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import os
import re
import time
import pandas as pd
import streamlit as st
from openfactcheck.base import OpenFactCheck
from openfactcheck.app.utils import metric_card
def extract_text(claim):
"""
Extracts text from a claim that might be a string formatted as a dictionary.
"""
# Try to extract text using regular expression if claim is a string formatted as a dictionary
match = re.search(r"'text': '([^']+)'", claim)
if match:
return match.group(1)
return claim # Return as is if no dictionary format detected
# Create a function to check a LLM response
def evaluate_response(ofc: OpenFactCheck):
"""
This function creates a Streamlit app to evaluate the factuality of a LLM response.
"""
# Initialize the response_evaluator
response_evaluator = ofc.ResponseEvaluator
# Initialize the solvers
st.session_state.claimprocessors = ofc.list_claimprocessors()
st.session_state.retrievers = ofc.list_retrievers()
st.session_state.verifiers = ofc.list_verifiers()
st.write("This is where you can check factuality of a LLM response.")
# Customize FactChecker
st.write("Customize FactChecker")
# Dropdown in three columns
col1, col2, col3 = st.columns(3)
with col1:
if "claimprocessor" not in st.session_state:
st.session_state.claimprocessor = st.selectbox(
"Select Claim Processor", list(st.session_state.claimprocessors)
)
else:
st.session_state.claimprocessor = st.selectbox(
"Select Claim Processor",
list(st.session_state.claimprocessors),
index=list(st.session_state.claimprocessors).index(st.session_state.claimprocessor),
)
with col2:
if "retriever" not in st.session_state:
st.session_state.retriever = st.selectbox("Select Retriever", list(st.session_state.retrievers))
else:
st.session_state.retriever = st.selectbox(
"Select Retriever",
list(st.session_state.retrievers),
index=list(st.session_state.retrievers).index(st.session_state.retriever),
)
with col3:
if "verifier" not in st.session_state:
st.session_state.verifier = st.selectbox("Select Verifier", list(st.session_state.verifiers))
else:
st.session_state.verifier = st.selectbox(
"Select Verifier",
list(st.session_state.verifiers),
index=list(st.session_state.verifiers).index(st.session_state.verifier),
)
# Input
if "input_text" not in st.session_state:
st.session_state.input_text = {
"text": st.text_area("Enter LLM response here", "This is a sample LLM response.")
}
else:
st.session_state.input_text = {
"text": st.text_area("Enter LLM response here", st.session_state.input_text["text"])
}
# Button to check factuality
if st.button("Check Factuality"):
with st.status("Checking factuality...", expanded=True) as status:
# Configure the pipeline
st.write("Configuring pipeline...")
ofc.init_pipeline_manually(
[st.session_state.claimprocessor, st.session_state.retriever, st.session_state.verifier]
)
st.write("Pipeline configured...")
# Evaluate the response
st.write("Evaluating response...")
response = response_evaluator.evaluate_streaming(st.session_state.input_text)
st.write("Response evaluated...")
status.update(label="Factuality checked...", state="complete", expanded=False)
# Display pipeline configuration
pipeline_str = " ┈➤ ".join(
[st.session_state.claimprocessor, st.session_state.retriever, st.session_state.verifier]
)
st.info(f"""**Pipeline**: \n{pipeline_str}""")
# Store the final response in the session state
st.session_state.final_response = None
col1, col2 = st.columns([3, 1])
with col1:
def process_stream(responses):
"""
Process each response from the stream as a simulated chat output.
This function yields each word from the formatted text of the response,
adding a slight delay to simulate typing in a chat.
"""
for response in responses:
if "claimprocessor" in response["solver_name"]:
# Extract response details
output_text = response["output"]
# Get the number of detected claims
detected_claims = output_text.get("claims", [])
# Generate formatted text with enumerated claims in Markdown format
formatted_text = "### Detected Claims\n"
formatted_text += "\n".join(
f"{i}. {extract_text(claim)}" for i, claim in enumerate(detected_claims, start=1)
)
formatted_text += "\n"
with col2:
metric_card(label="Detected Claims", value=len(detected_claims))
# Yield each word with a space and simulate typing by sleeping
for word in formatted_text.split(" "):
yield word + " "
time.sleep(0.01)
st.session_state.claimprocessor_flag = True
elif "retriever" in response["solver_name"]:
# Extract response details
output_text = response["output"]
questions = []
evidences = []
for _, claim_with_evidences in output_text.get("claims_with_evidences", {}).items():
for claim_with_evidence in claim_with_evidences:
questions.append(claim_with_evidence[0])
evidences.append(claim_with_evidence[1])
with col2:
metric_card(label="Retrieved Evidences", value=len(evidences))
elif "verifier" in response["solver_name"]:
# Extract response details
output_text = response["output"]
# Get detail
details = output_text.get("detail", None)
if details is None:
detail_text = "The verifier did not provide any detail. Please use other verifiers for more information."
else:
detail_text = ""
# Apply color to the claim based on factuality
claims = 0
false_claims = 0
true_claims = 0
controversial_claims = 0
unverified_claims = 0
for i, detail in enumerate(details):
# Get factuality information
factuality = str(detail.get("factuality", None))
if factuality is not None:
claim = detail.get("claim", "")
if factuality == "-1" or factuality == "False":
detail_text += f'##### :red[{str(i+1) + ". " + extract_text(claim)}]'
detail_text += "\n"
claims += 1
false_claims += 1
elif factuality == "1" or factuality == "True":
detail_text += f'##### :green[{str(i+1) + ". " + extract_text(claim)}]'
detail_text += "\n"
claims += 1
true_claims += 1
elif factuality == "0":
detail_text += f'##### :orange[{str(i+1) + ". " + extract_text(claim)}]'
detail_text += "\n"
claims += 1
controversial_claims += 1
else:
detail_text += f'##### :purple[{str(i+1) + ". " + extract_text(claim)}]'
detail_text += "\n"
claims += 1
unverified_claims += 1
else:
st.error("Factuality not found in the verifier output.")
# Add error information
if detail.get("error", None) != "None":
detail_text += f"- **Error**: {detail.get('error', '')}"
detail_text += "\n"
# Add reasoning information
if detail.get("reasoning", None) != "None":
detail_text += f"- **Reasoning**: {detail.get('reasoning', '')}"
detail_text += "\n"
# Add correction
if detail.get("correction", None) != "":
detail_text += f"- **Correction**: {detail.get('correction', '')}"
detail_text += "\n"
# Add evidence
if detail.get("evidences", None) != "":
evidence_text = ""
questions_evidences = {}
for evidence in detail.get("evidences", []):
if evidence[0] not in questions_evidences:
questions_evidences[evidence[0]] = []
questions_evidences[evidence[0]].append(evidence[1])
for question, evidences in questions_evidences.items():
evidence_text += f"- **Evidences against Question**: :orange[{question}]"
evidence_text += "\n"
for evidence in evidences:
evidence_text += f" - {evidence}\n"
detail_text += evidence_text
# Generate formatted text with the overall factuality in Markdown format
formatted_text = "### Factuality Detail\n"
formatted_text += "Factuality of each claim is color-coded (:red[red means false], :green[green means true], :orange[orange means controversial], :violet[violet means unverified]).\n"
formatted_text += f"{detail_text}\n"
formatted_text += "\n"
# Get the number of true and false claims
with col2:
metric_card(
label="Supported Claims",
value=true_claims,
background_color="#D1ECF1",
border_left_color="#17A2B8",
)
metric_card(
label="Conflicted Claims",
value=false_claims,
background_color="#D1ECF1",
border_left_color="#17A2B8",
)
metric_card(
label="Controversial Claims",
value=controversial_claims,
background_color="#D1ECF1",
border_left_color="#17A2B8",
)
metric_card(
label="Unverified Claims",
value=unverified_claims,
background_color="#D1ECF1",
border_left_color="#17A2B8",
)
# Get overall factuality (label)
overall_factuality = output_text.get("label", "Unknown")
with col2:
with st.container():
if overall_factuality:
metric_card(
label="Overall Factuality",
value="True",
background_color="#D4EDDA",
border_left_color="#28A745",
)
elif not overall_factuality:
metric_card(
label="Overall Factuality",
value="False",
background_color="#F8D7DA",
border_left_color="#DC3545",
)
# Get overall credibility (score)
overall_credibility = true_claims / claims if claims > 0 else 0
with col2:
if overall_credibility > 0.75 and overall_credibility <= 1:
# Green background
metric_card(
label="Overall Credibility",
value=f"{overall_credibility:.2%}",
background_color="#D4EDDA",
border_left_color="#28A745",
)
elif overall_credibility > 0.25 and overall_credibility <= 0.75:
# Yellow background
metric_card(
label="Overall Credibility",
value=f"{overall_credibility:.2%}",
background_color="#FFF3CD",
border_left_color="#FFC107",
)
else:
# Red background
metric_card(
label="Overall Credibility",
value=f"{overall_credibility:.2%}",
background_color="#F8D7DA",
border_left_color="#DC3545",
)
# Yield each word with a space and simulate typing by sleeping
for word in formatted_text.split(" "):
yield word + " "
time.sleep(0.01)
st.write_stream(process_stream(response))
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