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Build error
Build error
try wiring up feedback element
Browse files
app.py
CHANGED
@@ -1,10 +1,14 @@
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import gradio as gr
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def chat(message, history):
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history = history or [
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if message.startswith("How many"):
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response = random.randint(1, 10)
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elif message.startswith("How"):
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@@ -14,13 +18,40 @@ def chat(message, history):
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else:
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response = "I don't know"
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history.append((message, response))
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return history,
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iface = gr.Interface(
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chat,
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["text", "state"],
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["chatbot", "
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allow_screenshot=False,
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allow_flagging="never",
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)
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iface.launch()
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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
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grammar_tokenizer = AutoTokenizer.from_pretrained("prithivida/grammar_error_correcter_v1")
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grammar_model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/grammar_error_correcter_v1")
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import torch
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import gradio as gr
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def chat(message, history):
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history = history or []
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if message.startswith("How many"):
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response = random.randint(1, 10)
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elif message.startswith("How"):
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else:
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response = "I don't know"
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history.append((message, response))
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return history, feedback(message)
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def feedback(text):
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tokenized_phrases = grammar_tokenizer([text], return_tensors='pt', padding=True)
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corrections = grammar_model.generate(**tokenized_phrases)
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corrections = grammar_tokenizer.batch_decode(corrections, skip_special_tokens=True)
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print("The corrections are: ", corrections)
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if corrections[0] == text:
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feedback = f'Looks good! Keep up the good work'
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else:
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feedback = f'\'{corrections[0]}\' might be a little better'
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return f'FEEDBACK: {feedback}'
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iface = gr.Interface(
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chat,
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["text", "state"],
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["chatbot", "text"],
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allow_screenshot=False,
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allow_flagging="never",
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)
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iface.launch()
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new_user_input_ids = tokenizer.encode(text+tokenizer.eos_token, return_tensors='pt')
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# append the new user input tokens to the chat history
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bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if chat_history_ids is not None else new_user_input_ids
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# generated a response while limiting the total chat history to 1000 tokens,
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chat_history_ids = model.generate(bot_input_ids, max_length=5000, pad_token_id=tokenizer.eos_token_id)
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print("The text is ", [text])
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# pretty print last ouput tokens from bot
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output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
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print("The outout is :", output)
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text_session.append(output)
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