testing multiple

app.py

@@ -1,49 +1,60 @@
-import os
+# import os
+# import gradio as gr
+
+# HF_TOKEN = os.getenv('HF_TOKEN')
+# hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "Rick-bot-flags")
+
+# 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
+
+# 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()
+
+
 import gradio as gr
 
-HF_TOKEN = os.getenv('HF_TOKEN')
-hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "Rick-bot-flags")
-
-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
-
-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()
+with gr.Blocks() as demo:
+  with gr.Tab("Translate to Spanish"):
+    gr.load("gradio/en2es", src="spaces")
+  with gr.Tab("Translate to French"):
+    gr.load("abidlabs/en2fr", src="spaces")
+
+demo.launch()