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import pandas as pd
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, Trainer, TrainingArguments
from Features.chat_interface import start_chat_interface
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, Trainer, TrainingArguments
from Features.chat_interface import start_chat_interface
import start_chat_interface, generate_response # Import needed functions
import os
# --- Step 1: Prepare Your Data ---
data = pd.read_csv("your_chatbot_data.csv") # Replace with your dataset file
data = data[["user_input", "chatbot_response"]] # Adjust column names if needed
data_path = "data/chatbot_data.csv"
data = pd.read_csv(data_path)
# --- Step 2: Choose a Pre-trained Model ---
model_name = "microsoft/DialoGPT-medium" # A good starting point, experiment with others!
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# --- Step 3: Tokenize the Data ---
def preprocess(examples):
inputs = [ex + tokenizer.eos_token for ex in examples["user_input"]]
targets = examples["chatbot_response"]
model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding=True)
with tokenizer.as_target_tokenizer():
model_inputs["labels"] = tokenizer(targets, max_length=128, truncation=True, padding=True).input_ids
return model_inputs
tokenized_data = data.apply(preprocess, axis=1)
# --- Step 4: Fine-Tune the Model ---
training_args = TrainingArguments(
"my-chatbot", # Output folder name
evaluation_strategy="steps",
learning_rate=2e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
save_steps=500, # Save model checkpoint every 500 steps
push_to_hub=True, # Push the model to your Hugging Face Hub
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_data,
eval_dataset=tokenized_data,
)
trainer.train()
# --- Step 5: Use Your Fine-Tuned Model (After Training) ---
def generate_response(user_input):
input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
output_sequences = model.generate(input_ids=input_ids)
response = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
return response
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