Duplicate from hunkim/DialoGPT

Co-authored-by: Sung Kim <[email protected]>

.gitattributes

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README.md

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+---
+title: DialoGPT
+emoji: 🐢
+colorFrom: indigo
+colorTo: yellow
+sdk: streamlit
+app_file: app.py
+pinned: false
+duplicated_from: hunkim/DialoGPT
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+# https://ai-brewery.medium.com/conversational-chatbot-using-transformers-and-streamlit-73d621afde9
+import streamlit as st
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+@st.cache(hash_funcs=
+    {transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast: hash}, 
+    suppress_st_warning=True, allow_output_mutation=True)
+
+def load_data():    
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+    model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+    return tokenizer, model
+
+tokenizer, model = load_data()
+
+st.write("Welcome to the Chatbot. I am still learning, please be patient")
+input = st.text_input('User:')
+if 'count' not in st.session_state or st.session_state.count == 6:
+    st.session_state.count = 0 
+    st.session_state.chat_history_ids = None
+    st.session_state.old_response = ''
+else:
+    st.session_state.count += 1
+
+
+new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+
+bot_input_ids = torch.cat([st.session_state.chat_history_ids, new_user_input_ids], dim=-1) if st.session_state.count > 1 else new_user_input_ids
+
+st.session_state.chat_history_ids = model.generate(bot_input_ids, max_length=5000, pad_token_id=tokenizer.eos_token_id)
+
+response = tokenizer.decode(st.session_state.chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+if st.session_state.old_response == response:
+    bot_input_ids = new_user_input_ids
+    
+    st.session_state.chat_history_ids = model.generate(bot_input_ids, max_length=5000, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(st.session_state.chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+st.write(f"Chatbot: {response}")
+st.session_state.old_response = response

requirements.txt

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+transformers
+streamlit
+torch