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import requests
import PyPDF2
# 1. Configure the page settings
st.set_page_config(page_title="HalluciGuard", page_icon="🛡️", layout="centered")
# --- CUSTOM Premium CSS ---
st.markdown("""
<style>
/* Global Font Import */
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;800&display=swap');
html, body, [class*="css"] {
font-family: 'Inter', sans-serif;
}
/* Glassmorphism for text areas */
.stTextArea textarea {
background-color: rgba(22, 27, 34, 0.6) !important;
border-radius: 12px;
border: 1px solid rgba(255, 255, 255, 0.08) !important;
color: #ffffff !important;
padding: 12px !important;
transition: all 0.3s ease;
}
.stTextArea textarea:focus {
border-color: #6C63FF !important;
box-shadow: 0 0 0 2px rgba(108, 99, 255, 0.3) !important;
}
/* Beautiful Button Gradient */
.stButton > button {
background: linear-gradient(135deg, #6C63FF 0%, #3B33D4 100%) !important;
color: white !important;
border-radius: 12px !important;
border: none !important;
padding: 0.6rem 1.5rem !important;
font-weight: 600 !important;
transition: all 0.3s ease !important;
box-shadow: 0 4px 15px rgba(108, 99, 255, 0.25) !important;
}
.stButton > button:hover {
transform: translateY(-2px);
box-shadow: 0 6px 20px rgba(108, 99, 255, 0.4) !important;
}
/* Glassmorphism Metric Cards */
div[data-testid="stMetric"] {
background-color: rgba(22, 27, 34, 0.7);
border: 1px solid rgba(255, 255, 255, 0.05);
padding: 1.5rem;
border-radius: 16px;
box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.2);
backdrop-filter: blur(12px);
-webkit-backdrop-filter: blur(12px);
transition: transform 0.2s ease;
}
div[data-testid="stMetric"]:hover {
transform: translateY(-4px);
}
/* Fancy Header Gradient */
h1 {
background: -webkit-linear-gradient(45deg, #A78BFA, #6C63FF);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-weight: 800 !important;
margin-bottom: 0rem !important;
padding-bottom: 1rem;
}
/* File Uploader tweaking */
div[data-testid="stFileUploader"] {
background-color: rgba(22, 27, 34, 0.4);
border-radius: 12px;
padding: 1.5rem;
border: 1px dashed rgba(255, 255, 255, 0.15);
transition: all 0.3s ease;
}
div[data-testid="stFileUploader"]:hover {
border-color: #6C63FF;
background-color: rgba(108, 99, 255, 0.05);
}
/* Subheaders */
h3 {
color: #e6edf3 !important;
font-weight: 600 !important;
}
</style>
""", unsafe_allow_html=True)
st.title("🛡️ HalluciGuard")
st.write("Research-grade LLM Hallucination Detection using NLI and Cross-Encoders.")
# 2. Define the Backend URL
API_URL = "http://127.0.0.1:8000/api/v1/score"
# 3. Create the Input Forms
st.subheader("Test an LLM Output")
# Initialize session state for context text and uploaded file tracking
if "context_text" not in st.session_state:
st.session_state.context_text = ""
if "uploaded_filename" not in st.session_state:
st.session_state.uploaded_filename = None
uploaded_file = st.file_uploader("Upload a document to use as Ground Truth (Optional)", type=["pdf"])
if uploaded_file is not None:
# Only process the file if it's a new upload or newly selected
if st.session_state.uploaded_filename != uploaded_file.name:
try:
reader = PyPDF2.PdfReader(uploaded_file)
extracted_text = ""
for page in reader.pages:
text = page.extract_text()
if text:
extracted_text += text + "\n"
if not extracted_text.strip():
st.warning("Could not extract text. Please ensure the PDF is text-searchable and not a scanned image.")
else:
st.session_state.context_text = extracted_text.strip()
st.session_state.uploaded_filename = uploaded_file.name
except Exception as e:
st.error(f"Error reading PDF: {e}")
context = st.text_area(
"Source Context (The factual ground truth):",
key="context_text",
height=150,
placeholder="e.g., The Q3 financial report states that the company's revenue grew by 15%, reaching $50 million."
)
llm_output = st.text_area(
"LLM Output (The generated response to evaluate):",
height=100,
placeholder="e.g., The company had a great Q3, bringing in $60 million in revenue."
)
# 4. The Action Button
if st.button("Detect Hallucination", type="primary", use_container_width=True):
if not context or not llm_output:
st.warning("⚠️ Please provide both a context and an LLM output to test.")
else:
# Show a premium animated status indicator while the API processes
with st.status("🔍 Extracting text and structuring context...", expanded=False) as status:
st.write("⏳ Creating overlapping sequence chunks (evading 512-token limit)...")
st.write("🧠 Evaluating chunk semantics against DeBERTa-v3 cross-encoder...")
try:
# Send data to our FastAPI backend
response = requests.post(
API_URL,
json={"context": context, "llm_output": llm_output}
)
response.raise_for_status() # Throw an error if the API crashes
status.update(label="✅ Analysis Complete & Scores Aggregated!", state="complete", expanded=True)
data = response.json()
results = data.get("results", {})
# 5. Display the Results
st.markdown("---")
# Big visual alert
if results.get("is_hallucination"):
st.error("🚨 **HALLUCINATION DETECTED (Contradiction > 60%)**")
else:
st.success("✅ **FACTUALLY CONSISTENT**")
# Display exact percentages cleanly
st.subheader("NLI Confidence Scores")
contradiction_score = results.get('contradiction_score', 0)
entailment_score = results.get('entailment_score', 0)
neutral_score = results.get('neutral_score', 0)
# Dynamic Plain-English Summary
if contradiction_score > 15:
st.warning("🚨 Warning: The AI is explicitly contradicting the source material.")
elif neutral_score > 50:
st.warning("⚠️ Note: The AI is going off-script. It is bringing in outside knowledge not found in your source context.")
elif entailment_score > 50 and contradiction_score < 5:
st.success("✅ Excellent: The AI is sticking strictly to the facts provided.")
# Generate dynamic contextual captions based on percentages
if contradiction_score >= 60:
c_text = "- Meaning: The AI is heavily contradicting the source facts. Critical hallucination risk."
elif contradiction_score > 10:
c_text = "- Meaning: Some distinct parts of the output directly oppose the source material."
else:
c_text = "- Meaning: The AI is largely staying true to the facts with zero serious contradictions."
if entailment_score >= 80:
e_text = "- Meaning: Excellent! The AI is strictly summarizing the facts given to it."
elif entailment_score > 40:
e_text = "- Meaning: The AI includes a fair mix of factual summaries alongside some conversational filler."
else:
e_text = "- Meaning: Very little of the output is strictly supported by the source text."
if neutral_score >= 60:
n_text = "- Meaning: The AI is heavily going off-script and bringing in outside knowledge/questions."
elif neutral_score > 10:
n_text = "- Meaning: The AI is adding a moderate amount of conversational filler or harmless additions."
else:
n_text = "- Meaning: The AI is strictly sticking to the provided context with almost no unverified additions."
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Contradiction", f"{contradiction_score}%")
st.caption(c_text)
with col2:
st.metric("Entailment", f"{entailment_score}%")
st.caption(e_text)
with col3:
st.metric("Neutral", f"{neutral_score}%")
st.caption(n_text)
except requests.exceptions.ConnectionError:
st.error("🔌 Failed to connect to the backend. Is your FastAPI server running on port 8000?")
except Exception as e:
st.error(f"An error occurred: {e}") |