Update app.py

app.py

@@ -200,83 +200,3 @@ def create_network_graph(df):
             node_colors.append('red')
         elif graph.nodes[node]['type'] == 'author':
             node_colors.append('blue')
-        else:
-            node_colors.append('green')
-
-    # Set node labels
-    node_labels = []
-    for node in graph.nodes():
-        node_labels.append(node)
-
-    node_trace.marker.color = node_colors
-    node_trace.text = node_labels
-
-    # Create the figure
-    fig = go.Figure(data=[edge_trace, node_trace],
-                   layout=go.Layout(
-                       title="GitHub Issue Network Graph",
-                       showlegend=False,
-                       hovermode='closest',
-                       margin=dict(b=20, l=5, r=5, t=40),
-                       xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-                       yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
-                   )
-                  )
-
-    # Display the figure in a Streamlit app
-    st.plotly_chart(fig)
-
-# Function to build a predictive model for issue resolution time
-def build_predictive_model(df):
-    # Feature engineering
-    df['created_at_day'] = df['created_at'].dt.day
-    df['created_at_weekday'] = df['created_at'].dt.weekday
-    df['created_at_hour'] = df['created_at'].dt.hour
-    df['author_encoded'] = df['author'].astype('category').cat.codes
-    df['assignee_encoded'] = df['assignee'].astype('category').cat.codes
-
-    # Select features and target variable
-    features = ['created_at_day', 'created_at_weekday', 'created_at_hour', 'author_encoded', 'assignee_encoded', 'sentiment']
-    target = 'resolution_time'
-
-    # Split data into training and testing sets
-    X_train, X_test, y_train, y_test = train_test_split(df[features], df[target], test_size=0.2, random_state=42)
-
-    # Create a pipeline for feature scaling and model training
-    pipeline = Pipeline([
-        ('scaler', StandardScaler()),
-        ('model', LogisticRegression())
-    ])
-
-    # Train the model
-    pipeline.fit(X_train, y_train)
-
-    # Evaluate the model
-    y_pred = pipeline.predict(X_test)
-    accuracy = accuracy_score(y_test, y_pred)
-    st.write("Accuracy:", accuracy)
-    st.write(classification_report(y_test, y_pred))
-
-# Main function
-if __name__ == "__main__":
-    # Replace with your GitHub username and repository name
-    username = "Ig0tU"
-    repository = "miagiii"
-
-    # Fetch issue data from GitHub
-    issues_data = fetch_issue_data(username, repository, 1, 10)
-
-    # Clean and structure the data
-    df = clean_and_structure_data(issues_data)
-
-    # Perform exploratory data analysis (EDA)
-    perform_eda(df)
-
-    # Analyze text content using NLP
-    analyze_text_content(df)
-
-    # Create a network graph of issues, authors, and assignees
-    create_network_graph(df)
-
-    # Build a predictive model for issue resolution time
-    build_predictive_model(df)