Update pages/21_GraphRag.py

pages/21_GraphRag.py

@@ -1,19 +1,25 @@
 import streamlit as st
 import pandas as pd
-from transformers import AutoTokenizer, BertForSequenceClassification, BertConfig
+from transformers import AutoTokenizer, AutoModel
 import torch
+import graphrag
 
 @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
+    bert_model = AutoModel.from_pretrained(bert_model_name)
+
+    # Initialize Graphrag model
+    model = graphrag.GraphRAG(
+        bert_model,
+        num_labels=2,  # Adjust based on your task
+        num_hidden_layers=2,
+        hidden_size=768,
+        intermediate_size=3072,
+    )
+
+    # If you have a pre-trained Graphrag model, load it here
     # model.load_state_dict(torch.load('path_to_your_model.pth'))
     
     return tokenizer, model
@@ -23,11 +29,12 @@ def process_text(text, tokenizer, model):
     with torch.no_grad():
         outputs = model(**inputs)
     # Process outputs based on your specific task
-    logits = outputs.logits
+    # This is a placeholder; adjust according to your model's output
+    logits = outputs.logits if hasattr(outputs, 'logits') else outputs
     probabilities = torch.softmax(logits, dim=1)
     return probabilities.tolist()[0]
 
-st.title("BERT Text Analysis")
+st.title("Graphrag Text Analysis")
 
 tokenizer, model = load_model()
 
@@ -57,4 +64,4 @@ if st.button("Analyze Text"):
         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)")
+st.markdown("[Download Sample CSV](https://raw.githubusercontent.com/your_username/your_repo/main/sample_data.csv)")