File size: 2,080 Bytes
dbad9b8 f907b1c 72d38f5 dbad9b8 72d38f5 f907b1c 72d38f5 f907b1c 72d38f5 f907b1c dbad9b8 72d38f5 f907b1c dbad9b8 72d38f5 dbad9b8 72d38f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
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()
|