Update pages/21_GraphRag.py

pages/21_GraphRag.py

@@ -1,99 +1,46 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModel
-import torch
-import networkx as nx
-import matplotlib.pyplot as plt
-from collections import Counter
 import graphrag
 import inspect
 
-st.title("GraphRAG Module Exploration and Text Analysis")
+st.title("GraphRAG Module Explorer")
 
-# Diagnostic section
+# Display all attributes and functions in the graphrag module
 st.header("GraphRAG Module Contents")
 graphrag_contents = dir(graphrag)
-st.write("Available attributes and methods in graphrag module:")
-for item in graphrag_contents:
-    st.write(f"- {item}")
-    attr = getattr(graphrag, item)
-    if inspect.isclass(attr) or inspect.isfunction(attr):
-        st.write(f"  Signature: {inspect.signature(attr)}")
-        st.write(f"  Docstring: {attr.__doc__}")
 
-# Attempt to find a suitable model class
-model_class = None
 for item in graphrag_contents:
-    if 'model' in item.lower():
-        model_class = getattr(graphrag, item)
-        st.write(f"Found potential model class: {item}")
-        break
-
-if model_class is None:
-    st.error("Could not find a suitable model class in graphrag module.")
-    st.stop()
-
-@st.cache_resource
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    bert_model = AutoModel.from_pretrained("bert-base-uncased")
-    
-    # Initialize graphrag model
-    # Note: This is a placeholder. Adjust based on the actual model class found
-    graph_rag_model = model_class(
-        bert_model,
-        num_labels=2,  # For binary sentiment classification
-        # Add or remove parameters based on the actual model's requirements
-    )
-    
-    return tokenizer, graph_rag_model
-
-def text_to_graph(text):
-    words = text.split()
-    G = nx.Graph()
-    for i, word in enumerate(words):
-        G.add_node(i, word=word)
-        if i > 0:
-            G.add_edge(i-1, i)
-    
-    edge_index = [[e[0] for e in G.edges()] + [e[1] for e in G.edges()],
-                  [e[1] for e in G.edges()] + [e[0] for e in G.edges()]]
-    
-    return {
-        "edge_index": edge_index,
-        "num_nodes": len(G.nodes()),
-        "node_feat": [[ord(word[0])] for word in words],  # Use ASCII value of first letter as feature
-        "edge_attr": [[1] for _ in range(len(G.edges()) * 2)],  # All edges have the same attribute
-    }
-
-def analyze_text(text, tokenizer, model):
-    # Tokenize the text
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
-    
-    # Create graph representation
-    graph = text_to_graph(text)
-    
-    # Combine tokenized input with graph representation
-    # Note: This is a placeholder. Adjust based on the actual model's input requirements
-    combined_input = {
-        "input_ids": inputs["input_ids"],
-        "attention_mask": inputs["attention_mask"],
-        "edge_index": torch.tensor(graph["edge_index"], dtype=torch.long),
-        "node_feat": torch.tensor(graph["node_feat"], dtype=torch.float),
-        "edge_attr": torch.tensor(graph["edge_attr"], dtype=torch.float),
-        "num_nodes": graph["num_nodes"]
-    }
-    
-    # Perform inference
-    with torch.no_grad():
-        outputs = model(**combined_input)
-    
-    # Process outputs
-    # Note: Adjust this based on the actual model's output format
-    logits = outputs.logits if hasattr(outputs, 'logits') else outputs
-    probabilities = torch.softmax(logits, dim=1)
-    sentiment = "Positive" if probabilities[0][1] > probabilities[0][0] else "Negative"
-    confidence = probabilities[0][1].item() if sentiment == "Positive" else probabilities[0][0].item()
-    
-    return sentiment, confidence, graph
-
-# Rest of the Streamlit app (text input, analysis button, etc.) remains the same...
+    attr = getattr(graphrag, item)
+    st.subheader(f"{item}")
+    st.write(f"Type: {type(attr)}")
+    
+    if inspect.isclass(attr):
+        st.write("Class Methods:")
+        for name, method in inspect.getmembers(attr, predicate=inspect.isfunction):
+            st.write(f"- {name}")
+            st.write(f"  Signature: {inspect.signature(method)}")
+            st.write(f"  Docstring: {method.__doc__}")
+    
+    elif inspect.isfunction(attr):
+        st.write("Function:")
+        st.write(f"Signature: {inspect.signature(attr)}")
+        st.write(f"Docstring: {attr.__doc__}")
+    
+    elif isinstance(attr, (int, float, str, bool)):
+        st.write(f"Value: {attr}")
+    
+    st.write("---")
+
+# Display the module's docstring if available
+if graphrag.__doc__:
+    st.header("GraphRAG Module Documentation")
+    st.write(graphrag.__doc__)
+
+st.header("Next Steps")
+st.write("""
+Based on the information above, we need to determine:
+1. How to create a graph representation of text using graphrag.
+2. How to process this graph representation for analysis.
+3. Whether graphrag provides any built-in analysis tools or if we need to integrate it with other libraries.
+
+Please review the module contents and let me know which components seem most relevant for our text analysis task.
+""")