Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Select the models you want to offer for chat
+MODELS = [
+    "gpt2",
+    "distilgpt2",
+    "openai-gpt",
+    "openai-gpt-2",
+    "openai-gpt3",
+]
+
+# Define the system prompt
+SYSTEM_PROMPT = "You are a helpful assistant. Answer the user's questions as best as you can."
+
+# Create a dictionary to store conversation history
+conversation_history = {}
+
+# Create a function to generate the chatbot response
+def chatbot_response(input_text, model_name, system_prompt):
+    # Load the selected model and tokenizer
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    # Define the chatbot pipeline
+    chatbot_pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+    # Prepare the input for the chatbot
+    inputs = tokenizer([SYSTEM_PROMPT + " " + input_text], return_tensors="pt")
+
+    # Generate a response from the chatbot
+    response = chatbot_pipe(inputs)
+
+    # Return the response
+    return response[0]['generated_text'].strip()
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=chatbot_response,
+    inputs=[
+        gr.inputs.Textbox(label="User input"),
+        gr.inputs.Radio(choices=MODELS, label="Model"),
+        gr.inputs.Textbox(label="System prompt", value=SYSTEM_PROMPT),
+    ],
+    outputs="text",
+    title="Large Language Model Chatbot",
+    description="Chat with a large language model from the HuggingFace Transformers library.",
+)
+
+# Initialize the conversation history
+for model in MODELS:
+    conversation_history[model] = []
+
+# Define a function to update the conversation history
+def update_history(history, new_message):
+    history.append(new_message)
+    return history
+
+# Define a function to display the conversation history
+def display_history(history):
+    return "\n".join(history)
+
+# Create a Gradio block to display the conversation history
+history_block = gr.Block(
+    label="Conversation History",
+    elem_id="history",
+    visible=False,
+)
+
+# Update the conversation history when a new message is sent
+def update_history_on_message(history, model, new_message):
+    history = update_history(history, new_message)
+    conversation_history[model] = history
+    return history
+
+# Display the conversation history
+def display_history_on_message(history):
+    return display_history(history)
+
+# Define event handlers for the Gradio interface
+interface.change(history_block.update, [conversation_history], queue=False)
+interface.submit(update_history_on_message, [conversation_history], [conversation_history], queue=False)
+history_block.change(display_history_on_message, [conversation_history], queue=False)
+
+# Launch the Gradio interface
+interface.launch()