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import gradio as gr
from transformers import AutoTokenizer, AutoModel, GPT2LMHeadModel, GPT2Tokenizer
import torch
# Load the bi-encoder model and tokenizer
bi_encoder_model_name = "sentence-transformers/all-MiniLM-L6-v2"
bi_tokenizer = AutoTokenizer.from_pretrained(bi_encoder_model_name)
bi_model = AutoModel.from_pretrained(bi_encoder_model_name)
# Load the GPT-2 model and tokenizer for response generation
gpt2_model_name = "gpt2"
gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model_name)
def encode_text(text):
inputs = bi_tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=128)
outputs = bi_model(**inputs)
# Ensure the output is 2D by averaging the last hidden state along the sequence dimension
return outputs.last_hidden_state.mean(dim=1).detach().numpy()
def generate_response(user_input, context_embedding):
# Combine user input with context embedding for GPT-2 input
combined_input = user_input + " " + context_embedding
# Generate a response using GPT-2 with adjusted parameters
gpt2_inputs = gpt2_tokenizer.encode(combined_input, return_tensors='pt')
gpt2_outputs = gpt2_model.generate(
gpt2_inputs,
max_length=150,
num_return_sequences=1,
temperature=0.5,
top_p=0.9,
repetition_penalty=1.2
)
generated_text = gpt2_tokenizer.decode(gpt2_outputs[0], skip_special_tokens=True)
return generated_text
def chatbot(user_input, context=""):
context_embedding = encode_text(context) if context else ""
response = generate_response(user_input, context_embedding)
return response
# Create the Gradio interface
iface = gr.Interface(
fn=chatbot,
inputs=[gr.Textbox(lines=2, placeholder="Enter your message here..."), gr.Textbox(lines=2, placeholder="Enter context here (optional)...")],
outputs="text",
title="Context-Aware Dynamic Response Chatbot",
description="A chatbot using a bi-encoder model to understand the input context and GPT-2 to generate dynamic responses."
)
# Launch the interface
iface.launch()
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