Update app.py

app.py

@@ -1,7 +1,111 @@
+
+import pandas as pd
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def load_csv(file_obj):
+    if file_obj is None:
+        return None, "Upload a judged CSV to begin."
+    try:
+        df = pd.read_csv(file_obj.name)
+        return df, ""
+    except Exception as e:
+        return None, f"Failed to read CSV: {e}"
+
+
+def filter_rows(file_obj, mode, similar, text_filter, max_rows):
+    df, err = load_csv(file_obj)
+    if err:
+        return err, "", None
+    if df is None or df.empty:
+        return "No data in CSV.", "", None
+
+    # Expected columns from judged-fast-accurate CSV
+    required = [
+        "frame_idx",
+        "t_sec",
+        "truth_text",
+        "fast_text",
+        "fast_similar",
+        "fast_score",
+        "fast_reason",
+        "accurate_text",
+        "accurate_similar",
+        "accurate_score",
+        "accurate_reason",
+    ]
+    missing = [c for c in required if c not in df.columns]
+    if missing:
+        return f"Missing columns: {missing}", "", None
+
+    df["fast_similar"] = df["fast_similar"].astype(str)
+    df["accurate_similar"] = df["accurate_similar"].astype(str)
+
+    if mode != "both":
+        if mode == "fast":
+            df = df[df["fast_text"].notna()]
+        else:
+            df = df[df["accurate_text"].notna()]
+
+    if similar != "all":
+        val = "True" if similar == "true" else "False"
+        df = df[(df["fast_similar"] == val) | (df["accurate_similar"] == val)]
+
+    if text_filter:
+        t = text_filter.lower()
+        cols = [
+            "truth_text",
+            "fast_text",
+            "fast_reason",
+            "accurate_text",
+            "accurate_reason",
+        ]
+        mask = df[cols].fillna("").apply(lambda x: x.str.lower().str.contains(t))
+        df = df[mask.any(axis=1)]
+
+    df = df.copy()
+    # Pretty display columns
+    df["fast"] = (
+        "similar=" + df["fast_similar"].fillna("")
+        + " score=" + df["fast_score"].fillna("").astype(str)
+        + " | " + df["fast_text"].fillna("")
+        + " | reason: " + df["fast_reason"].fillna("")
+    )
+    df["accurate"] = (
+        "similar=" + df["accurate_similar"].fillna("")
+        + " score=" + df["accurate_score"].fillna("").astype(str)
+        + " | " + df["accurate_text"].fillna("")
+        + " | reason: " + df["accurate_reason"].fillna("")
+    )
+    display_cols = ["frame_idx", "t_sec", "truth_text", "fast", "accurate"]
+
+    subset = df[display_cols].head(max_rows)
+    summary = (
+        f"Rows: {len(df)} | "
+        f"fast similar: {sum(df['fast_similar']=='True')} | "
+        f"accurate similar: {sum(df['accurate_similar']=='True')}"
+    )
+
+    return "", summary, subset
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# OCR Judge Viewer")
+    with gr.Row():
+        file_in = gr.File(label="judged-fast-accurate CSV", file_types=[".csv"])
+        mode = gr.Dropdown(["both", "fast", "accurate"], value="both", label="Mode")
+        similar = gr.Dropdown(["all", "true", "false"], value="all", label="similar flag")
+        text_filter = gr.Textbox(label="Search text", placeholder="search in truth/reasons")
+        max_rows = gr.Slider(10, 500, value=100, step=10, label="Max rows")
+    err_box = gr.Markdown()
+    summary_box = gr.Markdown()
+    table = gr.Dataframe(wrap=True)
+
+    file_in.change(filter_rows, [file_in, mode, similar, text_filter, max_rows], [err_box, summary_box, table])
+    mode.change(filter_rows, [file_in, mode, similar, text_filter, max_rows], [err_box, summary_box, table])
+    similar.change(filter_rows, [file_in, mode, similar, text_filter, max_rows], [err_box, summary_box, table])
+    text_filter.change(filter_rows, [file_in, mode, similar, text_filter, max_rows], [err_box, summary_box, table])
+    max_rows.change(filter_rows, [file_in, mode, similar, text_filter, max_rows], [err_box, summary_box, table])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)