move dataset

app.py

@@ -1,457 +1,3 @@
-# import gradio as gr
-# import pandas as pd
-# import os
-# from huggingface_hub import HfApi
-# from datasets import load_dataset, Dataset
-# import io
-# # from dotenv import load_dotenv
-
-# # # Load environment variables from a .env file (if present) and read HF token
-# # load_dotenv()
-# # HF_TOKEN = os.getenv("HF_TOKEN", "YOUR_HF_WRITE_TOKEN_HERE")
-
-# # --- 1. CONFIGURATION ---
-
-# # --- !!! NEW: DEBUG/TESTING MODE !!! ---
-# # Set to True to use local CSV files instead of Hugging Face Hub
-# # This will read from PREDICTIONS_CSV and read/write to LOCAL_DATASET_PATH
-# DEBUG_TESTING = False 
-# LOCAL_DATASET_PATH = "policy_evaluations.csv"
-# PREDICTIONS_CSV = "model_predictions.csv" # From batch_inference.py
-# # --- End Debug Config ---
-
-# HF = 'hf'
-# token = 'pQQADyqfDNewBCejvPmyMGlzpdgqDFSAFE'
-
-
-# HF_DATASET_REPO = "kaburia/policy-evaluations" # Your HF Dataset repo
-# HF_TOKEN = HF + '_' + token
-
-
-# # --- Email Authentication ---
-# APPROVED_EMAILS = {
-#     "kaburiaaustin1@tahmo.org": "user1",
-#     "E.Ramos@tudelft.nl" : "user2",
-#     "eunice.pramos@gmail.com" : "user3",
-#     "E.Abraham@tudelft.nl" : "user4",
-#     "dene.abv@gmail.com" : "user5",
-#     "rafatoufofana.abv@gmail.com" : "user6",
-#     "annorfrank@tahmo.org" : "user7",
-#     "n.marley@tahmo.org" : "user8",
-#     "H.F.Hagenaars@tudelft.nl" : "user9",
-# }
-
-# # --- Define Interaction Choices ---
-# DRILL_DOWN_MAP = {
-#     "coherent": ["+3 Indivisible", "+2 Reinforcing", "+1 Enabling"],
-#     "neutral": ["0 Consistent"],
-#     "incoherent": ["-1 Constraining", "-2 Counteracting", "-3 Cancelling"]
-# }
-# ALL_DRILL_DOWN_CHOICES = DRILL_DOWN_MAP["coherent"] + DRILL_DOWN_MAP["neutral"] + DRILL_DOWN_MAP["incoherent"]
-# VERIFY_CHOICES = ["neutral", "coherent", "incoherent"]
-
-# # --- 2. DATA LOADING FUNCTIONS ---
-
-# def load_data_from_hub(token):
-#     """
-#     (LIVE MODE) Loads the dataset from Hugging Face, converts to Pandas,
-#     and identifies pending rows.
-#     """
-#     if not token or token == "YOUR_HF_WRITE_TOKEN_HERE":
-#         return None, None, "Error: Hugging Face Token is not configured."
-        
-#     try:
-#         # Load the dataset (which may be policy_evaluations.csv)
-#         ds = load_dataset(HF_DATASET_REPO, token=token, split="train", cache_dir="./cache")
-#         full_df = ds.to_pandas()
-        
-#         # --- NEW LOGIC ---
-#         # Check for annotation columns and add them if they don't exist
-#         new_cols = ["UserVerifiedClass", "DrillDownInteraction", "AnnotatorUsername"]
-#         for col in new_cols:
-#             if col not in full_df.columns:
-#                 print(f"Adding missing column to DataFrame: {col}")
-#                 full_df[col] = pd.NA
-#         # --- END NEW LOGIC ---
-
-#         # Create a unique key
-#         full_df['key'] = full_df['PolicyA'].astype(str) + '||' + full_df['PolicyB'].astype(str)
-
-#         # Find rows that have NOT been annotated
-#         pending_df = full_df[full_df['UserVerifiedClass'].isnull()].reset_index(drop=True)
-        
-#         status = f"Loaded {len(pending_df)} remaining items to annotate. ({len(full_df) - len(pending_df)} already complete) [LIVE: HF Hub]"
-#         return full_df, pending_df, status
-        
-#     except Exception as e:
-#         return None, None, f"Error loading dataset from Hub: {e}"
-
-# def load_data_from_local():
-#     """
-#     (DEBUG MODE) Loads the dataset from a local CSV file.
-#     If it doesn't exist, it initializes it from 'model_predictions.csv'.
-#     """
-#     try:
-#         if not os.path.exists(LOCAL_DATASET_PATH):
-#             # First run: Initialize local file from predictions
-#             print(f"'{LOCAL_DATASET_PATH}' not found. Initializing from '{PREDICTIONS_CSV}'...")
-#             if not os.path.exists(PREDICTIONS_CSV):
-#                 return None, None, f"Error: '{PREDICTIONS_CSV}' not found. Please run batch_inference.py first."
-            
-#             df = pd.read_csv(PREDICTIONS_CSV)
-#             # --- FIX: Check for 'model_label' ---
-#             if "model_label" not in df.columns:
-#                  return None, None, f"Error: '{PREDICTIONS_CSV}' is missing 'model_label' column. Please run batch_inference.py"
-#             # --- END FIX ---
-#             df["UserVerifiedClass"] = pd.NA
-#             df["DrillDownInteraction"] = pd.NA
-#             df["AnnotatorUsername"] = pd.NA
-#             df.to_csv(LOCAL_DATASET_PATH, index=False)
-#             print(f"Initialized '{LOCAL_DATASET_PATH}'.")
-
-#         # Load the (now existing) local file
-#         full_df = pd.read_csv(LOCAL_DATASET_PATH)
-        
-#         # Ensure columns are present (for existing local files)
-#         new_cols = ["UserVerifiedClass", "DrillDownInteraction", "AnnotatorUsername"]
-#         for col in new_cols:
-#             if col not in full_df.columns:
-#                 full_df[col] = pd.NA
-                
-#         full_df['key'] = full_df['PolicyA'].astype(str) + '||' + full_df['PolicyB'].astype(str)
-#         pending_df = full_df[full_df['UserVerifiedClass'].isnull()].reset_index(drop=True)
-        
-#         status = f"Loaded {len(pending_df)} remaining items to annotate. ({len(full_df) - len(pending_df)} complete) [DEBUG: Local CSV]"
-#         return full_df, pending_df, status
-    
-#     except Exception as e:
-#         return None, None, f"Error loading local dataset: {e}"
-
-# # --- 3. DATA SAVING FUNCTIONS ---
-
-# def save_annotation_to_hub(index, verified_class, drill_down, user_tag, token, full_df, pending_df):
-#     """
-#     (LIVE MODE) Updates the DataFrame and pushes the entire dataset back to the Hub.
-#     """
-#     if not drill_down:
-#         return {status_box: "Error: Please select a drill-down interaction."}
-#     if not user_tag:
-#         return {status_box: "Error: User tag is missing. Please re-login."}
-
-#     try:
-#         # 1. Get the unique key of the item we just annotated
-#         current_key = pending_df.loc[index, 'key']
-        
-#         # 2. Update the *full* DataFrame with the annotation and user_tag
-#         full_df.loc[full_df['key'] == current_key, 'UserVerifiedClass'] = verified_class
-#         full_df.loc[full_df['key'] == current_key, 'DrillDownInteraction'] = drill_down
-#         full_df.loc[full_df['key'] == current_key, 'AnnotatorUsername'] = user_tag 
-
-#         # --- NEW SAVE LOGIC ---
-#         # 3. Convert back to CSV format in memory
-#         csv_buffer = io.StringIO()
-#         # Drop the temporary 'key' column before saving
-#         full_df.drop(columns=['key']).to_csv(csv_buffer, index=False) 
-#         csv_content_bytes = csv_buffer.getvalue().encode('utf-8')
-        
-#         # 4. Upload using HfApi to overwrite the specific file
-#         api = HfApi()
-#         api.upload_file(
-#             path_or_fileobj=io.BytesIO(csv_content_bytes),
-#             path_in_repo="policy_evaluations.csv", # Explicitly overwrite this file
-#             repo_id=HF_DATASET_REPO,
-#             token=token,
-#             repo_type="dataset"
-#         )
-#         # --- END NEW SAVE LOGIC ---
-        
-#         save_status = f"Saved to Hub: {verified_class} | {drill_down} by {user_tag}"
-        
-#         # 5. Load the next item
-#         next_index = index + 1
-#         ui_updates = load_next_item(pending_df, next_index) # Pass pending_df
-#         ui_updates[status_box] = save_status
-#         ui_updates[full_df_state] = full_df # Store the updated full_df in state
-#         return ui_updates
-
-#     except Exception as e:
-#         return {status_box: f"Error saving to Hub: {e}"}
-
-# def save_annotation_to_local(index, verified_class, drill_down, user_tag, full_df, pending_df):
-#     """
-#     (DEBUG MODE) Updates the DataFrame and saves it back to the local CSV.
-#     """
-#     if not drill_down:
-#         return {status_box: "Error: Please select a drill-down interaction."}
-#     if not user_tag:
-#         return {status_box: "Error: User tag is missing. Please re-login."}
-
-#     try:
-#         # 1. Get key
-#         current_key = pending_df.loc[index, 'key']
-        
-#         # 2. Update full DataFrame
-#         full_df.loc[full_df['key'] == current_key, 'UserVerifiedClass'] = verified_class
-#         full_df.loc[full_df['key'] == current_key, 'DrillDownInteraction'] = drill_down
-#         full_df.loc[full_df['key'] == current_key, 'AnnotatorUsername'] = user_tag
-
-#         # 3. Save to local CSV (overwriting)
-#         full_df.drop(columns=['key']).to_csv(LOCAL_DATASET_PATH, index=False)
-        
-#         save_status = f"Saved (Local): {verified_class} | {drill_down} by {user_tag}"
-        
-#         # 4. Load next item
-#         next_index = index + 1
-#         ui_updates = load_next_item(pending_df, next_index)
-#         ui_updates[status_box] = save_status
-#         ui_updates[full_df_state] = full_df # Store updated df in state
-#         return ui_updates
-
-#     except Exception as e:
-#         return {status_box: f"Error saving locally: {e}"}
-
-# # --- 4. GRADIO UI ---
-# with gr.Blocks(theme=gr.themes.Soft()) as demo:
-#     gr.Markdown("# Policy Coherence Annotation Tool")
-#     gr.Markdown(
-#         """
-#         Welcome! This tool is for human-in-the-loop annotation. 
-#         1.  Log in with your authorized email.
-#         2.  The model's prediction for two policies will be shown.
-#         3.  **Step 1:** Verify if the model's 3-class prediction (neutral, coherent, incoherent) is correct, or change it.
-#         4.  **Step 2:** Based on your verified choice, select a 7-class drill-down label.
-#         5.  Click 'Save & Next' to submit your annotation and load the next item.
-        
-#         ---
-#         ### Drill-Down Definitions
-#         - **+3 Indivisible**: Inextricably linked to the achievement of another goal.
-#         - **+2 Reinforcing**: Aids the achievement of another goal.
-#         - **+1 Enabling**: Creates conditions that further another goal.
-#         - **0 Consistent**: No significant positive or negative interactions.
-#         - **-1 Constraining**: Limits options on another goal.
-#         - **-2 Counteracting**: Clashes with another goal.
-#         - **-3 Cancelling**: Makes it impossible to reach another goal.
-#         """
-#     )
-
-#     # --- State variables ---
-#     full_df_state = gr.State()
-#     pending_df_state = gr.State()
-#     current_index_state = gr.State(value=0)
-#     hf_token_state = gr.State() 
-#     user_tag_state = gr.State()
-
-#     # --- Section 1: Login ---
-#     with gr.Group() as login_box: 
-#         with gr.Row():
-#             email_box = gr.Textbox(label="Email", placeholder="Enter your authorized email...")
-#         login_btn = gr.Button("Login & Load Dataset", variant="primary")
-#         progress_bar = gr.Markdown(value="Waiting for login...")
-
-#     # --- Section 2: Annotation (hidden until loaded) ---
-#     with gr.Group(visible=False) as annotation_box: 
-#         # --- MODIFIED: Use gr.Row for side-by-side table layout ---
-#         with gr.Row(): 
-#             policy_a_display = gr.Textbox(label="Policy / Objective A", interactive=False, lines=5, container=True)
-#             policy_b_display = gr.Textbox(label="Policy / Objective B", interactive=False, lines=5, container=True)
-#         # --- END MODIFICATION ---
-
-#         with gr.Row():
-#             model_confidence_label = gr.Label(label="Model Confidence")
-#             user_verified_radio = gr.Radio(
-#                 label="Step 1: Verify/Correct Classification",
-#                 choices=VERIFY_CHOICES,
-#                 info="The model's prediction is selected by default."
-#             )
-        
-#         # --- UPDATED: Markdown instructions moved to top ---
-        
-#         user_drill_down_dropdown = gr.Dropdown(
-#             label="Step 2: Drill-Down Interaction", 
-#             choices=[], # Will be populated dynamically
-#             interactive=True
-#         )
-        
-#         save_btn = gr.Button("Save & Next", variant="stop")
-#         status_box = gr.Textbox(label="Status", interactive=False)
-
-#     # --- 5. UI Event Handlers ---
-    
-#     def update_drill_down_choices(verified_class):
-#         """
-#         Updates the drill-down dropdown based on the 3-class selection.
-#         """
-#         choices = DRILL_DOWN_MAP.get(verified_class, [])
-#         value = choices[0] if len(choices) == 1 else None # Auto-select "0 Consistent"
-#         # --- FIX: Return the constructor (Gradio 4.x syntax) ---
-#         return gr.Dropdown(
-#             choices=choices, 
-#             value=value, 
-#             interactive=len(choices) > 1 # Disable interaction if only one choice
-#         )
-
-#     def load_next_item(pending_df, index):
-#         """
-#         Loads the item at 'index' from the PENDING DataFrame into the UI.
-#         """
-#         if pending_df is None:
-#             return {status_box: "Data not loaded."}
-            
-#         total_items = len(pending_df)
-#         if index >= total_items:
-#             return {
-#                 progress_bar: gr.Markdown(f"**Annotation Complete! ({total_items} items total)**"),
-#                 policy_a_display: "All items annotated.",
-#                 policy_b_display: "",
-#                 annotation_box: gr.Group(visible=False) 
-#             }
-        
-#         row = pending_df.iloc[index]
-#         # --- FIX: Use "model_label" from CSV ---
-#         model_pred = row["model_label"]
-        
-#         # --- NEW: Build conf_dict conditionally ---
-#         if "model_confidence" in row:
-#             # New format: "model_label" + "model_confidence"
-#             confidence = row["model_confidence"]
-#             conf_dict = {}
-            
-#             # Distribute probability
-#             remaining_prob = (1.0 - confidence) / 2.0
-#             for l in VERIFY_CHOICES: # ["neutral", "coherent", "incoherent"]
-#                 if l == model_pred:
-#                     conf_dict[l] = confidence
-#                 else:
-#                     conf_dict[l] = remaining_prob
-#         else:
-#             # Old format: "Confidence_Neutral", etc.
-#             conf_dict = {
-#                 "neutral": row.get("Confidence_Neutral", 0.0), 
-#                 "coherent": row.get("Confidence_Coherent", 0.0),
-#                 "incoherent": row.get("Confidence_Incoherent", 0.0)
-#             }
-#         # --- END NEW ---
-        
-#         # --- NEW: Update drill-down based on model_pred ---
-#         drill_down_choices = DRILL_DOWN_MAP.get(model_pred, [])
-#         drill_down_value = drill_down_choices[0] if len(drill_down_choices) == 1 else None
-#         drill_down_interactive = len(drill_down_choices) > 1
-        
-#         return {
-#             progress_bar: gr.Markdown(f"**Annotating Item {index + 1} of {total_items}**"),
-#             policy_a_display: row["PolicyA"],
-#             policy_b_display: row["PolicyB"],
-#             model_confidence_label: conf_dict,
-#             user_verified_radio: model_pred,
-#             # --- FIX: Return the constructor (Gradio 4.x syntax) ---
-#             user_drill_down_dropdown: gr.Dropdown(
-#                 choices=drill_down_choices, 
-#                 value=drill_down_value, 
-#                 interactive=drill_down_interactive
-#             ),
-#             current_index_state: index,
-#             annotation_box: gr.Group(visible=True) 
-#         }
-    
-#     # When 'Login' is clicked:
-#     def login_and_load(email):
-#         # --- Authentication Step ---
-#         if email not in APPROVED_EMAILS:
-#             return {
-#                 progress_bar: gr.Markdown(f"<font color='red'>Error: Email '{email}' is not authorized.</font>"),
-#                 login_box: gr.Group(visible=True) 
-#             }
-        
-#         user_tag = APPROVED_EMAILS[email] # Get the tag (e.g., "user1")
-        
-#         # --- NEW: Branching Logic for Debug/Live ---
-#         if DEBUG_TESTING:
-#             print("--- DEBUG MODE: Loading from local CSV ---")
-#             full_df, pending_df, status = load_data_from_local()
-#             token_to_store = "debug_mode" # Placeholder
-#         else:
-#             print("--- LIVE MODE: Loading from Hugging Face Hub ---")
-#             if HF_TOKEN == "YOUR_HF_WRITE_TOKEN_HERE" or not HF_TOKEN:
-#                  return {
-#                     progress_bar: gr.Markdown(f"<font color='red'>Error: App is not configured. HF_TOKEN is missing.</font>"),
-#                     login_box: gr.Group(visible=True) 
-#                 }
-#             full_df, pending_df, status = load_data_from_hub(HF_TOKEN)
-#             token_to_store = HF_TOKEN
-
-#         # --- Common Logic ---
-#         if full_df is None:
-#             return {
-#                 progress_bar: gr.Markdown(f"<font color='red'>{status}</font>"),
-#                 login_box: gr.Group(visible=True) 
-#             }
-        
-#         # --- Load the first item ---
-#         first_item_updates = load_next_item(pending_df, 0)
-        
-#         # --- Save all data to state and update UI ---
-#         first_item_updates[full_df_state] = full_df
-#         first_item_updates[pending_df_state] = pending_df
-#         first_item_updates[progress_bar] = f"Login successful as **{user_tag}**. {status}"
-#         first_item_updates[hf_token_state] = token_to_store # Save token/debug_flag to state
-#         first_item_updates[user_tag_state] = user_tag 
-#         first_item_updates[login_box] = gr.Group(visible=False) # Hide login box
-#         first_item_updates[annotation_box] = gr.Group(visible=True) # Show annotation box
-#         return first_item_updates
-
-#     login_btn.click(
-#         fn=login_and_load,
-#         inputs=[email_box], # Input is ONLY the email box
-#         outputs=[
-#             progress_bar, policy_a_display, policy_b_display,
-#             model_confidence_label, user_verified_radio, user_drill_down_dropdown,
-#             current_index_state, annotation_box, login_box,
-#             full_df_state, pending_df_state, hf_token_state, user_tag_state, status_box
-#         ]
-#     )
-    
-#     # --- NEW: Wrapper for Save Button ---
-#     def save_wrapper(index, verified_class, drill_down, user_tag, token, full_df, pending_df):
-#         if DEBUG_TESTING:
-#             return save_annotation_to_local(index, verified_class, drill_down, user_tag, full_df, pending_df)
-#         else:
-#             return save_annotation_to_hub(index, verified_class, drill_down, user_tag, token, full_df, pending_df)
-
-#     # --- NEW: Event listener for dynamic drill-down ---
-#     user_verified_radio.change(
-#         fn=update_drill_down_choices,
-#         inputs=user_verified_radio,
-#         outputs=user_drill_down_dropdown
-#     )
-
-#     # When 'Save & Next' is clicked
-#     save_btn.click(
-#         fn=save_wrapper, # Call the new wrapper function
-#         inputs=[
-#             current_index_state,
-#             user_verified_radio,
-#             user_drill_down_dropdown,
-#             user_tag_state, # Pass the user tag from state
-#             hf_token_state, # Pass the token from state
-#             full_df_state,
-#             pending_df_state
-#         ],
-#         outputs=[
-#             progress_bar, policy_a_display, policy_b_display,
-#             model_confidence_label, user_verified_radio, user_drill_down_dropdown,
-#             current_index_state, annotation_box, status_box, full_df_state
-#         ]
-#     )
-
-# if __name__ == "__main__":
-#     if DEBUG_TESTING:
-#         print("\n" + "="*30)
-#         print("--- RUNNING IN DEBUG MODE ---")
-#         print(f"--- Data will be read/written to '{LOCAL_DATASET_PATH}' ---")
-#         print("="*30 + "\n")
-#     elif HF_TOKEN == "YOUR_HF_WRITE_TOKEN_HERE":
-#         print("\n--- WARNING: HF_TOKEN NOT SET ---")
-#         print("Please edit 'annotation_app.py' and add your HF_TOKEN to the top.")
 import gradio as gr
 import pandas as pd
 import os
@@ -467,8 +13,8 @@ LOCAL_DATASET_PATH = "policy_evaluations.csv"
 PREDICTIONS_CSV = "model_predictions.csv" 
 
 HF = 'hf'
-token = 'pQQADyqfDNewBCejvPmyMGlzpdgqDFSAFE'
-HF_DATASET_REPO = "kaburia/policy-evaluations" 
+token = 'GbeqFrdNnENcHiJtUnTKcAbVkneXrlOkHb'
+HF_DATASET_REPO = "akaburia/policy-evaluations" 
 HF_TOKEN = HF + '_' + token
 
 APPROVED_EMAILS = {
@@ -983,4 +529,4 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(debug=True, share=True)
+    demo.launch(debug=True)