought 6 ounces of soda because 1.5 / .25 = <<6=6>>6 #### 6"
+    ],
+    ["Krystian works in the library. He borrows an average of 40 books every day. \
+Every Friday, his number of borrowed books is about 40% higher than the daily average. How many books does he borrow in a week if the library is open from Monday to Friday?"
+    ,"How many books does Krystian borrow on Friday? ** The number of books borrowed \
+on Friday is higher by 40 * 40/100 = <<40*40/100=16>>16 books. How many books does Krystian borrow in a week? ** There are 5 days from Monday to Friday inclusive, so Krystian borrows an average of 5 * 40 = <<5*40=200>>200 books during that time. How many books does Krystian borrow in a week? ** With Friday's increase in borrowings, during one week Krystian borrows 200 + 16 = <<200+16=216>>216 books."]
+    , ["Jane had $60 but gave $30 to dave and went to movies and spend $2. How much money does Jane has left? Answer by reasoning step by step:", "$28"]
+    ]
+    
+    
+def text2text(input_text):
+    input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
+    results = translator.translate_batch([input_tokens])
+
+    output_tokens = results[0].hypotheses[0]
+    output_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens))
+
+    return output_text
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """
+    # Fast Chat T5 Demo
+    Fast inference with quantized LLM
+    Prompt the model in the Input box.
+    """)
+    txt_in = gr.Textbox(label="Input", lines=3)
+    correct_label = gr.Label(label="Correct")
+    txt_out = gr.Textbox(value="", label="Output", lines=4)
+    
+
+    btn = gr.Button(value="Submit")
+    btn.click(text2text, inputs=[txt_in], outputs=[txt_out])
+
+
+    gr.Examples(
+        examples=get_examples(),
+        inputs=[txt_in,correct_label]
+    )
+    
+
+if __name__ == "__main__":
+    demo.launch()