import gradio as gr
from transformers import GPT2Tokenizer, GPT2LMHeadModel, AutoModelForSeq2SeqLM, AutoTokenizer
import torch
from langchain.memory import ConversationBufferMemory

# Move model to device (GPU if available)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

# Load the tokenizer and model for DistilGPT-2
tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
model = GPT2LMHeadModel.from_pretrained("distilgpt2")
model.to(device)

# Load summarization model (e.g., T5-small)
summarizer_tokenizer = AutoTokenizer.from_pretrained("t5-small")
summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("t5-small").to(device)

def summarize_history(history):
    input_ids = summarizer_tokenizer.encode(
        "summarize: " + history,
        return_tensors="pt"
    ).to(device)
    summary_ids = summarizer_model.generate(input_ids, max_length=50, min_length=25, length_penalty=5., num_beams=2)
    summary = summarizer_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

# Set up conversational memory using LangChain's ConversationBufferMemory
memory = ConversationBufferMemory()

# Define the chatbot function with memory
def chat_with_distilgpt2(input_text):
    # Retrieve conversation history
    conversation_history = memory.load_memory_variables({})['history']
    
    # Summarize if history exceeds certain length
    if len(conversation_history.split()) > 200:
        conversation_history = summarize_history(conversation_history)
    
    # Combine the (possibly summarized) history with the current user input
    full_input = f"{conversation_history}\nUser: {input_text}\nAssistant:"
    
    # Tokenize the input and convert to tensor
    input_ids = tokenizer.encode(full_input, return_tensors="pt").to(device)
    
    # Generate the response using the model with adjusted parameters
    outputs = model.generate(
        input_ids,
        max_length=input_ids.shape[1] + 100,  # Limit total length
        max_new_tokens=100,
        num_return_sequences=1,
        no_repeat_ngram_size=3,
        repetition_penalty=1.2,
        temperature=0.7,
        top_k=20,
        top_p=0.8,
        early_stopping=True,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )
    
    # Decode the model output
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Update the memory with the user input and model response
    memory.save_context({"input": input_text}, {"output": response})
    
    return response

# Set up the Gradio interface
interface = gr.Interface(
    fn=chat_with_distilgpt2,
    inputs=gr.Textbox(label="Chat with DistilGPT-2"),
    outputs=gr.Textbox(label="DistilGPT-2's Response"),
    title="DistilGPT-2 Chatbot with Memory",
    description="This is a simple chatbot powered by the DistilGPT-2 model with conversational memory, using LangChain.",
)

# Launch the Gradio app
interface.launch()