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import os
import gradio as gr
import pandas as pd
from datetime import datetime
from pydantic import BaseModel, Field
from typing import List, Dict, Any, Optional
import numpy as np
from mistralai import Mistral
from openai import OpenAI
import re
import json
import logging
import time
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor
import threading
import pymongo
from pymongo import MongoClient
from bson.objectid import ObjectId
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] %(message)s',
handlers=[
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)
class HallucinationJudgment(BaseModel):
hallucination_detected: bool = Field(description="Whether a hallucination is detected across the responses")
confidence_score: float = Field(description="Confidence score between 0-1 for the hallucination judgment")
conflicting_facts: List[Dict[str, Any]] = Field(description="List of conflicting facts found in the responses")
reasoning: str = Field(description="Detailed reasoning for the judgment")
summary: str = Field(description="A summary of the analysis")
class PAS2:
"""Paraphrase-based Approach for LLM Systems - Using llm-as-judge methods"""
def __init__(self, mistral_api_key=None, openai_api_key=None, progress_callback=None):
"""Initialize the PAS2 with API keys"""
# For Hugging Face Spaces, we prioritize getting API keys from HF_* environment variables
# which are set from the Secrets tab in the Space settings
self.mistral_api_key = mistral_api_key or os.environ.get("HF_MISTRAL_API_KEY") or os.environ.get("MISTRAL_API_KEY")
self.openai_api_key = openai_api_key or os.environ.get("HF_OPENAI_API_KEY") or os.environ.get("OPENAI_API_KEY")
self.progress_callback = progress_callback
if not self.mistral_api_key:
raise ValueError("Mistral API key is required. Set it via HF_MISTRAL_API_KEY in Hugging Face Spaces secrets or pass it as a parameter.")
if not self.openai_api_key:
raise ValueError("OpenAI API key is required. Set it via HF_OPENAI_API_KEY in Hugging Face Spaces secrets or pass it as a parameter.")
self.mistral_client = Mistral(api_key=self.mistral_api_key)
self.openai_client = OpenAI(api_key=self.openai_api_key)
self.mistral_model = "mistral-large-latest"
self.openai_model = "o3-mini"
logger.info("PAS2 initialized with Mistral model: %s and OpenAI model: %s",
self.mistral_model, self.openai_model)
def generate_paraphrases(self, query: str, n_paraphrases: int = 3) -> List[str]:
"""Generate paraphrases of the input query using Mistral API"""
logger.info("Generating %d paraphrases for query: %s", n_paraphrases, query)
start_time = time.time()
messages = [
{
"role": "system",
"content": f"You are an expert at creating semantically equivalent paraphrases. Generate {n_paraphrases} different paraphrases of the given query that preserve the original meaning but vary in wording and structure. Return a JSON array of strings, each containing one paraphrase."
},
{
"role": "user",
"content": query
}
]
try:
logger.info("Sending paraphrase generation request to Mistral API...")
response = self.mistral_client.chat.complete(
model=self.mistral_model,
messages=messages,
response_format={"type": "json_object"}
)
content = response.choices[0].message.content
logger.debug("Received raw paraphrase response: %s", content)
paraphrases_data = json.loads(content)
# Handle different possible JSON structures
if isinstance(paraphrases_data, dict) and "paraphrases" in paraphrases_data:
paraphrases = paraphrases_data["paraphrases"]
elif isinstance(paraphrases_data, dict) and "results" in paraphrases_data:
paraphrases = paraphrases_data["results"]
elif isinstance(paraphrases_data, list):
paraphrases = paraphrases_data
else:
# Try to extract a list from any field
for key, value in paraphrases_data.items():
if isinstance(value, list) and len(value) > 0:
paraphrases = value
break
else:
logger.warning("Could not extract paraphrases from response: %s", content)
raise ValueError(f"Could not extract paraphrases from response: {content}")
# Ensure we have the right number of paraphrases
paraphrases = paraphrases[:n_paraphrases]
# Add the original query as the first item
all_queries = [query] + paraphrases
elapsed_time = time.time() - start_time
logger.info("Generated %d paraphrases in %.2f seconds", len(paraphrases), elapsed_time)
for i, p in enumerate(paraphrases, 1):
logger.info("Paraphrase %d: %s", i, p)
return all_queries
except Exception as e:
logger.error("Error generating paraphrases: %s", str(e), exc_info=True)
# Return original plus simple paraphrases as fallback
fallback_paraphrases = [
query,
f"Could you tell me about {query.strip('?')}?",
f"I'd like to know: {query}",
f"Please provide information on {query.strip('?')}."
][:n_paraphrases+1]
logger.info("Using fallback paraphrases due to error")
for i, p in enumerate(fallback_paraphrases[1:], 1):
logger.info("Fallback paraphrase %d: %s", i, p)
return fallback_paraphrases
def _get_single_response(self, query: str, index: int = None) -> str:
"""Get a single response from Mistral API for a query"""
try:
query_description = f"Query {index}: {query}" if index is not None else f"Query: {query}"
logger.info("Getting response for %s", query_description)
start_time = time.time()
messages = [
{
"role": "system",
"content": "You are a helpful AI assistant. Provide accurate, factual information in response to questions."
},
{
"role": "user",
"content": query
}
]
response = self.mistral_client.chat.complete(
model=self.mistral_model,
messages=messages
)
result = response.choices[0].message.content
elapsed_time = time.time() - start_time
logger.info("Received response for %s (%.2f seconds)", query_description, elapsed_time)
logger.debug("Response content for %s: %s", query_description, result[:100] + "..." if len(result) > 100 else result)
return result
except Exception as e:
error_msg = f"Error getting response for query '{query}': {e}"
logger.error(error_msg, exc_info=True)
return f"Error: Failed to get response for this query."
def get_responses(self, queries: List[str]) -> List[str]:
"""Get responses from Mistral API for each query in parallel"""
logger.info("Getting responses for %d queries in parallel", len(queries))
start_time = time.time()
# Use ThreadPoolExecutor for parallel API calls
with ThreadPoolExecutor(max_workers=min(len(queries), 5)) as executor:
# Submit tasks and map them to their original indices
future_to_index = {
executor.submit(self._get_single_response, query, i): i
for i, query in enumerate(queries)
}
# Prepare a list with the correct length
responses = [""] * len(queries)
# Counter for completed responses
completed_count = 0
# Collect results as they complete
for future in concurrent.futures.as_completed(future_to_index):
index = future_to_index[future]
try:
responses[index] = future.result()
# Update completion count and report progress
completed_count += 1
if self.progress_callback:
self.progress_callback("responses_progress",
completed_responses=completed_count,
total_responses=len(queries))
except Exception as e:
logger.error("Error processing response for index %d: %s", index, str(e))
responses[index] = f"Error: Failed to get response for query {index}."
# Still update completion count even for errors
completed_count += 1
if self.progress_callback:
self.progress_callback("responses_progress",
completed_responses=completed_count,
total_responses=len(queries))
elapsed_time = time.time() - start_time
logger.info("Received all %d responses in %.2f seconds total", len(responses), elapsed_time)
return responses
def detect_hallucination(self, query: str, n_paraphrases: int = 3) -> Dict:
"""
Detect hallucinations by comparing responses to paraphrased queries using a judge model
Returns:
Dict containing hallucination judgment and all responses
"""
logger.info("Starting hallucination detection for query: %s", query)
start_time = time.time()
# Report progress
if self.progress_callback:
self.progress_callback("starting", query=query)
# Generate paraphrases
logger.info("Step 1: Generating paraphrases")
if self.progress_callback:
self.progress_callback("generating_paraphrases", query=query)
all_queries = self.generate_paraphrases(query, n_paraphrases)
if self.progress_callback:
self.progress_callback("paraphrases_complete", query=query, count=len(all_queries))
# Get responses to all queries
logger.info("Step 2: Getting responses to all %d queries", len(all_queries))
if self.progress_callback:
self.progress_callback("getting_responses", query=query, total=len(all_queries))
all_responses = []
for i, q in enumerate(all_queries):
logger.info("Getting response %d/%d for query: %s", i+1, len(all_queries), q)
if self.progress_callback:
self.progress_callback("responses_progress", query=query, completed=i, total=len(all_queries))
response = self._get_single_response(q, index=i)
all_responses.append(response)
if self.progress_callback:
self.progress_callback("responses_complete", query=query)
# Judge the responses for hallucinations
logger.info("Step 3: Judging for hallucinations")
if self.progress_callback:
self.progress_callback("judging", query=query)
# The first query is the original, rest are paraphrases
original_query = all_queries[0]
original_response = all_responses[0]
paraphrased_queries = all_queries[1:] if len(all_queries) > 1 else []
paraphrased_responses = all_responses[1:] if len(all_responses) > 1 else []
# Judge the responses
judgment = self.judge_hallucination(
original_query=original_query,
original_response=original_response,
paraphrased_queries=paraphrased_queries,
paraphrased_responses=paraphrased_responses
)
# Assemble the results
results = {
"original_query": original_query,
"original_response": original_response,
"paraphrased_queries": paraphrased_queries,
"paraphrased_responses": paraphrased_responses,
"hallucination_detected": judgment.hallucination_detected,
"confidence_score": judgment.confidence_score,
"conflicting_facts": judgment.conflicting_facts,
"reasoning": judgment.reasoning,
"summary": judgment.summary
}
# Report completion
if self.progress_callback:
self.progress_callback("complete", query=query)
logger.info("Hallucination detection completed in %.2f seconds", time.time() - start_time)
return results
def judge_hallucination(self,
original_query: str,
original_response: str,
paraphrased_queries: List[str],
paraphrased_responses: List[str]) -> HallucinationJudgment:
"""
Use OpenAI's o3-mini as a judge to detect hallucinations in the responses
"""
logger.info("Judging hallucinations with OpenAI's %s model", self.openai_model)
start_time = time.time()
# Prepare the context for the judge
context = f"""
Original Question: {original_query}
Original Response:
{original_response}
Paraphrased Questions and their Responses:
"""
for i, (query, response) in enumerate(zip(paraphrased_queries, paraphrased_responses), 1):
context += f"\nParaphrased Question {i}: {query}\n\nResponse {i}:\n{response}\n"
system_prompt = """
You are a judge evaluating whether an AI is hallucinating across different responses to semantically equivalent questions.
Analyze all responses carefully to identify any factual inconsistencies or contradictions.
Focus on factual discrepancies, not stylistic differences.
A hallucination is when the AI states different facts in response to questions that are asking for the same information.
Your response should be a JSON with the following fields:
- hallucination_detected: boolean indicating whether hallucinations were found
- confidence_score: number between 0 and 1 representing your confidence in the judgment
- conflicting_facts: an array of objects describing any conflicting information found
- reasoning: detailed explanation for your judgment
- summary: a concise summary of your analysis
"""
try:
logger.info("Sending judgment request to OpenAI API...")
response = self.openai_client.chat.completions.create(
model=self.openai_model,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Evaluate these responses for hallucinations:\n\n{context}"}
],
response_format={"type": "json_object"}
)
result_json = json.loads(response.choices[0].message.content)
logger.debug("Received judgment response: %s", result_json)
# Create the HallucinationJudgment object from the JSON response
judgment = HallucinationJudgment(
hallucination_detected=result_json.get("hallucination_detected", False),
confidence_score=result_json.get("confidence_score", 0.0),
conflicting_facts=result_json.get("conflicting_facts", []),
reasoning=result_json.get("reasoning", "No reasoning provided."),
summary=result_json.get("summary", "No summary provided.")
)
elapsed_time = time.time() - start_time
logger.info("Judgment completed in %.2f seconds", elapsed_time)
return judgment
except Exception as e:
logger.error("Error in hallucination judgment: %s", str(e), exc_info=True)
# Return a fallback judgment
return HallucinationJudgment(
hallucination_detected=False,
confidence_score=0.0,
conflicting_facts=[],
reasoning="Failed to obtain judgment from the model.",
summary="Analysis failed due to API error."
)
class HallucinationDetectorApp:
def __init__(self):
self.pas2 = None
logger.info("Initializing HallucinationDetectorApp")
self._initialize_database()
self.progress_callback = None
def _initialize_database(self):
"""Initialize MongoDB connection for persistent feedback storage"""
try:
# Get MongoDB connection string from environment variable
mongo_uri = os.environ.get("MONGODB_URI")
if not mongo_uri:
logger.warning("MONGODB_URI not found in environment variables. Please set it in HuggingFace Spaces secrets.")
logger.warning("Using a placeholder URI for now - connection will fail until proper URI is provided.")
# Use a placeholder - this will fail but allows the app to initialize
mongo_uri = "mongodb+srv://username:[email protected]/?retryWrites=true&w=majority"
# Connect to MongoDB
self.mongo_client = MongoClient(mongo_uri)
# Access or create database
self.db = self.mongo_client["hallucination_detector"]
# Access or create collection
self.feedback_collection = self.db["feedback"]
# Create index on timestamp for faster querying
self.feedback_collection.create_index("timestamp")
# Test connection
self.mongo_client.admin.command('ping')
logger.info("MongoDB connection successful")
except Exception as e:
logger.error(f"Error initializing MongoDB: {str(e)}", exc_info=True)
logger.warning("Proceeding without database connection. Data will not be saved persistently.")
self.mongo_client = None
self.db = None
self.feedback_collection = None
def set_progress_callback(self, callback):
"""Set the progress callback function"""
self.progress_callback = callback
def initialize_api(self, mistral_api_key, openai_api_key):
"""Initialize the PAS2 with API keys"""
try:
logger.info("Initializing PAS2 with API keys")
self.pas2 = PAS2(
mistral_api_key=mistral_api_key,
openai_api_key=openai_api_key,
progress_callback=self.progress_callback
)
logger.info("API initialization successful")
return "API keys set successfully! You can now use the application."
except Exception as e:
logger.error("Error initializing API: %s", str(e), exc_info=True)
return f"Error initializing API: {str(e)}"
def process_query(self, query: str):
"""Process the query using PAS2"""
if not self.pas2:
logger.error("PAS2 not initialized")
return {
"error": "Please set API keys first before processing queries."
}
if not query.strip():
logger.warning("Empty query provided")
return {
"error": "Please enter a query."
}
try:
# Set the progress callback if needed
if self.progress_callback and self.pas2.progress_callback != self.progress_callback:
self.pas2.progress_callback = self.progress_callback
# Process the query
logger.info("Processing query with PAS2: %s", query)
results = self.pas2.detect_hallucination(query)
logger.info("Query processing completed successfully")
return results
except Exception as e:
logger.error("Error processing query: %s", str(e), exc_info=True)
return {
"error": f"Error processing query: {str(e)}"
}
def save_feedback(self, results, feedback):
"""Save results and user feedback to MongoDB"""
try:
logger.info("Saving user feedback: %s", feedback)
if self.feedback_collection is None:
logger.error("MongoDB connection not available. Cannot save feedback.")
return "Database connection not available. Feedback not saved."
# Prepare document for MongoDB
document = {
"timestamp": datetime.now(),
"original_query": results.get('original_query', ''),
"original_response": results.get('original_response', ''),
"paraphrased_queries": results.get('paraphrased_queries', []),
"paraphrased_responses": results.get('paraphrased_responses', []),
"hallucination_detected": results.get('hallucination_detected', False),
"confidence_score": results.get('confidence_score', 0.0),
"conflicting_facts": results.get('conflicting_facts', []),
"reasoning": results.get('reasoning', ''),
"summary": results.get('summary', ''),
"user_feedback": feedback
}
# Insert document into collection
self.feedback_collection.insert_one(document)
logger.info("Feedback saved successfully to MongoDB")
return "Feedback saved successfully!"
except Exception as e:
logger.error("Error saving feedback: %s", str(e), exc_info=True)
return f"Error saving feedback: {str(e)}"
def get_feedback_stats(self):
"""Get statistics about collected feedback from MongoDB"""
try:
if self.feedback_collection is None:
logger.error("MongoDB connection not available. Cannot get feedback stats.")
return None
# Get total feedback count
total_count = self.feedback_collection.count_documents({})
# Get accuracy stats based on user feedback
correct_predictions = 0
# Fetch all feedback documents
feedback_docs = list(self.feedback_collection.find({}, {"user_feedback": 1}))
# Count correct predictions based on user feedback
for doc in feedback_docs:
if "user_feedback" in doc:
# If feedback starts with "Yes", it's a correct prediction
if doc["user_feedback"].startswith("Yes"):
correct_predictions += 1
# Calculate accuracy percentage
accuracy = correct_predictions / max(total_count, 1)
return {
"total_feedback": total_count,
"correct_predictions": correct_predictions,
"accuracy": accuracy
}
except Exception as e:
logger.error("Error getting feedback stats: %s", str(e), exc_info=True)
return None
def export_data_to_csv(self, filepath=None):
"""Export all feedback data to a CSV file for analysis"""
try:
if self.feedback_collection is None:
logger.error("MongoDB connection not available. Cannot export data.")
return "Database connection not available. Cannot export data."
# Query all feedback data
cursor = self.feedback_collection.find({})
# Convert cursor to list of dictionaries
records = list(cursor)
# Convert MongoDB documents to pandas DataFrame
# Handle nested arrays and complex objects
for record in records:
# Convert ObjectId to string
record['_id'] = str(record['_id'])
# Convert datetime objects to string
if 'timestamp' in record:
record['timestamp'] = record['timestamp'].strftime("%Y-%m-%d %H:%M:%S")
# Convert lists to strings for CSV storage
if 'paraphrased_queries' in record:
record['paraphrased_queries'] = json.dumps(record['paraphrased_queries'])
if 'paraphrased_responses' in record:
record['paraphrased_responses'] = json.dumps(record['paraphrased_responses'])
if 'conflicting_facts' in record:
record['conflicting_facts'] = json.dumps(record['conflicting_facts'])
# Create DataFrame
df = pd.DataFrame(records)
# Define default filepath if not provided
if not filepath:
filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),
f"hallucination_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv")
# Export to CSV
df.to_csv(filepath, index=False)
logger.info(f"Data successfully exported to {filepath}")
return filepath
except Exception as e:
logger.error(f"Error exporting data: {str(e)}", exc_info=True)
return f"Error exporting data: {str(e)}"
def get_recent_queries(self, limit=10):
"""Get most recent queries for display in the UI"""
try:
if self.feedback_collection is None:
logger.error("MongoDB connection not available. Cannot get recent queries.")
return []
# Get most recent queries
cursor = self.feedback_collection.find(
{},
{"original_query": 1, "hallucination_detected": 1, "timestamp": 1}
).sort("timestamp", pymongo.DESCENDING).limit(limit)
# Convert to list of dictionaries
recent_queries = []
for doc in cursor:
recent_queries.append({
"id": str(doc["_id"]),
"query": doc["original_query"],
"hallucination_detected": doc.get("hallucination_detected", False),
"timestamp": doc["timestamp"].strftime("%Y-%m-%d %H:%M:%S") if isinstance(doc["timestamp"], datetime) else doc["timestamp"]
})
return recent_queries
except Exception as e:
logger.error(f"Error getting recent queries: {str(e)}", exc_info=True)
return []
def get_query_details(self, query_id):
"""Get full details for a specific query by ID"""
try:
if self.feedback_collection is None:
logger.error("MongoDB connection not available. Cannot get query details.")
return None
# Convert string ID to ObjectId
obj_id = ObjectId(query_id)
# Find the query by ID
doc = self.feedback_collection.find_one({"_id": obj_id})
if doc is None:
logger.warning(f"No query found with ID {query_id}")
return None
# Convert ObjectId to string for JSON serialization
doc["_id"] = str(doc["_id"])
# Convert timestamp to string
if "timestamp" in doc and isinstance(doc["timestamp"], datetime):
doc["timestamp"] = doc["timestamp"].strftime("%Y-%m-%d %H:%M:%S")
return doc
except Exception as e:
logger.error(f"Error getting query details: {str(e)}", exc_info=True)
return None
# Progress tracking for UI updates
class ProgressTracker:
"""Tracks progress of hallucination detection for UI updates"""
STAGES = {
"idle": {"status": "Ready", "progress": 0, "color": "#757575"},
"starting": {"status": "Starting process...", "progress": 5, "color": "#2196F3"},
"generating_paraphrases": {"status": "Generating paraphrases...", "progress": 15, "color": "#2196F3"},
"paraphrases_complete": {"status": "Paraphrases generated", "progress": 30, "color": "#2196F3"},
"getting_responses": {"status": "Getting responses (0/0)...", "progress": 35, "color": "#2196F3"},
"responses_progress": {"status": "Getting responses ({completed}/{total})...", "progress": 40, "color": "#2196F3"},
"responses_complete": {"status": "All responses received", "progress": 65, "color": "#2196F3"},
"judging": {"status": "Analyzing responses for hallucinations...", "progress": 70, "color": "#2196F3"},
"complete": {"status": "Analysis complete!", "progress": 100, "color": "#4CAF50"},
"error": {"status": "Error: {error_message}", "progress": 100, "color": "#F44336"}
}
def __init__(self):
self.stage = "idle"
self.stage_data = self.STAGES[self.stage].copy()
self.query = ""
self.completed_responses = 0
self.total_responses = 0
self.error_message = ""
self._lock = threading.Lock()
self._status_callback = None
self._stop_event = threading.Event()
self._update_thread = None
def register_callback(self, callback_fn):
"""Register callback function to update UI"""
self._status_callback = callback_fn
def update_stage(self, stage, **kwargs):
"""Update the current stage and trigger callback"""
with self._lock:
if stage in self.STAGES:
self.stage = stage
self.stage_data = self.STAGES[stage].copy()
# Update with any additional parameters
for key, value in kwargs.items():
if key == 'query':
self.query = value
elif key == 'completed_responses':
self.completed_responses = value
elif key == 'total_responses':
self.total_responses = value
elif key == 'error_message':
self.error_message = value
# Format status message
if stage == 'responses_progress':
self.stage_data['status'] = self.stage_data['status'].format(
completed=self.completed_responses,
total=self.total_responses
)
elif stage == 'error':
self.stage_data['status'] = self.stage_data['status'].format(
error_message=self.error_message
)
if self._status_callback:
self._status_callback(self.get_html_status())
def get_html_status(self):
"""Get HTML representation of current status"""
progress_width = f"{self.stage_data['progress']}%"
status_text = self.stage_data['status']
color = self.stage_data['color']
query_info = f'<div class="query-display">{self.query}</div>' if self.query else ''
# Only show status text if not in idle state
status_display = f'<div class="progress-status" style="color: {color};">{status_text}</div>' if self.stage != "idle" else ''
html = f"""
<div class="progress-container">
{query_info}
{status_display}
<div class="progress-bar-container">
<div class="progress-bar" style="width: {progress_width}; background-color: {color};"></div>
</div>
</div>
"""
return html
def start_pulsing(self):
"""Start a pulsing animation for the progress bar during long operations"""
if self._update_thread and self._update_thread.is_alive():
return
self._stop_event.clear()
self._update_thread = threading.Thread(target=self._pulse_progress)
self._update_thread.daemon = True
self._update_thread.start()
def stop_pulsing(self):
"""Stop the pulsing animation"""
self._stop_event.set()
if self._update_thread:
self._update_thread.join(0.5)
def _pulse_progress(self):
"""Animate the progress bar to show activity"""
pulse_stages = ["⋯", "⋯⋯", "⋯⋯⋯", "⋯⋯", "⋯"]
i = 0
while not self._stop_event.is_set():
with self._lock:
if self.stage not in ["idle", "complete", "error"]:
status_base = self.stage_data['status'].split("...")[0] if "..." in self.stage_data['status'] else self.stage_data['status']
self.stage_data['status'] = f"{status_base}... {pulse_stages[i]}"
if self._status_callback:
self._status_callback(self.get_html_status())
i = (i + 1) % len(pulse_stages)
time.sleep(0.3)
def create_interface():
"""Create Gradio interface"""
detector = HallucinationDetectorApp()
# Initialize Progress Tracker
progress_tracker = ProgressTracker()
# Initialize APIs from environment variables automatically
try:
detector.initialize_api(
mistral_api_key=os.environ.get("HF_MISTRAL_API_KEY"),
openai_api_key=os.environ.get("HF_OPENAI_API_KEY")
)
except Exception as e:
print(f"Warning: Failed to initialize APIs from environment variables: {e}")
print("Please make sure HF_MISTRAL_API_KEY and HF_OPENAI_API_KEY are set in your environment")
# CSS for styling
css = """
.container {
max-width: 1000px;
margin: 0 auto;
}
.title {
text-align: center;
margin-bottom: 0.5em;
color: #1a237e;
font-weight: 600;
}
.subtitle {
text-align: center;
margin-bottom: 1.5em;
color: #455a64;
font-size: 1.2em;
}
.section-title {
margin-top: 1em;
margin-bottom: 0.5em;
font-weight: bold;
color: #283593;
}
.info-box {
padding: 1.2em;
border-radius: 8px;
background-color: #f5f5f5;
margin-bottom: 1em;
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
}
.hallucination-positive {
padding: 1.2em;
border-radius: 8px;
background-color: #ffebee;
border-left: 5px solid #f44336;
margin-bottom: 1em;
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
}
.hallucination-negative {
padding: 1.2em;
border-radius: 8px;
background-color: #e8f5e9;
border-left: 5px solid #4caf50;
margin-bottom: 1em;
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
}
.response-box {
padding: 1.2em;
border-radius: 8px;
background-color: #f5f5f5;
margin-bottom: 0.8em;
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
}
.example-queries {
display: flex;
flex-wrap: wrap;
gap: 8px;
margin-bottom: 15px;
}
.example-query {
background-color: #e3f2fd;
padding: 8px 15px;
border-radius: 18px;
font-size: 0.9em;
cursor: pointer;
transition: all 0.2s;
border: 1px solid #bbdefb;
}
.example-query:hover {
background-color: #bbdefb;
box-shadow: 0 2px 5px rgba(0,0,0,0.1);
}
.stats-section {
display: flex;
justify-content: space-between;
background-color: #e8eaf6;
padding: 15px;
border-radius: 8px;
margin-bottom: 20px;
}
.stat-item {
text-align: center;
padding: 10px;
}
.stat-value {
font-size: 1.5em;
font-weight: bold;
color: #303f9f;
}
.stat-label {
font-size: 0.9em;
color: #5c6bc0;
}
.feedback-section {
border-top: 1px solid #e0e0e0;
padding-top: 15px;
margin-top: 20px;
}
footer {
text-align: center;
padding: 20px;
margin-top: 30px;
color: #9e9e9e;
font-size: 0.9em;
}
.processing-status {
padding: 12px;
background-color: #fff3e0;
border-left: 4px solid #ff9800;
margin-bottom: 15px;
font-weight: 500;
color: #e65100;
}
.debug-panel {
background-color: #f5f5f5;
border: 1px solid #e0e0e0;
border-radius: 4px;
padding: 10px;
margin-top: 15px;
font-family: monospace;
font-size: 0.9em;
white-space: pre-wrap;
max-height: 200px;
overflow-y: auto;
}
.progress-container {
padding: 15px;
background-color: #fff;
border-radius: 8px;
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
margin-bottom: 15px;
}
.progress-status {
font-weight: 500;
margin-bottom: 8px;
padding: 4px 0;
font-size: 0.95em;
}
.progress-bar-container {
background-color: #e0e0e0;
height: 10px;
border-radius: 5px;
overflow: hidden;
margin-bottom: 10px;
box-shadow: inset 0 1px 3px rgba(0,0,0,0.1);
}
.progress-bar {
height: 100%;
transition: width 0.5s ease;
background-image: linear-gradient(to right, #2196F3, #3f51b5);
}
.query-display {
font-style: italic;
color: #666;
margin-bottom: 10px;
background-color: #f5f5f5;
padding: 8px;
border-radius: 4px;
border-left: 3px solid #2196F3;
}
"""
# Example queries
example_queries = [
"Who was the first person to land on the moon?",
"What is the capital of France?",
"How many planets are in our solar system?",
"Who wrote the novel 1984?",
"What is the speed of light?",
"What was the first computer?"
]
# Function to update the progress display
def update_progress_display(html):
"""Update the progress display with the provided HTML"""
return gr.update(visible=True, value=html)
# Register the callback with the tracker
progress_tracker.register_callback(update_progress_display)
# Register the tracker with the detector
detector.set_progress_callback(progress_tracker.update_stage)
# Helper function to set example query
def set_example_query(example):
return example
# Function to show processing is starting
def start_processing(query):
logger.info("Processing query: %s", query)
# Stop any existing pulsing to prepare for incremental progress updates
progress_tracker.stop_pulsing()
# Reset to a processing state without the "Ready" text
# Use "starting" stage but with minimal UI display
progress_tracker.stage = "starting"
progress_tracker.query = query
# Force UI update with clean display
if progress_tracker._status_callback:
progress_tracker._status_callback(progress_tracker.get_html_status())
return [
gr.update(visible=True), # Show the progress display
gr.update(visible=False), # Hide the results accordion
gr.update(visible=False), # Hide the feedback accordion
None # Reset hidden results
]
# Main processing function
def process_query_and_display_results(query, progress=gr.Progress()):
if not query.strip():
logger.warning("Empty query submitted")
progress_tracker.stop_pulsing()
progress_tracker.update_stage("error", error_message="Please enter a query.")
return [
gr.update(visible=True), # Show the progress with error
gr.update(visible=False),
gr.update(visible=False),
None
]
# Check if API is initialized
if not detector.pas2:
try:
# Try to initialize from environment variables
logger.info("Initializing APIs from environment variables")
progress(0.05, desc="Initializing API...")
init_message = detector.initialize_api(
mistral_api_key=os.environ.get("HF_MISTRAL_API_KEY"),
openai_api_key=os.environ.get("HF_OPENAI_API_KEY")
)
if "successfully" not in init_message:
logger.error("Failed to initialize APIs: %s", init_message)
progress_tracker.stop_pulsing()
progress_tracker.update_stage("error", error_message="API keys not found in environment variables.")
return [
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=False),
None
]
except Exception as e:
logger.error("Error initializing API: %s", str(e), exc_info=True)
progress_tracker.stop_pulsing()
progress_tracker.update_stage("error", error_message=f"Error initializing API: {str(e)}")
return [
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=False),
None
]
try:
# Process the query
logger.info("Starting hallucination detection process")
start_time = time.time()
# Set up a custom progress callback that uses both the progress_tracker and the gr.Progress
def combined_progress_callback(stage, **kwargs):
# Skip the idle stage, which shows "Ready"
if stage == "idle":
return
progress_tracker.update_stage(stage, **kwargs)
# Map the stages to progress values for the gr.Progress bar
stage_to_progress = {
"starting": 0.05,
"generating_paraphrases": 0.15,
"paraphrases_complete": 0.3,
"getting_responses": 0.35,
"responses_progress": lambda kwargs: 0.35 + (0.3 * (kwargs.get("completed", 0) / max(kwargs.get("total", 1), 1))),
"responses_complete": 0.65,
"judging": 0.7,
"complete": 1.0,
"error": 1.0
}
# Update the gr.Progress bar
if stage in stage_to_progress:
prog_value = stage_to_progress[stage]
if callable(prog_value):
prog_value = prog_value(kwargs)
desc = progress_tracker.STAGES[stage]["status"]
if "{" in desc and "}" in desc:
# Format the description with any kwargs
desc = desc.format(**kwargs)
# Ensure UI updates by adding a small delay
# This forces the progress updates to be rendered
progress(prog_value, desc=desc)
# For certain key stages, add a small sleep to ensure progress is visible
if stage in ["starting", "generating_paraphrases", "paraphrases_complete",
"getting_responses", "responses_complete", "judging", "complete"]:
time.sleep(0.2) # Small delay to ensure UI update is visible
# Use these steps for processing
detector.set_progress_callback(combined_progress_callback)
# Create a wrapper function for detect_hallucination that gives more control over progress updates
def run_detection_with_visible_progress():
# Step 1: Start
combined_progress_callback("starting", query=query)
time.sleep(0.3) # Ensure starting status is visible
# Step 2: Generate paraphrases (15-30%)
combined_progress_callback("generating_paraphrases", query=query)
all_queries = detector.pas2.generate_paraphrases(query)
combined_progress_callback("paraphrases_complete", query=query, count=len(all_queries))
# Step 3: Get responses (35-65%)
combined_progress_callback("getting_responses", query=query, total=len(all_queries))
all_responses = []
for i, q in enumerate(all_queries):
# Show incremental progress for each response
combined_progress_callback("responses_progress", query=query, completed=i, total=len(all_queries))
response = detector.pas2._get_single_response(q, index=i)
all_responses.append(response)
combined_progress_callback("responses_complete", query=query)
# Step 4: Judge hallucinations (70-100%)
combined_progress_callback("judging", query=query)
# The first query is the original, rest are paraphrases
original_query = all_queries[0]
original_response = all_responses[0]
paraphrased_queries = all_queries[1:] if len(all_queries) > 1 else []
paraphrased_responses = all_responses[1:] if len(all_responses) > 1 else []
# Judge the responses
judgment = detector.pas2.judge_hallucination(
original_query=original_query,
original_response=original_response,
paraphrased_queries=paraphrased_queries,
paraphrased_responses=paraphrased_responses
)
# Assemble the results
results = {
"original_query": original_query,
"original_response": original_response,
"paraphrased_queries": paraphrased_queries,
"paraphrased_responses": paraphrased_responses,
"hallucination_detected": judgment.hallucination_detected,
"confidence_score": judgment.confidence_score,
"conflicting_facts": judgment.conflicting_facts,
"reasoning": judgment.reasoning,
"summary": judgment.summary
}
# Show completion
combined_progress_callback("complete", query=query)
time.sleep(0.3) # Ensure complete status is visible
return results
# Run the detection process with visible progress
results = run_detection_with_visible_progress()
# Calculate elapsed time
elapsed_time = time.time() - start_time
logger.info("Hallucination detection completed in %.2f seconds", elapsed_time)
# Check for errors
if "error" in results:
logger.error("Error in results: %s", results["error"])
progress_tracker.stop_pulsing()
progress_tracker.update_stage("error", error_message=results["error"])
return [
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=False),
None
]
# Prepare responses for display
original_query = results["original_query"]
original_response = results["original_response"]
paraphrased_queries = results["paraphrased_queries"]
paraphrased_responses = results["paraphrased_responses"]
hallucination_detected = results["hallucination_detected"]
confidence = results["confidence_score"]
reasoning = results["reasoning"]
summary = results["summary"]
# Format conflicting facts
conflicting_facts = results["conflicting_facts"]
conflicting_facts_text = ""
if conflicting_facts:
for i, fact in enumerate(conflicting_facts, 1):
conflicting_facts_text += f"{i}. "
if isinstance(fact, dict):
for key, value in fact.items():
conflicting_facts_text += f"{key}: {value}, "
conflicting_facts_text = conflicting_facts_text.rstrip(", ")
else:
conflicting_facts_text += str(fact)
conflicting_facts_text += "\n"
# Format responses to escape any backslashes
original_response_safe = original_response.replace('\\', '\\\\').replace('\n', '<br>')
paraphrased_responses_safe = [r.replace('\\', '\\\\').replace('\n', '<br>') for r in paraphrased_responses]
reasoning_safe = reasoning.replace('\\', '\\\\').replace('\n', '<br>')
conflicting_facts_text_safe = conflicting_facts_text.replace('\\', '\\\\').replace('\n', '<br>') if conflicting_facts_text else "None identified"
html_output = f"""
<div class="container">
<h2 class="title">Hallucination Detection Results</h2>
<div class="stats-section">
<div class="stat-item">
<div class="stat-value">{'Yes' if hallucination_detected else 'No'}</div>
<div class="stat-label">Hallucination Detected</div>
</div>
<div class="stat-item">
<div class="stat-value">{confidence:.2f}</div>
<div class="stat-label">Confidence Score</div>
</div>
<div class="stat-item">
<div class="stat-value">{len(paraphrased_queries)}</div>
<div class="stat-label">Paraphrases Analyzed</div>
</div>
<div class="stat-item">
<div class="stat-value">{elapsed_time:.1f}s</div>
<div class="stat-label">Processing Time</div>
</div>
</div>
<div class="{'hallucination-positive' if hallucination_detected else 'hallucination-negative'}">
<h3>Analysis Summary</h3>
<p>{summary}</p>
</div>
<div class="section-title">Original Query</div>
<div class="response-box">
{original_query}
</div>
<div class="section-title">Original Response</div>
<div class="response-box">
{original_response_safe}
</div>
<div class="section-title">Paraphrased Queries and Responses</div>
"""
for i, (q, r) in enumerate(zip(paraphrased_queries, paraphrased_responses_safe), 1):
html_output += f"""
<div class="section-title">Paraphrased Query {i}</div>
<div class="response-box">
{q}
</div>
<div class="section-title">Response {i}</div>
<div class="response-box">
{r}
</div>
"""
html_output += f"""
<div class="section-title">Detailed Analysis</div>
<div class="info-box">
<p><strong>Reasoning:</strong></p>
<p>{reasoning_safe}</p>
<p><strong>Conflicting Facts:</strong></p>
<p>{conflicting_facts_text_safe}</p>
</div>
</div>
"""
logger.info("Updating UI with results")
progress_tracker.stop_pulsing()
return [
gr.update(visible=False), # Hide progress display when showing results
gr.update(visible=True, value=html_output),
gr.update(visible=True),
results
]
except Exception as e:
logger.error("Error processing query: %s", str(e), exc_info=True)
progress_tracker.stop_pulsing()
progress_tracker.update_stage("error", error_message=f"Error processing query: {str(e)}")
return [
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=False),
None
]
# Helper function to submit feedback
def combine_feedback(fb_input, fb_text, results):
combined_feedback = f"{fb_input}: {fb_text}" if fb_text else fb_input
if not results:
return "No results to attach feedback to."
response = detector.save_feedback(results, combined_feedback)
return response
# Create the interface
with gr.Blocks(css=css, theme=gr.themes.Soft()) as interface:
gr.HTML(
"""
<div style="text-align: center; margin-bottom: 1.5rem">
<h1 style="font-size: 2.2em; font-weight: 600; color: #1a237e; margin-bottom: 0.2em;">PAS2 - Hallucination Detector</h1>
<h3 style="font-size: 1.3em; color: #455a64; margin-bottom: 0.8em;">Advanced AI Response Verification Using Model-as-Judge</h3>
<p style="font-size: 1.1em; color: #546e7a; max-width: 800px; margin: 0 auto;">
This tool detects hallucinations in AI responses by comparing answers to semantically equivalent questions and using a specialized judge model.
</p>
</div>
"""
)
with gr.Accordion("About this Tool", open=False):
gr.Markdown(
"""
### How It Works
This tool implements the Paraphrase-based Approach for Scrutinizing Systems (PAS2) with a model-as-judge enhancement:
1. **Paraphrase Generation**: Your question is paraphrased multiple ways while preserving its core meaning
2. **Multiple Responses**: All questions (original + paraphrases) are sent to Mistral Large model
3. **Expert Judgment**: OpenAI's o3-mini analyzes all responses to detect factual inconsistencies
### Why This Approach?
When an AI hallucinates, it often provides different answers to the same question when phrased differently.
By using a separate judge model, we can identify these inconsistencies more effectively than with
metric-based approaches.
### Understanding the Results
- **Confidence Score**: Indicates the judge's confidence in the hallucination detection
- **Conflicting Facts**: Specific inconsistencies found across responses
- **Reasoning**: The judge's detailed analysis explaining its decision
### Privacy Notice
Your queries and the system's responses are saved to help improve hallucination detection.
No personally identifiable information is collected.
"""
)
with gr.Row():
with gr.Column():
# First define the query input
gr.Markdown("### Enter Your Question")
with gr.Row():
query_input = gr.Textbox(
label="",
placeholder="Ask a factual question (e.g., Who was the first person to land on the moon?)",
lines=3
)
# Now define the example queries
gr.Markdown("### Or Try an Example")
example_row = gr.Row()
with example_row:
for example in example_queries:
example_btn = gr.Button(
example,
elem_classes=["example-query"],
scale=0
)
example_btn.click(
fn=set_example_query,
inputs=[gr.Textbox(value=example, visible=False)],
outputs=[query_input]
)
with gr.Row():
submit_button = gr.Button("Detect Hallucinations", variant="primary", scale=1)
# Error message
error_message = gr.HTML(
label="Status",
visible=False
)
# Progress display
progress_display = gr.HTML(
value=progress_tracker.get_html_status(),
visible=True
)
# Results display
results_accordion = gr.HTML(visible=False)
# Add feedback stats display
feedback_stats = gr.HTML(visible=True)
# Function to continuously update stats
def update_stats():
stats = detector.get_feedback_stats()
if stats:
total = stats['total_feedback']
correct = stats['correct_predictions']
# Get accuracy directly from the stats
accuracy = stats['accuracy']
# Format accuracy percentage
accuracy_pct = f"{accuracy * 100:.1f}%"
stats_html = f"""
<div class="stats-section" style="background-color: #e8f5e9; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); margin-top: 5px;">
<div class="stat-item">
<div class="stat-value" style="font-size: 2em; color: #2e7d32;">{total}</div>
<div class="stat-label" style="font-weight: bold;">Total Responses</div>
</div>
<div class="stat-item">
<div class="stat-value" style="font-size: 2em; color: #2e7d32;">{accuracy_pct}</div>
<div class="stat-label" style="font-weight: bold;">Correct Predictions</div>
</div>
</div>
<div style="text-align: center; margin-top: 10px; font-style: italic; color: #666;">
Based on user feedback: {correct} correct out of {total} total predictions
</div>
"""
return stats_html
return ""
# Set up interval to update stats
with gr.Row(elem_id="stats-container"):
with gr.Column():
gr.Markdown("### 📊 Live Prediction Accuracy")
gr.Markdown("_Auto-refreshes every 5 seconds from MongoDB based on user feedback_")
live_stats = gr.HTML(update_stats())
# Add loading animation style
gr.HTML("""
<style>
@keyframes pulse {
0% { opacity: 0.6; }
50% { opacity: 1; }
100% { opacity: 0.6; }
}
.refreshing::after {
content: "⟳";
display: inline-block;
margin-left: 8px;
animation: pulse 1.5s infinite ease-in-out;
color: #2e7d32;
}
#stats-container {
border: 1px solid #e0e0e0;
border-radius: 10px;
padding: 15px;
margin: 10px 0;
background-color: #2762d7;
}
</style>
<div class="refreshing" style="text-align: right; font-size: 0.8em; color: #666;">Auto-refreshing</div>
""")
# Create a refresh button that will be auto-clicked
refresh_btn = gr.Button("Refresh Stats", visible=False)
refresh_btn.click(
fn=update_stats,
outputs=[live_stats]
)
# Add JavaScript to auto-refresh the statistics
gr.HTML("""
<script>
// Auto-refresh stats every 5 seconds
function setupAutoRefresh() {
const refreshInterval = 5000; // 5 seconds
setInterval(() => {
// Find the refresh button by its text and click it
const refreshButtons = Array.from(document.querySelectorAll('button'));
const refreshBtn = refreshButtons.find(btn => btn.textContent.includes('Refresh Stats'));
if (refreshBtn) {
refreshBtn.click();
}
}, refreshInterval);
}
// Set up the auto-refresh after the page loads
if (window.gradio_loaded) {
setupAutoRefresh();
} else {
document.addEventListener('DOMContentLoaded', setupAutoRefresh);
}
</script>
""")
# Feedback section
with gr.Accordion("Provide Feedback", open=False, visible=False) as feedback_accordion:
gr.Markdown("### Help Improve the System")
gr.Markdown("Your feedback helps us refine the hallucination detection system.")
feedback_input = gr.Radio(
label="Is the hallucination detection accurate?",
choices=["Yes, correct detection", "No, incorrectly flagged hallucination", "No, missed hallucination", "Unsure/Other"],
value="Yes, correct detection"
)
feedback_text = gr.Textbox(
label="Additional comments (optional)",
placeholder="Please provide any additional observations or details...",
lines=2
)
feedback_button = gr.Button("Submit Feedback", variant="secondary")
feedback_status = gr.Textbox(label="Feedback Status", interactive=False, visible=False)
# Stats are now displayed in the live stats section
# Hidden state to store results for feedback
hidden_results = gr.State()
# Set up event handlers
submit_button.click(
fn=start_processing,
inputs=[query_input],
outputs=[progress_display, results_accordion, feedback_accordion, hidden_results],
queue=False
).then(
fn=process_query_and_display_results,
inputs=[query_input],
outputs=[progress_display, results_accordion, feedback_accordion, hidden_results]
)
feedback_button.click(
fn=combine_feedback,
inputs=[feedback_input, feedback_text, hidden_results],
outputs=[feedback_status]
)
# Footer
gr.HTML(
"""
<footer>
<p>Paraphrase-based Approach for Scrutinizing Systems (PAS2) - Advanced Hallucination Detection</p>
<p>Using Mistral Large for generation and OpenAI o3-mini as judge</p>
</footer>
"""
)
return interface
# Add a test function to demonstrate progress bar in isolation
def test_progress():
"""Simple test function to demonstrate progress bar"""
import gradio as gr
import time
def slow_process(progress=gr.Progress()):
progress(0, desc="Starting process...")
time.sleep(0.5)
# Phase 1: Generating paraphrases
progress(0.15, desc="Generating paraphrases...")
time.sleep(1)
progress(0.3, desc="Paraphrases generated")
time.sleep(0.5)
# Phase 2: Getting responses
progress(0.35, desc="Getting responses...")
# Show incremental progress for responses
for i in range(3):
time.sleep(0.8)
prog = 0.35 + (0.3 * ((i+1) / 3))
progress(prog, desc=f"Getting responses ({i+1}/3)...")
progress(0.65, desc="All responses received")
time.sleep(0.5)
# Phase 3: Analyzing
progress(0.7, desc="Analyzing responses for hallucinations...")
time.sleep(2)
# Complete
progress(1.0, desc="Analysis complete!")
return "Process completed successfully!"
with gr.Blocks() as demo:
with gr.Row():
btn = gr.Button("Start Process")
output = gr.Textbox(label="Result")
btn.click(fn=slow_process, outputs=output)
demo.launch()
# Main application entry point
if __name__ == "__main__":
logger.info("Starting PAS2 Hallucination Detector")
interface = create_interface()
logger.info("Launching Gradio interface...")
interface.launch(
server_name="0.0.0.0", # Bind to all interfaces
server_port=7860, # Default Hugging Face Spaces port
show_api=False,
quiet=True, # Changed to True for Hugging Face deployment
share=False,
max_threads=10,
debug=False # Changed to False for production deployment
)
# Uncomment this line to run the test function instead of the main interface
# if __name__ == "__main__":
# test_progress()
|