updated the gradio ui

app.py

@@ -1,25 +1,54 @@
 import os
 import gradio as gr
-from Oracle.deepfundingoracle import prepare_dataset, train_predict_weight, create_submission_csv
+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
 
-# Gradio-only deployment entrypoint for Hugging Face Spaces
-def analyze_file(upload):
-    # upload is a file-like object with .name
-    df = prepare_dataset(upload.name)
-    df = train_predict_weight(df)
-    csv_path = create_submission_csv(df, "submission.csv")
-    preview = df.head().to_csv(index=False)
-    return preview, csv_path
+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=analyze_file,
-    inputs=gr.File(label="Upload CSV", type="file"),
+    fn=pipeline,
+    inputs=gr.File(label="Upload CSV", type="filepath"),
     outputs=[
-        gr.Textbox(label="Preview of Results"),
-        gr.Textbox(label="Download CSV Path")
+        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 of repo-parent relationships; returns base and final weight predictions as CSV."
+    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__":

requirements.txt

@@ -11,4 +11,5 @@ smolagents
 huggingface_hub
 gradio
 fastapi
-uvicorn[standard]
+uvicorn[standard]
+networkx