Create app.py

app.py

@@ -0,0 +1,52 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import BlenderbotForConditionalGeneration
+import torch
+imprt gradio as gr
+
+# model_name = "facebook/blenderbot-400M-distill"
+model_name = "microsoft/DialoGPT-medium"
+chat_tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def conversation(user_input, chat_history=[]):
+    user_input_ids = chat_tokenizer(user_input+chat_tokenizer.eos_token, return_tensors="pt").input_ids
+
+    # maintain history in the tensor
+    chatbot_input_ids = torch.cat([torch.LongTensor(chat_hostory), user_input_ids], dim=-1)
+
+    # ger response
+    chat_history = model.generate(chatbot_input_ids, max_length=1000, pad_token_id=chat_tokenizer.eos_token_id).tolist()
+    print(chat_history)
+
+    response = chat_tokeniser.decode(chat_history[0]).split("<|endoftext|>")
+    print("Starting to print response")
+    print(response)
+
+    # html for display
+    html = "<div class='mychatbot'>"
+    for x, msg in enumerate(response):
+        if x%2 !=0:
+            msg = "ChatBot:" + msg
+            class = "bot"
+        else:
+            class = "user"
+
+        print("Value of x:")
+        print(x)
+        print("Message:")
+        print(msg)
+        html += "<div class='msg {}'></div>".format(class, msg)
+    html += "</div>"
+    print(html)
+    return html, chat_history
+
+# UX
+css = """
+.mychat {display:flex;flex-direction:column}
+.msg {padding:5px;margin-bottom:5px;border-radius:5px;width:75%}
+.msg.user {background-color:lightblue;color:white}
+.msg.bot {background-color:orange;color:white,align-self:self-end}
+.footer
+"""
+in_text = gr.inputs.Textbox(placeholder="Let's start a chat...")
+gr.Interface(fn=conversation, theme="default", inputs=[in_text, "state"], outputs=["html", "state"], css=css).launch()