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import gradio as gr
def chatbot_response(user_input):
# Handle basic greeting
if user_input.lower() in ["hello", "hi"]:
return "Hello! How can I assist you today?"
# Add more conditions for different queries here
elif "supervised learning" in user_input.lower():
return "Supervised learning is a machine learning approach where models are trained using labeled data."
# If no predefined match, ask for more clarification
else:
return "I'm here to assist with academic questions. Please specify what you'd like help with."
with gr.Blocks() as demo:
gr.Markdown("### Study Assistance Chatbot")
gr.Markdown("Welcome! Ask me anything related to your academic studies.")
with gr.Row():
with gr.Column():
user_input = gr.Textbox(label="Enter your question here:")
submit_button = gr.Button("Submit")
with gr.Column():
chatbot_output = gr.Textbox(label="Chatbot Response", interactive=False)
submit_button.click(chatbot_response, inputs=user_input, outputs=chatbot_output)
demo.launch()
from datasets import load_dataset
# Load a sample dataset from Hugging Face
dataset = load_dataset("squad") # you can replace "squad" with any dataset you're using
# Print the first few entries to verify that it’s loaded
print(dataset["train"][0]) # Prints the first example from the training set
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
# Load pre-trained GPT-2 model and tokenizer from Hugging Face
model_name = "gpt2" # You can use other models such as 'distilgpt2' for faster responses
# Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Create a pipeline for text generation
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
def chatbot_response(user_input):
# Generate a response using the model
response = generator(user_input, max_length=100, num_return_sequences=1)
# Extract and return the generated text
return response[0]['generated_text']
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