Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# Load the language model and tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+
+# Define the coding mode function
+def coding_mode(text):
+    # Prepend the text with a coding prompt
+    prompt = ">>> "
+    input_text = prompt + text
+    # Generate a response from the model
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    output_ids = model.generate(input_ids, max_length=1000, do_sample=True)
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    # Remove the coding prompt from the response
+    response = response[len(prompt):]
+    return response
+
+# Define the normal mode function
+def normal_mode(text):
+    # Generate a response from the model
+    input_ids = tokenizer.encode(text, return_tensors="pt")
+    output_ids = model.generate(input_ids, max_length=1000, do_sample=True)
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return response
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=normal_mode,
+    inputs=gr.inputs.Chat(
+        placeholder="Enter your message here...",
+        prompt="Me: ",
+        allow_audio_input=False,
+        allow_video_input=False,
+    ),
+    outputs=gr.outputs.Textbox(placeholder="Output text will appear here..."),
+    title="Chatbot",
+    description="Enter text and the chatbot will respond!",
+    theme="compact",
+    layout="vertical",
+    allow_flagging=False,
+    allow_screenshot=False,
+    allow_sharing=False,
+    examples=[
+        ["Hi there!"],
+        ["What's your name?"],
+        ["How old are you?"],
+        ["What do you like to do for fun?"],
+    ],
+)
+
+# Add the coding mode toggle to the interface
+iface.add_mode("Coding Mode", coding_mode)
+
+# Launch the interface
+iface.launch(share=True)