Update app.py

app.py

@@ -1,28 +1,30 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+from transformers import AutoTokenizer, OpenAIGPTLMHeadModel
 import gradio as gr
 
-# Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("gpt-chatbot")
-model = AutoModelForCausalLM.from_pretrained("gpt-chatbot")
+tokenizer = AutoTokenizer.from_pretrained("openai-gpt")
+model = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
 
-
-def generate_response(input_text):
-    input_text = "User: " + input_text
-    input_ids = tokenizer.encode(input_text, return_tensors='pt')
-    generated_output = model.generate(input_ids, max_length=100, num_return_sequences=1)
-    response = tokenizer.decode(generated_output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
-    return response
+def generate_response(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs, labels=inputs["input_ids"])
+    generated_ids = model.generate(inputs['input_ids'], 
+                                   num_return_sequences=1,
+                                   max_length=50)
+    generated_text = tokenizer.decode(generated_ids.squeeze(), skip_special_tokens=True)
+    return generated_text
 
 iface = gr.Interface(
     fn=generate_response,
-    inputs='text',
-    outputs='text',
-    layout='vertical',
-    title='ChatGPT',
-    description='A simple chatbot powered by ChatGPT',
-    article= 'https://huggingface.co/models',
-    examples=[['Hello'], ['How are you?'], ['What is your name?']],
+    inputs="text",
+    outputs="text",
+    title="OpenAI GPT4 Chatbot",
+    description="Enter some text, and the OpenAI GPT model will generate a response.",
+    examples=[
+        ["Hello, how are you?"],
+        ["What is the capital of France?"],
+        ["Tell me a joke."]
+    ]
 )
 
-iface.launch()
+iface.launch()