Update pages/21_GraphRag.py

pages/21_GraphRag.py

@@ -4,21 +4,45 @@ import torch
 import networkx as nx
 import matplotlib.pyplot as plt
 from collections import Counter
-import graphrag  # Import the graphrag library
+import graphrag
+import inspect
+
+st.title("GraphRAG Module Exploration and Text Analysis")
+
+# Diagnostic section
+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: You may need to adjust these parameters based on GraphRAG's actual interface
-    graph_rag_model = graphrag.GraphRAG(
+    # 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
-        num_hidden_layers=2,
-        hidden_size=768,
-        intermediate_size=3072,
+        # Add or remove parameters based on the actual model's requirements
     )
     
     return tokenizer, graph_rag_model
@@ -49,7 +73,7 @@ def analyze_text(text, tokenizer, model):
     graph = text_to_graph(text)
     
     # Combine tokenized input with graph representation
-    # Note: You may need to adjust this based on GraphRAG's actual input requirements
+    # 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"],
@@ -64,7 +88,7 @@ def analyze_text(text, tokenizer, model):
         outputs = model(**combined_input)
     
     # Process outputs
-    # Note: Adjust this based on GraphRAG's actual output format
+    # 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"
@@ -72,38 +96,4 @@ def analyze_text(text, tokenizer, model):
     
     return sentiment, confidence, graph
 
-st.title("GraphRAG-based Text Analysis")
-
-tokenizer, model = load_model()
-
-text_input = st.text_area("Enter text for analysis:", height=200)
-
-if st.button("Analyze Text"):
-    if text_input:
-        sentiment, confidence, graph = analyze_text(text_input, tokenizer, model)
-        st.write(f"Sentiment: {sentiment}")
-        st.write(f"Confidence: {confidence:.2f}")
-        
-        # Additional analysis
-        word_count = len(text_input.split())
-        st.write(f"Word count: {word_count}")
-        
-        # Most common words
-        words = [word.lower() for word in text_input.split() if word.isalnum()]
-        word_freq = Counter(words).most_common(5)
-        
-        st.write("Top 5 most common words:")
-        for word, freq in word_freq:
-            st.write(f"- {word}: {freq}")
-        
-        # Visualize graph
-        G = nx.Graph()
-        G.add_edges_from(zip(graph["edge_index"][0], graph["edge_index"][1]))
-        
-        plt.figure(figsize=(10, 6))
-        nx.draw(G, with_labels=False, node_size=30, node_color='lightblue', edge_color='gray')
-        plt.title("Text as Graph")
-        st.pyplot(plt)
-        
-    else:
-        st.write("Please enter some text to analyze.")
+# Rest of the Streamlit app (text input, analysis button, etc.) remains the same...