app.py

import os
import requests
import time
import csv
import pandas as pd
import kagglehub
import gradio as gr
from cerebras.cloud.sdk import Cerebras
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.schema import Document
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import numpy as np

# Initialize Cerebras API client
Cerekey = os.getenv("Kc")
client = Cerebras(api_key= Cerekey)

Newskey = os.getenv("Nk")

def get_latest_news(query):
    url = f"https://newsapi.org/v2/everything?q={query}&apiKey={Newskey}"
    response = requests.get(url)
    data = response.json()
    return [(article["title"], article["url"], article["source"]["name"]) for article in data.get("articles", [])[:2]]

def update_fact_checks_file(query):
    with open("fact_checks.txt", "w", encoding="utf-8") as file:
        file.write(f"{query}\n")

def create_faiss_retriever():
    if not os.path.exists("fact_checks.txt"):
        open("fact_checks.txt", "w").close()
    
    loader = TextLoader("fact_checks.txt")
    documents = loader.load()
    
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
    docs = text_splitter.split_documents(documents)
    
    embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    vector_store = FAISS.from_documents(docs, embedding_model)
    
    return vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 4})

def clear_fact_checks_file():
    open("fact_checks.txt", "w").close()

def fact_check_with_llama3(query):
    update_fact_checks_file(query)
    retriever = create_faiss_retriever()
    retrieved_docs = retriever.invoke(query)
    retrieved_texts = [doc.page_content for doc in retrieved_docs]
    news = get_latest_news(query)
    context_text = "\n".join(retrieved_texts)
    
    prompt = f"""
    Claim: {query}
    Context: {context_text}
    Based on the provided context, determine whether the claim is True, False, or Misleading. Provide a concise explanation and cite relevant sources. Don't mention any instance of your knowledge cut-off.
    """
    
    stream = client.chat.completions.create(
        messages=[{"role": "system", "content": prompt}],
        model="llama-3.3-70b",
        stream=True,
        max_completion_tokens=512,
        temperature=0.2,
        top_p=1
    )
    
    result = "".join(chunk.choices[0].delta.content or "" for chunk in stream)
    sources = "\n".join([f"{title} ({source}): {url}" for title, url, source in news])
    clear_fact_checks_file()
    return result, sources if sources else "No relevant sources found."

def map_politifact_label(label):
    label_mapping = {
        "pants-fire": "False",
        "false": "False",
        "half-true": "Misleading",
        "mostly-true": "True",
        "barely-true": "False",
        "true": "True"
    }
    return label_mapping.get(label.lower(), "Unknown")

def evaluate_politifact(csv_file):
    df = pd.read_csv(csv_file.name)
    results = []
    
    for index, row in df.iterrows():
        claim = row["sources_quote"]
        actual_label = map_politifact_label(row["fact"])  # Convert Politifact label to Facto equivalent
        start_time = time.time()
        facto_result, sources = fact_check_with_llama3(claim)
        time_taken = time.time() - start_time
        accuracy = "100" if facto_result.lower() == actual_label.lower() else "0"
        
        results.append([claim, facto_result, actual_label, time_taken, accuracy])
    
    results_df = pd.DataFrame(results, columns=["Claim", "Facto Verdict", "Politifact Verdict", "Time Taken (s)", "Accuracy"])
    output_csv = "fact_check_results.csv"
    results_df.to_csv(output_csv, index=False)
    
    return output_csv

def gradio_interface(csv_file):
    output_csv = evaluate_politifact(csv_file)
    return output_csv

gui = gr.Interface(
    fn=gradio_interface,
    inputs=gr.File(label="Upload Politifact CSV"),
    outputs=gr.File(label="Fact-Check Results CSV"),
    title="Facto - AI Fact-Checking System",
    description="Upload a CSV file with claims, and the system will verify them using Llama 3.3 and compare the results with Politifact."
)

gui.launch(debug=True)