Resolve merge conflicts with main

app.py

@@ -0,0 +1,450 @@
+"""
+app.py
+"""
+
+# Standard imports
+import json
+import os
+import sys
+import uuid
+import asyncio
+from datetime import datetime
+
+# Third party imports
+import openai
+import gradio as gr
+import gspread
+from google.oauth2 import service_account
+from transformers import AutoModel
+
+# Local imports
+from utils import get_embeddings
+
+# --- Categories
+CATEGORIES = {
+    "binary": ["binary"],
+    "hateful": ["hateful_l1", "hateful_l2"],
+    "insults": ["insults"],
+    "sexual": [
+        "sexual_l1",
+        "sexual_l2",
+    ],
+    "physical_violence": ["physical_violence"],
+    "self_harm": ["self_harm_l1", "self_harm_l2"],
+    "all_other_misconduct": [
+        "all_other_misconduct_l1",
+        "all_other_misconduct_l2",
+    ],
+}
+
+# --- OpenAI Setup ---
+# Create both sync and async clients
+client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+async_client = openai.AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# --- Model Loading ---
+def load_lionguard2():
+    model = AutoModel.from_pretrained("govtech/lionguard-2", trust_remote_code=True)
+    return model
+
+model = load_lionguard2()
+
+# --- Google Sheets Config ---
+GOOGLE_SHEET_URL = os.environ.get("GOOGLE_SHEET_URL")
+GOOGLE_CREDENTIALS = os.environ.get("GCP_SERVICE_ACCOUNT")
+RESULTS_SHEET_NAME = "results"
+VOTES_SHEET_NAME = "votes"
+CHATBOT_SHEET_NAME = "chatbot"
+
+def get_gspread_client():
+    credentials = service_account.Credentials.from_service_account_info(
+        json.loads(GOOGLE_CREDENTIALS),
+        scopes=[
+            "https://www.googleapis.com/auth/spreadsheets",
+            "https://www.googleapis.com/auth/drive",
+        ],
+    )
+    return gspread.authorize(credentials)
+
+def save_results_data(row):
+    try:
+        gc = get_gspread_client()
+        sheet = gc.open_by_url(GOOGLE_SHEET_URL)
+        ws = sheet.worksheet(RESULTS_SHEET_NAME)
+        ws.append_row(list(row.values()))
+    except Exception as e:
+        print(f"Error saving results data: {e}")
+
+def save_vote_data(text_id, agree):
+    try:
+        gc = get_gspread_client()
+        sheet = gc.open_by_url(GOOGLE_SHEET_URL)
+        ws = sheet.worksheet(VOTES_SHEET_NAME)
+        vote_row = {
+            "datetime": datetime.now().isoformat(),
+            "text_id": text_id,
+            "agree": agree
+        }
+        ws.append_row(list(vote_row.values()))
+    except Exception as e:
+        print(f"Error saving vote data: {e}")
+
+def log_chatbot_data(row):
+    try:
+        gc = get_gspread_client()
+        sheet = gc.open_by_url(GOOGLE_SHEET_URL)
+        ws = sheet.worksheet(CHATBOT_SHEET_NAME)
+        ws.append_row([
+            row["datetime"], row["text_id"], row["text"], row["binary_score"],
+            row["hateful_l1_score"], row["hateful_l2_score"], row["insults_score"],
+            row["sexual_l1_score"], row["sexual_l2_score"], row["physical_violence_score"],
+            row["self_harm_l1_score"], row["self_harm_l2_score"], row["aom_l1_score"],
+            row["aom_l2_score"], row["openai_score"]
+        ])
+    except Exception as e:
+        print(f"Error saving chatbot data: {e}")
+
+# --- Classifier logic ---
+
+def format_score_with_style(score_str):
+    if score_str == "-":
+        return '<span style="color: #9ca3af;">-</span>'
+    try:
+        score = float(score_str)
+        percentage = int(score * 100)
+        if score < 0.4:
+            return f'<span style="color: #34d399; font-weight:600;">👌 {percentage}%</span>'
+        elif 0.4 <= score < 0.7:
+            return f'<span style="color: #fbbf24; font-weight:600;">⚠️ {percentage}%</span>'
+        else:
+            return f'<span style="color: #fca5a5; font-weight:600;">🚨 {percentage}%</span>'
+    except:
+        return score_str
+
+def format_binary_score(score):
+    percentage = int(score * 100)
+    if score < 0.4:
+        return f'<div style="background:linear-gradient(135deg, #065f46 0%, #047857 100%); color:#34d399; padding:24px 0; border-radius:20px; text-align:center; font-weight:900; border:3px solid #10b981; font-size:24px; margin:24px 0; box-shadow:0 4px 24px rgba(0,0,0,0.3);">✅ Pass ({percentage}/100)</div>'
+    elif 0.4 <= score < 0.7:
+        return f'<div style="background:linear-gradient(135deg, #92400e 0%, #b45309 100%); color:#fbbf24; padding:24px 0; border-radius:20px; text-align:center; font-weight:900; border:3px solid #f59e0b; font-size:24px; margin:24px 0; box-shadow:0 4px 24px rgba(0,0,0,0.3);">⚠️ Warning ({percentage}/100)</div>'
+    else:
+        return f'<div style="background:linear-gradient(135deg, #991b1b 0%, #b91c1c 100%); color:#fca5a5; padding:24px 0; border-radius:20px; text-align:center; font-weight:900; border:3px solid #ef4444; font-size:24px; margin:24px 0; box-shadow:0 4px 24px rgba(0,0,0,0.3);">🚨 Fail ({percentage}/100)</div>'
+
+def analyze_text(text):
+    if not text.strip():
+        empty_html = '<div style="text-align: center; color: #9ca3af; padding: 30px; font-style: italic;">Enter text to analyze</div>'
+        return empty_html, empty_html, "", ""
+    try:
+        text_id = str(uuid.uuid4())
+        embeddings = get_embeddings([text])
+        results = model.predict(embeddings)
+        binary_score = results.get('binary', [0.0])[0]
+
+        main_categories = ['hateful', 'insults', 'sexual', 'physical_violence', 'self_harm', 'all_other_misconduct']
+        categories_html = []
+        max_scores = {}
+        for category in main_categories:
+            subcategories = CATEGORIES[category]
+            category_name = category.replace('_', ' ').title()
+            category_emojis = {
+                'Hateful': '🤬',
+                'Insults': '💢',
+                'Sexual': '🔞',
+                'Physical Violence': '⚔️',
+                'Self Harm': '☹️',
+                'All Other Misconduct': '🙅‍♀️'
+            }
+            category_display = f"{category_emojis.get(category_name, '📝')} {category_name}"
+            level_scores = [results.get(subcategory_key, [0.0])[0] for subcategory_key in subcategories]
+            max_score = max(level_scores) if level_scores else 0.0
+            max_scores[category] = max_score
+            categories_html.append(f'''
+            <tr>
+                <td>{category_display}</td>
+                <td style="text-align: center;">{format_score_with_style(f"{max_score:.4f}")}</td>
+            </tr>
+            ''')
+
+        html_table = f'''
+        <table style="width:100%">
+        <thead>
+        <tr><th>Category</th><th>Score</th></tr>
+        </thead>
+        <tbody>
+        {''.join(categories_html)}
+        </tbody>
+        </table>
+        '''
+
+        # Save to Google Sheets if enabled
+        if GOOGLE_SHEET_URL and GOOGLE_CREDENTIALS:
+            results_row = {
+                "datetime": datetime.now().isoformat(),
+                "text_id": text_id,
+                "text": text,
+                "binary_score": binary_score,
+            }
+            for category in main_categories:
+                results_row[f"{category}_max"] = max_scores[category]
+            save_results_data(results_row)
+
+        voting_html = '<div>Help improve LionGuard2! Rate the analysis below.</div>'
+        return format_binary_score(binary_score), html_table, text_id, voting_html
+
+    except Exception as e:
+        error_msg = f"Error analyzing text: {str(e)}"
+        return f'<div style="color: #fca5a5;">❌ {error_msg}</div>', '', '', ''
+
+def vote_thumbs_up(text_id):
+    if text_id and GOOGLE_SHEET_URL and GOOGLE_CREDENTIALS:
+        save_vote_data(text_id, True)
+        return '<div style="color: #34d399; font-weight:700;">🎉 Thank you!</div>'
+    return '<div>Voting not available or analysis not yet run.</div>'
+
+def vote_thumbs_down(text_id):
+    if text_id and GOOGLE_SHEET_URL and GOOGLE_CREDENTIALS:
+        save_vote_data(text_id, False)
+        return '<div style="color: #fca5a5; font-weight:700;">📝 Thanks for the feedback!</div>'
+    return '<div>Voting not available or analysis not yet run.</div>'
+
+# --- Guardrail Comparison logic (ASYNC VERSION) ---
+
+async def get_openai_response_async(message, system_prompt="You are a helpful assistant."):
+    """Async version of OpenAI API call"""
+    try:
+        response = await async_client.chat.completions.create(
+            model="gpt-4.1-nano",
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": message}
+            ],
+            max_tokens=500,
+            temperature=0,
+            seed=42,
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error: {str(e)}. Please check your OpenAI API key."
+
+async def openai_moderation_async(message):
+    """Async version of OpenAI moderation"""
+    try:
+        response = await async_client.moderations.create(input=message)
+        return response.results[0].flagged
+    except Exception as e:
+        print(f"Error in OpenAI moderation: {e}")
+        return False
+
+def lionguard_2_sync(message, threshold=0.5):
+    """LionGuard remains sync as it's using a local model"""
+    try:
+        embeddings = get_embeddings([message])
+        results = model.predict(embeddings)
+        binary_prob = results['binary'][0]
+        return binary_prob > threshold, binary_prob
+    except Exception as e:
+        print(f"Error in LionGuard 2: {e}")
+        return False, 0.0
+
+async def process_no_moderation(message, history_no_mod):
+    """Process message without moderation"""
+    no_mod_response = await get_openai_response_async(message)
+    history_no_mod.append({"role": "user", "content": message})
+    history_no_mod.append({"role": "assistant", "content": no_mod_response})
+    return history_no_mod
+
+async def process_openai_moderation(message, history_openai):
+    """Process message with OpenAI moderation"""
+    openai_flagged = await openai_moderation_async(message)
+    history_openai.append({"role": "user", "content": message})
+    if openai_flagged:
+        openai_response = "🚫 This message has been flagged by OpenAI moderation"
+        history_openai.append({"role": "assistant", "content": openai_response})
+    else:
+        openai_response = await get_openai_response_async(message)
+        history_openai.append({"role": "assistant", "content": openai_response})
+    return history_openai
+
+async def process_lionguard(message, history_lg):
+    """Process message with LionGuard 2"""
+    # Run LionGuard sync check in thread pool to not block
+    loop = asyncio.get_event_loop()
+    lg_flagged, lg_score = await loop.run_in_executor(None, lionguard_2_sync, message, 0.5)
+    
+    history_lg.append({"role": "user", "content": message})
+    if lg_flagged:
+        lg_response = "🚫 This message has been flagged by LionGuard 2"
+        history_lg.append({"role": "assistant", "content": lg_response})
+    else:
+        lg_response = await get_openai_response_async(message)
+        history_lg.append({"role": "assistant", "content": lg_response})
+    return history_lg, lg_score
+
+async def process_message_async(message, history_no_mod, history_openai, history_lg):
+    """Process message concurrently across all three guardrails"""
+    if not message.strip():
+        return history_no_mod, history_openai, history_lg, ""
+    
+    # Run all three processes concurrently using asyncio.gather
+    results = await asyncio.gather(
+        process_no_moderation(message, history_no_mod),
+        process_openai_moderation(message, history_openai),
+        process_lionguard(message, history_lg),
+        return_exceptions=True  # Continue even if one fails
+    )
+    
+    # Unpack results
+    history_no_mod = results[0] if not isinstance(results[0], Exception) else history_no_mod
+    history_openai = results[1] if not isinstance(results[1], Exception) else history_openai
+    history_lg_result = results[2] if not isinstance(results[2], Exception) else (history_lg, 0.0)
+    history_lg = history_lg_result[0]
+    lg_score = history_lg_result[1] if isinstance(history_lg_result, tuple) else 0.0
+
+    # --- Logging for chatbot worksheet (runs in background) ---
+    if GOOGLE_SHEET_URL and GOOGLE_CREDENTIALS:
+        try:
+            loop = asyncio.get_event_loop()
+            # Run logging in thread pool so it doesn't block
+            loop.run_in_executor(None, _log_chatbot_sync, message, lg_score)
+        except Exception as e:
+            print(f"Chatbot logging failed: {e}")
+
+    return history_no_mod, history_openai, history_lg, ""
+
+def _log_chatbot_sync(message, lg_score):
+    """Sync helper for logging - runs in thread pool"""
+    try:
+        embeddings = get_embeddings([message])
+        results = model.predict(embeddings)
+        now = datetime.now().isoformat()
+        text_id = str(uuid.uuid4())
+        row = {
+            "datetime": now,
+            "text_id": text_id,
+            "text": message,
+            "binary_score": results.get("binary", [None])[0],
+            "hateful_l1_score": results.get(CATEGORIES['hateful'][0], [None])[0],
+            "hateful_l2_score": results.get(CATEGORIES['hateful'][1], [None])[0],
+            "insults_score": results.get(CATEGORIES['insults'][0], [None])[0],
+            "sexual_l1_score": results.get(CATEGORIES['sexual'][0], [None])[0],
+            "sexual_l2_score": results.get(CATEGORIES['sexual'][1], [None])[0],
+            "physical_violence_score": results.get(CATEGORIES['physical_violence'][0], [None])[0],
+            "self_harm_l1_score": results.get(CATEGORIES['self_harm'][0], [None])[0],
+            "self_harm_l2_score": results.get(CATEGORIES['self_harm'][1], [None])[0],
+            "aom_l1_score": results.get(CATEGORIES['all_other_misconduct'][0], [None])[0],
+            "aom_l2_score": results.get(CATEGORIES['all_other_misconduct'][1], [None])[0],
+            "openai_score": None
+        }
+        try:
+            openai_result = client.moderations.create(input=message)
+            row["openai_score"] = float(openai_result.results[0].category_scores.get("hate", 0.0))
+        except Exception:
+            row["openai_score"] = None
+
+        log_chatbot_data(row)
+    except Exception as e:
+        print(f"Error in sync logging: {e}")
+
+def process_message(message, history_no_mod, history_openai, history_lg):
+    """Wrapper function for Gradio (converts async to sync)"""
+    return asyncio.run(process_message_async(message, history_no_mod, history_openai, history_lg))
+
+def clear_all_chats():
+    return [], [], []
+
+# ---- MAIN GRADIO UI ----
+
+DISCLAIMER = """
+<div style='background: #fbbf24; color: #1e293b; border-radius: 8px; padding: 14px; margin-bottom: 12px; font-size: 15px; font-weight:500;'>
+⚠️ LionGuard 2 may make mistakes. All entries are logged (anonymised) to improve the model.
+</div>
+"""
+
+with gr.Blocks(title="LionGuard 2 Demo", theme=gr.themes.Soft()) as demo:
+    gr.HTML("<h1 style='text-align:center'>LionGuard 2 Demo</h1>")
+
+    with gr.Tabs():
+        with gr.Tab("Classifier"):
+            gr.HTML(DISCLAIMER)
+            with gr.Row():
+                with gr.Column(scale=1, min_width=400):
+                    text_input = gr.Textbox(
+                        label="Enter text to analyze:",
+                        placeholder="Type your text here...",
+                        lines=8,
+                        max_lines=16,
+                        container=True
+                    )
+                    analyze_btn = gr.Button("Analyze", variant="primary")
+                with gr.Column(scale=1, min_width=400):
+                    binary_output = gr.HTML(
+                        value='<div style="text-align: center; color: #9ca3af; padding: 30px; font-style: italic; font-size:36px;">Enter text to analyze</div>'
+                    )
+                    category_table = gr.HTML(
+                        value='<div style="text-align: center; color: #9ca3af; padding: 30px; font-style: italic;">Category scores will appear here after analysis</div>'
+                    )
+                    voting_feedback = gr.HTML(value="")
+                    current_text_id = gr.Textbox(value="", visible=False)
+
+                    with gr.Row(visible=False) as voting_buttons_row:
+                        thumbs_up_btn = gr.Button("👍 Looks Accurate", variant="primary")
+                        thumbs_down_btn = gr.Button("👎 Looks Wrong", variant="secondary")
+
+            def analyze_and_show_voting(text):
+                binary_score, category_table_val, text_id, voting_html = analyze_text(text)
+                show_vote = gr.update(visible=True) if text_id else gr.update(visible=False)
+                return binary_score, category_table_val, text_id, show_vote, "", ""
+
+            analyze_btn.click(
+                analyze_and_show_voting,
+                inputs=[text_input],
+                outputs=[binary_output, category_table, current_text_id, voting_buttons_row, voting_feedback, voting_feedback]
+            )
+            text_input.submit(
+                analyze_and_show_voting,
+                inputs=[text_input],
+                outputs=[binary_output, category_table, current_text_id, voting_buttons_row, voting_feedback, voting_feedback]
+            )
+            thumbs_up_btn.click(vote_thumbs_up, inputs=[current_text_id], outputs=[voting_feedback])
+            thumbs_down_btn.click(vote_thumbs_down, inputs=[current_text_id], outputs=[voting_feedback])
+
+        with gr.Tab("Guardrail Comparison"):
+            gr.HTML(DISCLAIMER)
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.Markdown("#### 🔵 No Moderation")
+                    chatbot_no_mod = gr.Chatbot(height=650, label="No Moderation", show_label=False, bubble_full_width=False, type='messages')
+                with gr.Column(scale=1):
+                    gr.Markdown("#### 🟠 OpenAI Moderation")
+                    chatbot_openai = gr.Chatbot(height=650, label="OpenAI Moderation", show_label=False, bubble_full_width=False, type='messages')
+                with gr.Column(scale=1):
+                    gr.Markdown("#### 🛡️ LionGuard 2")
+                    chatbot_lg = gr.Chatbot(height=650, label="LionGuard 2", show_label=False, bubble_full_width=False, type='messages')
+            gr.Markdown("##### 💬 Send Message to All Models")
+            with gr.Row():
+                message_input = gr.Textbox(
+                    placeholder="Type your message to compare responses...",
+                    show_label=False,
+                    scale=4
+                )
+                send_btn = gr.Button("Send", variant="primary", scale=1)
+            with gr.Row():
+                clear_btn = gr.Button("Clear All Chats", variant="stop")
+
+            send_btn.click(
+                process_message,
+                inputs=[message_input, chatbot_no_mod, chatbot_openai, chatbot_lg],
+                outputs=[chatbot_no_mod, chatbot_openai, chatbot_lg, message_input]
+            )
+            message_input.submit(
+                process_message,
+                inputs=[message_input, chatbot_no_mod, chatbot_openai, chatbot_lg],
+                outputs=[chatbot_no_mod, chatbot_openai, chatbot_lg, message_input]
+            )
+            clear_btn.click(
+                clear_all_chats,
+                outputs=[chatbot_no_mod, chatbot_openai, chatbot_lg]
+            )
+
+if __name__ == "__main__":
+    demo.launch()

utils.py

@@ -140,4 +140,4 @@ def set_active_model(model_key: str) -> str:
     current_model_choice = model_key
     load_model_instance(model_key)
     label = MODEL_CONFIGS[model_key]["label"]
-    return f"🦁 Using {label} ({model_key})"
+    return f"🦁 Using {label} ({model_key})"