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.gradio/certificate.pem

@@ -0,0 +1,31 @@
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+-----END CERTIFICATE-----

.ipynb_checkpoints/ui-checkpoint.py

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+# paragraph_annotation_tool.py
+"""
+Paragraph-level annotation tool for rating two prompts from multiple LLMs.
+-------------------------------------------------------------------------
+* Shows Content_Paragraph (the context) on top.
+* Five models; for each model we display:
+      ┌──────────────┬─────────────┐
+      │ prompt-1 out │ prompt-2 out│
+      ├──────────────┼─────────────┤
+      │ A/B/C radio  │ A/B/C radio │
+      └──────────────┴─────────────┘
+* Model rows are shuffled per example and the permutation is stored
+  in column 'perm_models' so the order is stable across sessions.
+* Ratings are written to    rating_<model>__prompt1 / __prompt2
+* Back button always works; Save&Next is enabled only when every radio
+  has a value.  Tool resumes at the first example with any missing rating.
+"""
+
+import gradio as gr
+import pandas as pd
+import time, os, random
+from typing import List
+
+# ---------- CONFIG ----------
+CONTENT_COL   = "Content_Paragraph"
+PROMPT1_SUFFIX, PROMPT2_SUFFIX = "_prompt1", "_prompt2"
+PERM_COL      = "perm_models"
+RATING_OPTS   = ["A", "B", "C"]           # 3-level scale
+
+# ---------- LOAD CSV ----------
+csv_path = input("Enter CSV filename: ").strip()
+if not os.path.exists(csv_path):
+    raise FileNotFoundError(csv_path)
+
+df = pd.read_csv(csv_path, keep_default_na=False)
+TOTAL = len(df)
+
+if CONTENT_COL not in df.columns:
+    raise ValueError(f"Missing required column '{CONTENT_COL}' in CSV")
+
+# ---------- DISCOVER MODELS ----------
+models: List[str] = []
+for c in df.columns:
+    if c.endswith(PROMPT1_SUFFIX) and not c.startswith("rating_"):
+        m = c[:-len(PROMPT1_SUFFIX)]
+        if f"{m}{PROMPT2_SUFFIX}" not in df.columns:
+            raise ValueError(
+                f"Found '{c}' but no matching '{m}{PROMPT2_SUFFIX}'")
+        models.append(m)
+
+if len(models) < 1:
+    raise ValueError(f"No '*{PROMPT1_SUFFIX}' columns found")
+
+# ---------- ADD HELPER COLUMNS IF NEEDED ----------
+if PERM_COL not in df.columns:
+    df[PERM_COL] = ""
+
+for m in models:
+    for p in ("prompt1", "prompt2"):
+        rc = f"rating_{m}__{p}"
+        if rc not in df.columns:
+            df[rc] = ""
+
+for col in ("annotator", "annotation_time"):
+    if col not in df.columns:
+        df[col] = "" if col == "annotator" else 0.0
+
+# ---------- ANNOTATOR ----------
+annotator = input("Annotator name: ").strip()
+while not annotator:
+    annotator = input("Name cannot be empty – try again: ").strip()
+
+current_start: float | None = None   # per-example timer
+
+# ---------- UTILS ----------
+def first_incomplete() -> int:
+    for i, row in df.iterrows():
+        for m in models:
+            if row[f"rating_{m}__prompt1"] == "" or row[f"rating_{m}__prompt2"] == "":
+                return i
+    return 0
+
+def get_perm(idx: int) -> List[str]:
+    cell = str(df.at[idx, PERM_COL])
+    if not cell:
+        seq = models.copy(); random.shuffle(seq)
+        df.at[idx, PERM_COL] = "|".join(seq)
+        df.to_csv(csv_path, index=False)
+    return df.at[idx, PERM_COL].split("|")
+
+N_OUT = 2 * len(models)     # total textboxes / radios per example
+
+# ---------- BUILD ONE ROW ----------
+def build_row(idx: int):
+    global current_start
+    row = df.loc[idx]
+    order = get_perm(idx)
+    outputs, ratings = [], []
+
+    for m in order:
+        outputs.append(row[f"{m}{PROMPT1_SUFFIX}"])
+        outputs.append(row[f"{m}{PROMPT2_SUFFIX}"])
+        ratings.append(row[f"rating_{m}__prompt1"] or None)
+        ratings.append(row[f"rating_{m}__prompt2"] or None)
+
+    current_start = time.time()
+    ready = all(r in RATING_OPTS for r in ratings)
+    header = f"Example {idx + 1}/{TOTAL}"
+    return [idx, idx, header, row[CONTENT_COL]] + outputs + ratings + \
+           [gr.update(), gr.update(interactive=ready)]
+
+# ---------- SAVE ----------
+def save_row(idx: int, ratings: List[str]):
+    if not all(r in RATING_OPTS for r in ratings):
+        return                              # ignore partial rows
+    elapsed = time.time() - current_start if current_start else 0.0
+    p = 0
+    for m in get_perm(idx):
+        df.at[idx, f"rating_{m}__prompt1"] = ratings[p]; p += 1
+        df.at[idx, f"rating_{m}__prompt2"] = ratings[p]; p += 1
+    df.at[idx, "annotator"]       = annotator
+    df.at[idx, "annotation_time"] = float(elapsed)
+    df.to_csv(csv_path, index=False)
+
+# ---------- GRADIO UI ----------
+with gr.Blocks(title="Paragraph Annotation Tool") as demo:
+    state = gr.State(first_incomplete())
+
+    gr.Markdown("# Paragraph Annotation Tool")
+    gr.Markdown(f"**Annotator:** {annotator}")
+
+    idx_box  = gr.Number(label="Index", interactive=False)
+    hdr_box  = gr.Markdown()
+    para_box = gr.Textbox(label="Content Paragraph",
+                          interactive=False, lines=6)
+
+    # --- dynamic widgets for models ---
+    out_boxes, radio_widgets = [], []
+
+    for _ in models:                  # actual order varies per row
+        with gr.Row():
+            # prompt-1 column
+            with gr.Column():
+                out1 = gr.Textbox(interactive=False, lines=6)
+                rad1 = gr.Radio(RATING_OPTS, label="Rating (P1)")
+            # prompt-2 column
+            with gr.Column():
+                out2 = gr.Textbox(interactive=False, lines=6)
+                rad2 = gr.Radio(RATING_OPTS, label="Rating (P2)")
+            out_boxes.extend((out1, out2))
+            radio_widgets.extend((rad1, rad2))
+
+    back_btn = gr.Button("⟵ Back")
+    next_btn = gr.Button("Save & Next ⟶", interactive=False)
+
+    # ---- enable NEXT when complete ----
+    def toggle_next(*vals):
+        ready = all(v in RATING_OPTS for v in vals)
+        return gr.update(interactive=ready)
+
+    for r in radio_widgets:
+        r.change(toggle_next, inputs=radio_widgets, outputs=next_btn)
+
+    # ---- navigation callbacks ----
+    def goto(step: int):
+        def _fn(idx: int, *vals):
+            ratings = list(vals[:-1])               # last arg is next_btn state
+            if step != -1 or all(r in RATING_OPTS for r in ratings):
+                save_row(idx, ratings)
+            new_idx = max(0, min(idx + step, TOTAL - 1))
+            return build_row(new_idx)
+        return _fn
+
+    back_btn.click(goto(-1),
+                   inputs=[state] + radio_widgets + [next_btn],
+                   outputs=[state, idx_box, hdr_box, para_box] +
+                           out_boxes + radio_widgets + [back_btn, next_btn])
+
+    next_btn.click(goto(1),
+                   inputs=[state] + radio_widgets + [next_btn],
+                   outputs=[state, idx_box, hdr_box, para_box] +
+                           out_boxes + radio_widgets + [back_btn, next_btn])
+
+    demo.load(lambda: tuple(build_row(first_incomplete())), inputs=[],
+              outputs=[state, idx_box, hdr_box, para_box] +
+                      out_boxes + radio_widgets + [back_btn, next_btn])
+
+if __name__ == "__main__":
+    demo.queue()          # enables request queueing
+    demo.launch(share=True)
+

README.md

@@ -1,12 +1,6 @@
 ---
 title: CS
-emoji: 🐢
-colorFrom: green
-colorTo: gray
+app_file: ui.py
 sdk: gradio
-sdk_version: 5.33.0
-app_file: app.py
-pinned: false
+sdk_version: 5.23.3
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ui.py

@@ -0,0 +1,191 @@
+# paragraph_annotation_tool.py
+"""
+Paragraph-level annotation tool for rating two prompts from multiple LLMs.
+-------------------------------------------------------------------------
+* Shows Content_Paragraph (the context) on top.
+* Five models; for each model we display:
+      ┌──────────────┬─────────────┐
+      │ prompt-1 out │ prompt-2 out│
+      ├──────────────┼─────────────┤
+      │ A/B/C radio  │ A/B/C radio │
+      └──────────────┴─────────────┘
+* Model rows are shuffled per example and the permutation is stored
+  in column 'perm_models' so the order is stable across sessions.
+* Ratings are written to    rating_<model>__prompt1 / __prompt2
+* Back button always works; Save&Next is enabled only when every radio
+  has a value.  Tool resumes at the first example with any missing rating.
+"""
+
+import gradio as gr
+import pandas as pd
+import time, os, random
+from typing import List
+
+# ---------- CONFIG ----------
+CONTENT_COL   = "Content_Paragraph"
+PROMPT1_SUFFIX, PROMPT2_SUFFIX = "_prompt1", "_prompt2"
+PERM_COL      = "perm_models"
+RATING_OPTS   = ["A", "B", "C"]           # 3-level scale
+
+# ---------- LOAD CSV ----------
+csv_path = input("Enter CSV filename: ").strip()
+if not os.path.exists(csv_path):
+    raise FileNotFoundError(csv_path)
+
+df = pd.read_csv(csv_path, keep_default_na=False)
+TOTAL = len(df)
+
+if CONTENT_COL not in df.columns:
+    raise ValueError(f"Missing required column '{CONTENT_COL}' in CSV")
+
+# ---------- DISCOVER MODELS ----------
+models: List[str] = []
+for c in df.columns:
+    if c.endswith(PROMPT1_SUFFIX) and not c.startswith("rating_"):
+        m = c[:-len(PROMPT1_SUFFIX)]
+        if f"{m}{PROMPT2_SUFFIX}" not in df.columns:
+            raise ValueError(
+                f"Found '{c}' but no matching '{m}{PROMPT2_SUFFIX}'")
+        models.append(m)
+
+if len(models) < 1:
+    raise ValueError(f"No '*{PROMPT1_SUFFIX}' columns found")
+
+# ---------- ADD HELPER COLUMNS IF NEEDED ----------
+if PERM_COL not in df.columns:
+    df[PERM_COL] = ""
+
+for m in models:
+    for p in ("prompt1", "prompt2"):
+        rc = f"rating_{m}__{p}"
+        if rc not in df.columns:
+            df[rc] = ""
+
+for col in ("annotator", "annotation_time"):
+    if col not in df.columns:
+        df[col] = "" if col == "annotator" else 0.0
+
+# ---------- ANNOTATOR ----------
+annotator = input("Annotator name: ").strip()
+while not annotator:
+    annotator = input("Name cannot be empty – try again: ").strip()
+
+current_start: float | None = None   # per-example timer
+
+# ---------- UTILS ----------
+def first_incomplete() -> int:
+    for i, row in df.iterrows():
+        for m in models:
+            if row[f"rating_{m}__prompt1"] == "" or row[f"rating_{m}__prompt2"] == "":
+                return i
+    return 0
+
+def get_perm(idx: int) -> List[str]:
+    cell = str(df.at[idx, PERM_COL])
+    if not cell:
+        seq = models.copy(); random.shuffle(seq)
+        df.at[idx, PERM_COL] = "|".join(seq)
+        df.to_csv(csv_path, index=False)
+    return df.at[idx, PERM_COL].split("|")
+
+N_OUT = 2 * len(models)     # total textboxes / radios per example
+
+# ---------- BUILD ONE ROW ----------
+def build_row(idx: int):
+    global current_start
+    row = df.loc[idx]
+    order = get_perm(idx)
+    outputs, ratings = [], []
+
+    for m in order:
+        outputs.append(row[f"{m}{PROMPT1_SUFFIX}"])
+        outputs.append(row[f"{m}{PROMPT2_SUFFIX}"])
+        ratings.append(row[f"rating_{m}__prompt1"] or None)
+        ratings.append(row[f"rating_{m}__prompt2"] or None)
+
+    current_start = time.time()
+    ready = all(r in RATING_OPTS for r in ratings)
+    header = f"Example {idx + 1}/{TOTAL}"
+    return [idx, idx, header, row[CONTENT_COL]] + outputs + ratings + \
+           [gr.update(), gr.update(interactive=ready)]
+
+# ---------- SAVE ----------
+def save_row(idx: int, ratings: List[str]):
+    if not all(r in RATING_OPTS for r in ratings):
+        return                              # ignore partial rows
+    elapsed = time.time() - current_start if current_start else 0.0
+    p = 0
+    for m in get_perm(idx):
+        df.at[idx, f"rating_{m}__prompt1"] = ratings[p]; p += 1
+        df.at[idx, f"rating_{m}__prompt2"] = ratings[p]; p += 1
+    df.at[idx, "annotator"]       = annotator
+    df.at[idx, "annotation_time"] = float(elapsed)
+    df.to_csv(csv_path, index=False)
+
+# ---------- GRADIO UI ----------
+with gr.Blocks(title="Paragraph Annotation Tool") as demo:
+    state = gr.State(first_incomplete())
+
+    gr.Markdown("# Paragraph Annotation Tool")
+    gr.Markdown(f"**Annotator:** {annotator}")
+
+    idx_box  = gr.Number(label="Index", interactive=False)
+    hdr_box  = gr.Markdown()
+    para_box = gr.Textbox(label="Content Paragraph",
+                          interactive=False, lines=6)
+
+    # --- dynamic widgets for models ---
+    out_boxes, radio_widgets = [], []
+
+    for _ in models:                  # actual order varies per row
+        with gr.Row():
+            # prompt-1 column
+            with gr.Column():
+                out1 = gr.Textbox(interactive=False, lines=6)
+                rad1 = gr.Radio(RATING_OPTS, label="Rating (P1)")
+            # prompt-2 column
+            with gr.Column():
+                out2 = gr.Textbox(interactive=False, lines=6)
+                rad2 = gr.Radio(RATING_OPTS, label="Rating (P2)")
+            out_boxes.extend((out1, out2))
+            radio_widgets.extend((rad1, rad2))
+
+    back_btn = gr.Button("⟵ Back")
+    next_btn = gr.Button("Save & Next ⟶", interactive=False)
+
+    # ---- enable NEXT when complete ----
+    def toggle_next(*vals):
+        ready = all(v in RATING_OPTS for v in vals)
+        return gr.update(interactive=ready)
+
+    for r in radio_widgets:
+        r.change(toggle_next, inputs=radio_widgets, outputs=next_btn)
+
+    # ---- navigation callbacks ----
+    def goto(step: int):
+        def _fn(idx: int, *vals):
+            ratings = list(vals[:-1])               # last arg is next_btn state
+            if step != -1 or all(r in RATING_OPTS for r in ratings):
+                save_row(idx, ratings)
+            new_idx = max(0, min(idx + step, TOTAL - 1))
+            return build_row(new_idx)
+        return _fn
+
+    back_btn.click(goto(-1),
+                   inputs=[state] + radio_widgets + [next_btn],
+                   outputs=[state, idx_box, hdr_box, para_box] +
+                           out_boxes + radio_widgets + [back_btn, next_btn])
+
+    next_btn.click(goto(1),
+                   inputs=[state] + radio_widgets + [next_btn],
+                   outputs=[state, idx_box, hdr_box, para_box] +
+                           out_boxes + radio_widgets + [back_btn, next_btn])
+
+    demo.load(lambda: tuple(build_row(first_incomplete())), inputs=[],
+              outputs=[state, idx_box, hdr_box, para_box] +
+                      out_boxes + radio_widgets + [back_btn, next_btn])
+
+if __name__ == "__main__":
+    demo.queue()          # enables request queueing
+    demo.launch(share=True)
+