Create app.py

app.py

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+import gradio as gr 
+
+import pandas as pd
+from datasets import load_dataset
+
+
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+device = 'cpu' # if you have a GPU
+
+tokenizer = T5Tokenizer.from_pretrained('stanfordnlp/SteamSHP-flan-t5-large')
+model = T5ForConditionalGeneration.from_pretrained('stanfordnlp/SteamSHP-flan-t5-large').to(device)
+
+def process():
+    input_text = "POST: Instacart gave me 50 pounds of limes instead of 5 pounds... what the hell do I do with 50 pounds of limes? I've already donated a bunch and gave a bunch away. I'm planning on making a bunch of lime-themed cocktails, but... jeez. Ceviche? \n\n RESPONSE A: Lime juice, and zest, then freeze in small quantities.\n\n RESPONSE B: Lime marmalade lol\n\n Which response is better? RESPONSE"
+    x = tokenizer([input_text], return_tensors='pt').input_ids.to(device)
+    y = model.generate(x, max_new_tokens=1)
+    return tokenizer.batch_decode(y, skip_special_tokens=True)[0]
+
+title = "Compare Instruction Models to see which one is more helpful"
+interface = gr.Interface(fn=process, 
+                     #inputs=[gr.Image(type="pil"), gr.Textbox(label="Question")],
+                     outputs=[, 
+                              gr.Textbox(label = "Responses")
+                              ],
+                     title=title,
+                     )
+                     
+interface.launch(debug=True)