Update pages/21_GraphRag.py

pages/21_GraphRag.py

@@ -1 +1,66 @@
-# put code here
+# put code here
+
+import streamlit as st
+import pandas as pd
+from transformers import AutoTokenizer, AutoModel
+from graphrag import GraphragModel, GraphragConfig
+import torch
+
+@st.cache_resource
+def load_model():
+    bert_model_name = "bert-base-uncased"
+    tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
+    bert_model = AutoModel.from_pretrained(bert_model_name)
+
+    config = GraphragConfig(
+        bert_model=bert_model,
+        num_labels=2,  # Adjust based on your task
+        num_hidden_layers=2,
+        hidden_size=768,
+        intermediate_size=3072,
+    )
+
+    model = GraphragModel(config)
+    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
+    # This is a placeholder; adjust according to your model's output
+    logits = outputs.logits
+    probabilities = torch.softmax(logits, dim=1)
+    return probabilities.tolist()[0]
+
+st.title("Graphrag 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)")