pages/21_GraphRag.py

import streamlit as st
import pandas as pd
from transformers import AutoTokenizer, BertForSequenceClassification, BertConfig
import torch

@st.cache_resource
def load_model():
    bert_model_name = "bert-base-uncased"
    tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
    
    config = BertConfig.from_pretrained(bert_model_name)
    config.num_labels = 2  # Adjust based on your task
    
    model = BertForSequenceClassification.from_pretrained(bert_model_name, config=config)
    
    # If you have a pre-trained model, load it here
    # model.load_state_dict(torch.load('path_to_your_model.pth'))
    
    return tokenizer, model

def process_text(text, tokenizer, model):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    # Process outputs based on your specific task
    logits = outputs.logits
    probabilities = torch.softmax(logits, dim=1)
    return probabilities.tolist()[0]

st.title("BERT Text Analysis")

tokenizer, model = load_model()

# File uploader
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

if uploaded_file is not None:
    data = pd.read_csv(uploaded_file)
    st.write(data.head())

    if st.button("Process Data"):
        results = []
        for text in data['text']:  # Assuming your CSV has a 'text' column
            result = process_text(text, tokenizer, model)
            results.append(result)
        
        data['results'] = results
        st.write(data)

# Text input for single prediction
text_input = st.text_area("Enter text for analysis:")
if st.button("Analyze Text"):
    if text_input:
        result = process_text(text_input, tokenizer, model)
        st.write(f"Analysis Result: {result}")
    else:
        st.write("Please enter some text to analyze.")

# Add a link to sample data
st.markdown("[Download Sample CSV](https://raw.githubusercontent.com/your_username/your_repo/main/sample_data.csv)")