Update app.py

app.py

@@ -1,38 +1,69 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
+import time
+import torch
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "nicholasKluge/Aira-Instruct-PT-124"
+token = "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"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+if device == "cuda":
+    model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=token, load_in_8bit=True)
+    
+else:
+    model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=token)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=token)
+model.to(device)
+
+intro = """
+## What is `Aira`?
+
+[`Aira`](https://github.com/Nkluge-correa/Aira-EXPERT) is a `chatbot` designed to simulate the way a human (expert) would behave during a round of questions and answers (Q&A). `Aira` has many iterations, from a closed-domain chatbot based on pre-defined rules to an open-domain chatbot achieved via fine-tuning pre-trained large language models. Aira has an area of expertise that comprises topics related to AI Ethics and AI Safety research. 
 
-tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-Instruct-PT-124M',
-    use_auth_token="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")
-model = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-Instruct-PT-124M',
-    use_auth_token="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") 
+We developed our open-domain conversational chatbots via conditional text generation/instruction fine-tuning. This approach has a lot of limitations. Even though we can make a chatbot that can answer questions about anything, forcing the model to produce good-quality responses is hard. And by good, we mean **factual** and **nontoxic**  text. This leads us to two of the most common problems of generative models used in conversational applications:
 
-disclaimer = """**`AVISO`:** Esta demonstração deve ser usada apenas para fins de pesquisa. O uso comercial é estritamente **proibido**. A saída do modelo não é censurada e os autores não endossam as opiniões no conteúdo gerado. **Use por sua própria conta e risco**."""
+🤥 Generative models can perpetuate the generation of pseudo-informative content, that is, false information that may appear truthful.
+ 
+🤬 In certain types of tasks, generative models can produce harmful and discriminatory content inspired by historical stereotypes against sensitive attributes (for example, gender, race, and religion).
+ 
+`Aira` is intended only for academic research. For more information, visit our [HuggingFace models](https://huggingface.co/nicholasKluge) to see how we developed `Aira`.
+"""
+
+disclaimer = """
+**Disclaimer:** You should use this demo for research purposes only. Moderators do not censor the model output, and the authors do not endorse the opinions generated by this model.
+
+If you would like to complain about any message produced by `Aira`, please contact [nicholas@airespucrs.org](mailto:nicholas@airespucrs.org).
+"""
 
 with gr.Blocks(theme='freddyaboulton/dracula_revamped') as demo:
+ 
+    gr.Markdown("""<h1><center>Aira Demo 🤓💬</h1></center>""")
+    gr.Markdown(intro)
     
-    gr.Markdown("""<h1><center>🔥Aira-PT Demo 🤓🚀</h1></center>""")
+    chatbot = gr.Chatbot(label="Aira").style(height=500)
+
+    with gr.Accordion(label="Parameters ⚙️", open=False):
+        top_k = gr.Slider( minimum=10, maximum=100, value=50, step=5, interactive=True, label="Top-k",)
+        top_p = gr.Slider( minimum=0.1, maximum=1.0, value=0.70, step=0.05, interactive=True, label="Top-p",)
+        temperature = gr.Slider( minimum=0.001, maximum=2.0, value=0.1, step=0.1, interactive=True, label="Temperature",)
+        max_length = gr.Slider( minimum=10, maximum=500, value=100, step=10, interactive=True, label="Max Length",)
     
-    with gr.Row(scale=1, equal_height=True):
-        
-        with gr.Column(scale=5):
-            chatbot = gr.Chatbot(label="Aira").style(height=300)
-        
-        with gr.Column(scale=2):
-           
-            with gr.Tab(label="Parâmetros ⚙️"):
-                top_k = gr.Slider( minimum=10, maximum=100, value=50, step=5, interactive=True, label="Top-k",)
-                top_p = gr.Slider( minimum=0.1, maximum=1.0, value=0.70, step=0.05, interactive=True, label="Top-p",)
-                temperature = gr.Slider( minimum=0.001, maximum=2.0, value=0.1, step=0.1, interactive=True, label="Temperatura",)
-                max_length = gr.Slider( minimum=10, maximum=500, value=100, step=10, interactive=True, label="Comprimento Máximo",)
-
-    msg = gr.Textbox(label="Faça uma pergunta para Aira", placeholder="Olá Aira, como vai você?")
-    clear = gr.Button("Limpar Conversa 🧹")
+    msg = gr.Textbox(label="Write a question or comment to Aira ...", placeholder="Hi Aira, how are you?")
+
+    clear = gr.Button("Clear Conversation 🧹")
     gr.Markdown(disclaimer)
 
-    def generate_response(message, chat_history, top_k, top_p, temperature, max_length):
-        inputs = tokenizer(tokenizer.bos_token + message + tokenizer.eos_token, return_tensors="pt")
+    def user(user_message, chat_history):
+        return gr.update(value=user_message, interactive=True), chat_history + [["👤 " + user_message, None]]
+
+    def generate_response(user_msg, top_p, temperature, top_k, max_length, chat_history):
 
-        response = model.generate(**inputs,
+        inputs = tokenizer(tokenizer.bos_token + user_msg + tokenizer.eos_token, return_tensors="pt").to(device)
+
+        generated_response = model.generate(**inputs,
             bos_token_id=tokenizer.bos_token_id,
             pad_token_id=tokenizer.pad_token_id,
             eos_token_id=tokenizer.eos_token_id,
@@ -43,12 +74,21 @@ with gr.Blocks(theme='freddyaboulton/dracula_revamped') as demo:
             top_p=top_p,
             temperature=temperature, 
             num_return_sequences=1)
-        
-        chat_history.append((f"👤 {message}", f"""🤖 {tokenizer.decode(response[0], skip_special_tokens=True).replace(message, "")}"""))
 
-        return "", chat_history
-    
-    msg.submit(generate_response, [msg, chatbot, top_k, top_p, temperature, max_length], [msg, chatbot])
+        bot_message = tokenizer.decode(generated_response[0], skip_special_tokens=True).replace(user_msg, "")
+
+        chat_history[-1][1] = "🤖 "
+        for character in bot_message:
+            chat_history[-1][1] += character
+            time.sleep(0.005)
+            yield chat_history
+
+    response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        generate_response, [msg, top_p, temperature, top_k, max_length, chatbot], chatbot
+    )
+    response.then(lambda: gr.update(interactive=True), None, [msg], queue=False)
+    msg.submit(lambda x: gr.update(value=''), [],[msg])
     clear.click(lambda: None, None, chatbot, queue=False)
 
+demo.queue()
 demo.launch()