import gradio as gr
from transformers import AutoTokenizer, AutoModel
from openai import OpenAI
import os
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# Load the NASA-specific bi-encoder model and tokenizer
bi_encoder_model_name = "nasa-impact/nasa-smd-ibm-st-v2"
bi_tokenizer = AutoTokenizer.from_pretrained(bi_encoder_model_name)
bi_model = AutoModel.from_pretrained(bi_encoder_model_name)

# Set up OpenAI client
api_key = os.getenv('OPENAI_API_KEY')
client = OpenAI(api_key=api_key)

def encode_text(text):
    inputs = bi_tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=128)
    outputs = bi_model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).detach().numpy()

def retrieve_relevant_context(user_input, context_texts):
    user_embedding = encode_text(user_input)
    context_embeddings = np.array([encode_text(text) for text in context_texts])
    similarities = cosine_similarity(user_embedding, context_embeddings).flatten()
    most_relevant_idx = np.argmax(similarities)
    return context_texts[most_relevant_idx]

def generate_response(user_input, relevant_context):
    combined_input = f"Context: {relevant_context}\nQuestion: {user_input}\nAnswer:"
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "user", "content": combined_input}
        ],
        max_tokens=150,
        temperature=0.7,
        top_p=0.9,
        frequency_penalty=0.5,
        presence_penalty=0.0
    )
    return response.choices[0].message['content'].strip()

def chatbot(user_input, context=""):
    context_texts = context.split("\n")
    relevant_context = retrieve_relevant_context(user_input, context_texts) if context else ""
    response = generate_response(user_input, relevant_context)
    return response

# Create the Gradio interface
iface = gr.Interface(
    fn=chatbot,
    inputs=[
        gr.Textbox(lines=2, placeholder="Enter your message here..."),
        gr.Textbox(lines=5, placeholder="Enter context here, separated by new lines...")
    ],
    outputs="text",
    title="Context-Aware Dynamic Response Chatbot",
    description="A chatbot using a NASA-specific bi-encoder model to understand the input context and GPT-4 to generate dynamic responses. Enter context to get more refined and relevant responses."
)

# Launch the interface
iface.launch()