Update app.py

app.py

@@ -125,106 +125,106 @@
 
 # demo.launch()
 # app.py
-"""
-Gradio application entrypoint for Hugging Face Spaces.
-"""
-
-import os
-import tempfile
-import pandas as pd
-import gradio as gr
-from evaluation import evaluate_dataframe   # ✅ updated import
-from synthetic_data import generate_synthetic_dataset
-
-# Helper to save uploaded file
-def save_uploaded(file_obj):
-    if not file_obj:
-        return None
-    try:
-        return file_obj.name
-    except Exception:
-        data = file_obj.read()
-        suffix = ".csv" if file_obj.name.endswith(".csv") else ".json"
-        fd, tmp = tempfile.mkstemp(suffix=suffix)
-        with os.fdopen(fd, "wb") as f:
-            f.write(data)
-        return tmp
-
-def load_file_to_df(path):
-    if path is None:
-        return None
-    try:
-        if path.endswith(".csv"):
-            return pd.read_csv(path)
-        try:
-            return pd.read_json(path, lines=True)
-        except ValueError:
-            return pd.read_json(path)
-    except Exception as e:
-        raise e
-
-def run_evaluation(file_obj):
-    if file_obj is None:
-        df = generate_synthetic_dataset(num_agents=3, num_samples=12)
-    else:
-        path = save_uploaded(file_obj)
-        df = load_file_to_df(path)
-
-    if df is None:
-        return None, "No data loaded", None
-
-    # Normalize column names
-    cols = {c.lower(): c for c in df.columns}
-    rename_map = {}
-    for k in ["prompt", "response", "task", "agent", "reference"]:
-        if k not in cols:
-            if k == "reference":
-                for alt in ["answer", "ground_truth", "ref"]:
-                    if alt in cols:
-                        rename_map[cols[alt]] = k
-                        break
-            else:
-                for alt in [k, k.capitalize(), k.upper()]:
-                    if alt.lower() in cols:
-                        rename_map[cols[alt.lower()]] = k
-    if rename_map:
-        df = df.rename(columns=rename_map)
-
-    metrics_df, images, leaderboard = evaluate_dataframe(df)
-
-    gallery_items = [p for (p, caption) in images]
-    captions = [caption for (p, caption) in images]
-
-    out_csv = "/tmp/eval_results.csv"
-    metrics_df.to_csv(out_csv, index=False)
-
-    return (gallery_items, captions), metrics_df, leaderboard
-
-# Build Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# Agentic Evaluation Framework")
-    gr.Markdown(
-        "Upload a CSV/JSON/JSONL with columns: `prompt,response,task,agent,reference`. "
-        "If no file is uploaded, a synthetic demo will run."
-    )
-
-    with gr.Row():
-        file_input = gr.File(label="Upload CSV/JSON/JSONL", file_types=[".csv", ".json", ".jsonl"])
-        run_btn = gr.Button("Run Evaluation")
-        download_report = gr.File(label="Download CSV Report")
-
-    gallery = gr.Gallery(label="Visualization Outputs", columns=2, height="auto")
-    table = gr.Dataframe(headers=None, label="Per-example Metrics (detailed)")
-    leaderboard = gr.Dataframe(headers=None, label="Leaderboard (Avg Score per Agent & Task)")
-
-    def on_run(file_in):
-        (gallery_items, captions), metrics_df, lb = run_evaluation(file_in)
-        gallery_display = [(p, captions[i] if i < len(captions) else "") for i, p in enumerate(gallery_items)]
-        return gallery_display, metrics_df, lb
-
-    run_btn.click(fn=on_run, inputs=[file_input], outputs=[gallery, table, leaderboard])
-
-    gr.Markdown("## Tips\n- Columns: `prompt,response,task,agent,reference` (case-insensitive). "
-                "- `reference` optional.\n- Download CSV report after evaluation.")
-
-demo.launch()
+# """
+# Gradio application entrypoint for Hugging Face Spaces.
+# """
+
+# import os
+# import tempfile
+# import pandas as pd
+# import gradio as gr
+# from evaluation import evaluate_dataframe   # ✅ updated import
+# from synthetic_data import generate_synthetic_dataset
+
+# # Helper to save uploaded file
+# def save_uploaded(file_obj):
+#     if not file_obj:
+#         return None
+#     try:
+#         return file_obj.name
+#     except Exception:
+#         data = file_obj.read()
+#         suffix = ".csv" if file_obj.name.endswith(".csv") else ".json"
+#         fd, tmp = tempfile.mkstemp(suffix=suffix)
+#         with os.fdopen(fd, "wb") as f:
+#             f.write(data)
+#         return tmp
+
+# def load_file_to_df(path):
+#     if path is None:
+#         return None
+#     try:
+#         if path.endswith(".csv"):
+#             return pd.read_csv(path)
+#         try:
+#             return pd.read_json(path, lines=True)
+#         except ValueError:
+#             return pd.read_json(path)
+#     except Exception as e:
+#         raise e
+
+# def run_evaluation(file_obj):
+#     if file_obj is None:
+#         df = generate_synthetic_dataset(num_agents=3, num_samples=12)
+#     else:
+#         path = save_uploaded(file_obj)
+#         df = load_file_to_df(path)
+
+#     if df is None:
+#         return None, "No data loaded", None
+
+#     # Normalize column names
+#     cols = {c.lower(): c for c in df.columns}
+#     rename_map = {}
+#     for k in ["prompt", "response", "task", "agent", "reference"]:
+#         if k not in cols:
+#             if k == "reference":
+#                 for alt in ["answer", "ground_truth", "ref"]:
+#                     if alt in cols:
+#                         rename_map[cols[alt]] = k
+#                         break
+#             else:
+#                 for alt in [k, k.capitalize(), k.upper()]:
+#                     if alt.lower() in cols:
+#                         rename_map[cols[alt.lower()]] = k
+#     if rename_map:
+#         df = df.rename(columns=rename_map)
+
+#     metrics_df, images, leaderboard = evaluate_dataframe(df)
+
+#     gallery_items = [p for (p, caption) in images]
+#     captions = [caption for (p, caption) in images]
+
+#     out_csv = "/tmp/eval_results.csv"
+#     metrics_df.to_csv(out_csv, index=False)
+
+#     return (gallery_items, captions), metrics_df, leaderboard
+
+# # Build Gradio UI
+# with gr.Blocks() as demo:
+#     gr.Markdown("# Agentic Evaluation Framework")
+#     gr.Markdown(
+#         "Upload a CSV/JSON/JSONL with columns: `prompt,response,task,agent,reference`. "
+#         "If no file is uploaded, a synthetic demo will run."
+#     )
+
+#     with gr.Row():
+#         file_input = gr.File(label="Upload CSV/JSON/JSONL", file_types=[".csv", ".json", ".jsonl"])
+#         run_btn = gr.Button("Run Evaluation")
+#         download_report = gr.File(label="Download CSV Report")
+
+#     gallery = gr.Gallery(label="Visualization Outputs", columns=2, height="auto")
+#     table = gr.Dataframe(headers=None, label="Per-example Metrics (detailed)")
+#     leaderboard = gr.Dataframe(headers=None, label="Leaderboard (Avg Score per Agent & Task)")
+
+#     def on_run(file_in):
+#         (gallery_items, captions), metrics_df, lb = run_evaluation(file_in)
+#         gallery_display = [(p, captions[i] if i < len(captions) else "") for i, p in enumerate(gallery_items)]
+#         return gallery_display, metrics_df, lb
+
+#     run_btn.click(fn=on_run, inputs=[file_input], outputs=[gallery, table, leaderboard])
+
+#     gr.Markdown("## Tips\n- Columns: `prompt,response,task,agent,reference` (case-insensitive). "
+#                 "- `reference` optional.\n- Download CSV report after evaluation.")
+
+# demo.launch()