Duplicate from EXFINITE/BlenderBot-UI

Co-authored-by: Abid Ali Awan <kingabzpro@users.noreply.huggingface.co>

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README.md

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+---
+title: BlenderBot New UI
+emoji: 🐱‍👓
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: EXFINITE/BlenderBot-UI
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+
+<a href='https://www.freepik.com/vectors/chat-bot'>Chat bot vector used in app is created by roserodionova - www.freepik.com</a>

app.py

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+import os
+import gradio as gr
+
+title = "Have Fun With ChubbyBot"
+description = """
+<p>
+<center>
+The bot is trained on blended_skill_talk dataset using facebook/blenderbot-400M-distill.
+<img src="https://huggingface.co/spaces/EXFINITE/BlenderBot-UI/resolve/main/img/cover.png" alt="rick" width="250"/>
+</center>
+</p>
+"""
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.06616' target='_blank'>Recipes for building an open-domain chatbot</a></p><p style='text-align: center'><a href='https://parl.ai/projects/recipes/' target='_blank'>Original PARLAI Code</a></p></center></p>"
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BlenderbotForConditionalGeneration, BlenderbotForCausalLM, BlenderbotTokenizer
+
+tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+model = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill",add_cross_attention=False)
+
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+
+    # generate a response 
+    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+
+    # convert the tokens to text, and then split the responses into the right format
+    response = tokenizer.decode(history[0]).replace("<s>","").split("</s>")
+    response = [(response[i], response[i+1]) for i in range(0, len(response), 2)]  # convert to tuples of list
+    return response, history
+
+gr.Interface(
+		fn = predict, 
+		inputs = ["textbox","state"], 
+		outputs = ["chatbot","state"],
+		theme ="seafoam",
+		title = title, 
+		description = description, 
+		article = article 
+		).launch(enable_queue=True)

requirements.txt

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+transformers
+torch