Spaces:
Sleeping
Sleeping
| import os | |
| import gradio as gr | |
| import pandas as pd | |
| import networkx as nx | |
| import matplotlib.pyplot as plt | |
| from Oracle.deepfundingoracle import prepare_dataset, train_predict_weight, create_submission_csv, SmolLM | |
| from Oracle.DataSmolAgent import DataSmolAgent | |
| def pipeline(upload): | |
| # Load and clean/extract features via SmolAgents | |
| df_raw = pd.read_csv(upload.name) | |
| df_features = DataSmolAgent(df_raw).run( | |
| prompt="Clean and extract features from the uploaded data", output_csv=False) | |
| # Save preprocessed features and run dataset preparation on them | |
| processed_path = "processed_input.csv" | |
| pd.DataFrame(df_features).to_csv(processed_path, index=False) | |
| df_prepared = prepare_dataset(processed_path) | |
| # Assign base weights and predict final weights | |
| df_results = train_predict_weight(df_prepared) | |
| # Create submission CSV | |
| csv_path = create_submission_csv(df_results, "submission.csv") | |
| # Build dependency graph | |
| G = nx.DiGraph() | |
| for _, row in df_results.iterrows(): | |
| G.add_edge(row["parent"], row["repo"], weight=row["final_weight"]) | |
| plt.figure(figsize=(10, 8)) | |
| pos = nx.spring_layout(G) | |
| weights = [G[u][v]["weight"] for u, v in G.edges()] | |
| nx.draw(G, pos, with_labels=True, node_size=500, node_color="lightblue", | |
| edge_color=weights, edge_cmap=plt.get_cmap('viridis'), width=2) | |
| plt.savefig("graph.png") | |
| # Generate explanation via SmolLM | |
| explanation = SmolLM().predict( | |
| "Explain the dependency graph and weight assignments for the dataset.") | |
| # Return results | |
| return (df_results.head().to_dict("records"), csv_path, "graph.png", explanation) | |
| iface = gr.Interface( | |
| fn=pipeline, | |
| inputs=gr.File(label="Upload CSV", type="filepath"), | |
| outputs=[ | |
| gr.Dataframe(label="Preview of Results"), | |
| gr.File(label="Download CSV"), | |
| gr.Image(label="Dependency Graph"), | |
| gr.Textbox(label="Explanation") | |
| ], | |
| title="DeepFunding Oracle", | |
| description=( | |
| "Upload a CSV to extract features, assign base weights via LLama, predict final weights with RandomForest, " | |
| "and visualize the dependency graph with explanations." | |
| ) | |
| ) | |
| if __name__ == "__main__": | |
| port = int(os.environ.get("PORT", 7860)) | |
| iface.launch(server_name="0.0.0.0", server_port=port) |