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Update app.py
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app.py
CHANGED
@@ -2,48 +2,46 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
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import gradio as gr
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import torch
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title = "
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description = "
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examples = [["How are you?"]]
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# Set the padding token to be used and initialize the model
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
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tokenizer.padding_side = 'left'
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tokenizer.add_special_tokens({'pad_token': '[EOS]'})
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tokenizer.pad_token = tokenizer.eos_token
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)
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iface = gr.Interface(
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fn=predict,
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title=title,
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description=description,
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examples=examples,
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inputs=
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outputs=
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theme="ParityError/Anime",
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)
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iface.launch()
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import gradio as gr
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import torch
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title = "👋🏻Welcome to Tonic's EZ Chat🚀"
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description = "You can use this Space to test out the current model (DialoGPT-medium) or duplicate this Space and use it for anyother model on 🤗HuggingFace."
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examples = [["How are you?"]]
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# Set the padding token to be used and initialize the model
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
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tokenizer.padding_side = 'left'
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import gradio as gr
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import torch
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title = "👋🏻Welcome to Tonic's EZ Chat🚀"
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description = "You can use this Space to test out the current model (DialoGPT-medium) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on [Discord](https://discord.gg/fpEPNZGsbt) to build together."
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examples = [["How are you?"]]
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
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tokenizer.padding_side = 'left'
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
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def predict(input, history=[]):
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new_user_input_ids = tokenizer.encode(input, return_tensors="pt")
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bot_input_ids = torch.cat([torch.tensor(history), new_user_input_ids], dim=-1) if history else new_user_input_ids
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chat_history_ids = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id)
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response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
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return response
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iface = gr.Interface(
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fn=predict,
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title=title,
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description=description,
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examples=examples,
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inputs="text",
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outputs="text",
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theme="ParityError/Anime",
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)
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iface.launch()
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