Duplicate from mandar100/chatbot_dialogpt

Co-authored-by: mandar <mandar100@users.noreply.huggingface.co>

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README.md

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+---
+title: Chatbot Dialogpt
+emoji: 🦀
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 3.12.0
+app_file: app.py
+pinned: false
+duplicated_from: mandar100/chatbot_dialogpt
+---
+This code deploys dialogpt model with gradio.
+User can chat with a bot using dialogpt model.
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
+
+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,
+             inputs=["text", "state"],
+             outputs=["chatbot", "state"]).launch()
+

requirements.txt

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+gradio
+transformers
+torch