Update app.py

app.py

@@ -4,16 +4,14 @@ import gradio as gr
 HF_TOKEN = os.getenv('HF_TOKEN')
 hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "Rick-bot-flags")
 
-title = "Talk To Me Morty"
+title = "Ask Rick a Question"
 description = """
-<p>
 <center>
-The bot was trained on Rick and Morty dialogues Kaggle Dataset using DialoGPT.
-<img src="https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot/resolve/main/img/rick.png" alt="rick" width="200"/>
+The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!
+![](rick.png)
 </center>
-</p>
 """
-article = "<p style='text-align: center'><a href='https://medium.com/geekculture/discord-bot-using-dailogpt-and-huggingface-api-c71983422701' target='_blank'>Complete Tutorial</a></p><p style='text-align: center'><a href='https://dagshub.com/kingabzpro/DailoGPT-RickBot' target='_blank'>Project is Available at DAGsHub</a></p></center></p>"
+article = "Check out (the original Rick and Morty Bot)[https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot] that this demo is based off of."
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
@@ -21,7 +19,7 @@ import torch
 tokenizer = AutoTokenizer.from_pretrained("ericzhou/DialoGPT-Medium-Rick_v2")
 model = AutoModelForCausalLM.from_pretrained("ericzhou/DialoGPT-Medium-Rick_v2")
 
-def predict(input, history=[]):
+def predict(input):
     # tokenize the new input sentence
     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
 
@@ -33,13 +31,6 @@ def predict(input, history=[]):
 
     # convert the tokens to text, and then split the responses into the right format
     response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-    return response, history
+    return response[1]
 
-gr.Interface(fn = predict, inputs = ["textbox","state"], outputs = ["chatbot","state"],allow_flagging = "manual",title = title, flagging_callback = hf_writer, description = description, article = article ).launch(enable_queue=True)  # customizes the input component
-
- #theme ="grass", 
- #title = title, 
- #flagging_callback=hf_writer,
- #description = description, 
- #article = article
+gr.Interface(fn = predict, inputs = ["textbox"], outputs = ["text"],allow_flagging = "manual",title = title, flagging_callback = hf_writer, description = description, article = article ).launch(enable_queue=True)  # customizes the input component