Duplicate from abidlabs/chatbot-stylized

Co-authored-by: Abubakar Abid <abidlabs@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Chatbot Minimal
+emoji: 📊
+colorFrom: red
+colorTo: indigo
+sdk: gradio
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: abidlabs/chatbot-stylized
+---
+
+# 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`, `streamlit`, or `static`
+
+`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, or `static` html code).
+Path is relative to the root of the repository.
+
+`models`: _List[string]_
+HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`datasets`: _List[string]_
+HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+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 lines
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response.remove("")
+    
+    # write some HTML
+    html = "<div class='chatbot'>"
+    for m, msg in enumerate(response):
+        cls = "user" if m%2 == 0 else "bot"
+        html += "<div class='msg {}'> {}</div>".format(cls, msg)
+    html += "</div>"
+    
+    return html, history
+
+import gradio as gr
+
+css = """
+.chatbox {display:flex;flex-direction:column}
+.msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
+.msg.user {background-color:cornflowerblue;color:white}
+.msg.bot {background-color:lightgray;align-self:self-end}
+.footer {display:none !important}
+"""
+
+gr.Interface(fn=predict,
+             theme="default",
+             inputs=[gr.inputs.Textbox(placeholder="How are you?"), "state"],
+             outputs=["html", "state"],
+             css=css).launch()
+                     

requirements.txt

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+transformers
+torch