create the file

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name="fadliaulawi/polylm-1.7b-finetuned"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+def user(message, history):
+    return "", history + [[message, None]]
+
+
+def bot(history,temperature, max_length, top_p,top_k):
+    user_message = history[-1][0]
+    new_user_input_ids = tokenizer.encode(
+        user_message + tokenizer.eos_token, return_tensors="pt"
+    )
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1)
+
+    # generate a response
+    response = model.generate(
+        bot_input_ids,
+        pad_token_id=tokenizer.eos_token_id,
+        temperature = float(temperature),
+        max_length=max_length,
+        top_p=float(top_p),
+        top_k=top_k,
+        do_sample=True
+    ).tolist()
+
+    # convert the tokens to text, and then split the responses into lines
+    response = tokenizer.decode(response[0]).split("<|endoftext|>")
+    response = [
+        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
+    ]  # convert to tuples of list
+    history[-1] = response[0]
+    return history
+
+
+with gr.Blocks() as demo:
+    temperature = gr.Slider(0, 5, value=0.8, step=0.1, label='Temperature')
+    max_length = gr.Slider(0, 8192, value=256, step=1, label='Max Length')
+    top_p = gr.Slider(0, 1, value=0.8, step=0.1, label='Top P')
+    top_k = gr.Slider(0, 50, value=50, step=1, label='Top K')
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    submit = gr.Button("Submit")
+    clear = gr.Button("Clear")
+
+    examples = gr.Examples(examples=["Hi Doctor"],inputs=[msg])
+
+    #submit.click(bot,[msg,chatbot,temperature, max_length, top_p,top_k],chatbot)
+    submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, [chatbot,temperature,max_length,top_p,top_k], chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.launch()