Update app.py

app.py

@@ -2,46 +2,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
 import torch
 
+import gradio as gr
 
-title = "AI Agemt ChatBot"
-description = "A State-of-the-Art Agent Chatbot"
-examples = [["How are you?"]]
-
-
-tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K")
-model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K")
-
-
-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=4000, 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|>")
-    # print('decoded_response-->>'+str(response))
-    response = [
-        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
-    ]  # convert to tuples of list
-    # print('response-->>'+str(response))
-    return response, history
-
+demo = gr.load("EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code", src="models")
 
-gr.Interface(
-    fn=predict,
-    title=title,
-    description=description,
-    examples=examples,
-    inputs=["text", "state"],
-    outputs=["chatbot", "state"],
-    theme="finlaymacklon/boxy_violet",
-).launch()
+demo.launch()