Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# model_name = "dwojcik/gpt2-large-fine-tuned-context-256"
+model_name = "gpt2-large"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model.generation_config.temperature = 2.0
+tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="right")
+tokenizer.pad_token = tokenizer.eos_token
+
+def generate_response(user_message):
+    inputs = tokenizer.encode(user_message, return_tensors='pt')
+    outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+def user(user_message, history):
+    return gr.update(value="", interactive=False), history + [[user_message, None]]
+
+def bot(history):
+    user_message = history[-1][0]
+    bot_message = generate_response(user_message)
+    history[-1][1] = bot_message
+    return history
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # GPT-PTZE
+    This chatbot utilizes a fine-tuned GPT-2 large model from OpenAI to generate contextually relevant responses based on user input. It was trained on large corpus of data from Przegląd Elektrotechniczny.""")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Your input")
+    clear = gr.Button("Clear")
+
+    response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    response.then(lambda: gr.update(interactive=True), None, [msg], queue=False)
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue()
+demo.launch(server_name="0.0.0.0")