import os
import json
import firebase_admin
from firebase_admin import credentials, db
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
import gradio as gr

# Initialize Firebase Admin SDK
firebase_credential = os.getenv("FIREBASE_CREDENTIALS")
if not firebase_credential:
    raise RuntimeError("FIREBASE_CREDENTIALS environment variable is not set.")

# Save Firebase credentials to a temporary file
with open("serviceAccountKey.json", "w") as f:
    f.write(firebase_credential)

# Initialize Firebase App
cred = credentials.Certificate("serviceAccountKey.json")
firebase_admin.initialize_app(cred, {"databaseURL": "https://your-database-name.firebaseio.com/"})

# Load the RAG model, tokenizer, and retriever
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
retriever = RagRetriever.from_pretrained("facebook/rag-token-base", use_dummy_dataset=True)  # Use a dummy dataset for now
model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-base")

# Function to generate answers using the RAG model
def generate_answer(question, context=""):
    # Tokenize the question and context
    inputs = tokenizer(question, return_tensors="pt")
    
    # Retrieve relevant documents (dummy dataset for this example)
    # In a real-world case, you would provide a proper knowledge base or corpus
    retrieved_docs = retriever(question=question, input_ids=inputs["input_ids"])
    
    # Generate the answer using the RAG model
    outputs = model.generate(input_ids=inputs["input_ids"], 
                             context_input_ids=retrieved_docs["context_input_ids"])
    
    # Decode the generated answer
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return answer

# Gradio interface function
def dashboard(question):
    # Generate the answer from the RAG model
    answer = generate_answer(question)
    return answer

# Gradio Interface Setup
interface = gr.Interface(fn=dashboard, inputs="text", outputs="text")

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()