app.py

import pandas as pd
import requests
import json
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
import streamlit as st

# Fetch the dataset
url = 'https://huggingface.co/spaces/AreesaAshfaq/ContentGenerator/raw/main/article-2020.json'
response = requests.get(url)
data = response.json()

# Convert to DataFrame
df = pd.DataFrame(data)
#print(df.head())


# Initialize the tokenizer, retriever, and model
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq")

def generate_article(query):
    inputs = tokenizer(query, return_tensors="pt")
    input_ids = inputs["input_ids"]
    
    # Generate articles
    with torch.no_grad():
        outputs = model.generate(input_ids=input_ids, num_beams=4, max_length=200)
    
    article = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return article


# Set up the Streamlit app
st.title('Tech Article Generator using RAG')

# User input for query
query = st.text_input("Enter a topic or keyword:")

if st.button("Generate Article"):
    if query:
        # Generate article
        article = generate_article(query)
        st.subheader("Generated Article:")
        st.write(article)
    else:
        st.error("Please enter a query to generate an article.")