add chat agent

app.py

@@ -1,27 +1,28 @@
-import random
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-import gradio as gr
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+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=1000, pad_token_id=tokenizer.eos_token_id).tolist()
 
-def chat(message, history):
-    history = history or []
-    if message.startswith("How many"):
-        response = random.randint(1, 10)
-    elif message.startswith("How"):
-        response = random.choice(["Great", "Good", "Okay", "Bad"])
-    elif message.startswith("Where"):
-        response = random.choice(["Here", "There", "Somewhere"])
-    else:
-        response = "I don't know"
-    history.append((message, response))
-    return history, 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
+
+import gradio as gr
 
-chatbot = gr.Chatbot(color_map=("green", "gray"))
-demo = gr.Interface(
-    chat,
-    ["text", "state"],
-    [chatbot, "state"],
-    allow_screenshot=False,
-    allow_flagging="never",
-)
-demo.launch()
+gr.Interface(fn=predict,
+             theme="default",
+             css=".footer {display:none !important}",
+             inputs=["text", "state"],
+             outputs=["chatbot", "state"]).launch()

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch