test rick and morty

app.py

@@ -1,11 +1,49 @@
+import os
 import gradio as gr
-from transformers import pipeline
 
-def greet(name):
-    return pipe
+HF_TOKEN = os.getenv('HF_TOKEN')
+hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "Rick-bot-flags")
 
-pipe = pipeline('sentiment-analysis')
+title = "Talk To Me Morty"
+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"/>
+</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><center><img src='https://visitor-badge.glitch.me/badge?page_id=kingabzpro/Rick_and_Morty_Bot' alt='visitor badge'></center></p>"
+examples = [["How are you Rick?"]]
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-iface = gr.Interface(fn=greet, inputs='text', outputs='text')
-# to share public link - set share=True in launch()
-iface.launch()
+tokenizer = AutoTokenizer.from_pretrained("ericzhou/DialoGPT-Medium-Rick_v2")
+model = AutoModelForCausalLM.from_pretrained("ericzhou/DialoGPT-Medium-Rick_v2")
+
+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
+
+gr.Interface(fn=predict,
+             title=title,
+             description=description,
+             examples=examples,
+             flagging_callback = hf_writer,
+             allow_flagging = "manual",
+             inputs=["text", "state"],
+             outputs=["chatbot", "state"],
+             theme='gradio/seafoam').launch()