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import os
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import json
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import requests
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from dotenv import load_dotenv
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from backend.langchain_tools import llm, deepseek_tool
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import re
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from backend.api.claims import classify_claim
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from backend.api.tone_intent import detect_tone_and_intent
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from langchain.prompts import PromptTemplate
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from langchain_core.runnables import RunnableSequence
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import wikipedia
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import logging
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from bs4 import BeautifulSoup
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from sumy.parsers.plaintext import PlaintextParser
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from sumy.nlp.tokenizers import Tokenizer
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from sumy.summarizers.lsa import LsaSummarizer
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import nltk
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from nltk.corpus import stopwords
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from nltk.tokenize import word_tokenize
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load_dotenv()
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SERPER_API_KEY = os.getenv("SERPER_API_KEY")
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USER_AGENT = {"User-Agent": "Mozilla/5.0"}
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session = requests.Session()
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session.headers.update(USER_AGENT)
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nltk_data_dir = "/tmp/nltk_data"
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os.makedirs(nltk_data_dir, exist_ok=True)
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nltk.download("punkt", download_dir=nltk_data_dir)
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nltk.download("stopwords", download_dir=nltk_data_dir)
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nltk.data.path.append(nltk_data_dir)
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logging.basicConfig(level=logging.INFO)
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fact_check_prompt = PromptTemplate.from_template("""
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You are an expert fact-checker. Your task is to determine whether the following claim is true or false based on the information available from external sources (Wikipedia and Google search). The claim has already been classified into categories and analyzed for tone/intent.
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Claim: "{claim}"
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Classification: "{classification}"
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Tone: "{tone}"
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Intent: "{intent}"
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Wikipedia Evidence: "{wikipedia_evidence}"
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Serper Evidence: "{serper_evidence}"
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Fact-checking Task:
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- Based on the evidence from Wikipedia and Serper search results, classify the claim as:
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1. True
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2. False
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3. Uncertain (when there is insufficient evidence)
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Respond in JSON format:
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{{
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"claim": "{claim}",
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"classification": "{classification}",
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"tone": "{tone}",
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"intent": "{intent}",
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"fact_check_result": "<True/False/Uncertain/Misleading/Exaggerated/Partially True/Inconclusive:: >",
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"evidence": "<evidence supporting or contradicting the claim>",
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"sources": [
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{{
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"source": "Wikipedia",
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"url": "<url>"
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}},
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{{
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"source": "Search result",
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"url": "<serper_url>"
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}},
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{{
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"source": "LLM",
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"url": "<llm_inferred_url>"
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}},
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...
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],
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"reasoning": "<reasoning for the fact-checking result>"
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}}
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""")
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fact_check_chain = fact_check_prompt | llm
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def search_wikipedia(query):
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try:
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results = wikipedia.search(query, results=5)
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if not results:
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return {"error": "No results found on Wikipedia."}
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summaries = []
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for title in results:
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try:
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page = wikipedia.page(title)
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summaries.append({
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"title": title,
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"summary": page.summary,
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"url": page.url
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})
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except wikipedia.exceptions.DisambiguationError as e:
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summaries.append({"error": f"Disambiguation: {str(e)}"})
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except Exception as e:
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summaries.append({"error": str(e)})
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return summaries
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except Exception as e:
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return {"error": str(e)}
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def fetch_search_results(query, sentences_count=3):
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url = "https://google.serper.dev/search"
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headers = {"X-API-KEY": SERPER_API_KEY}
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data = {"q": query, "num": 10}
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try:
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response = session.post(url, json=data, headers=headers)
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response.raise_for_status()
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results = response.json()
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if not results.get("organic"):
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return "Error: No search results found."
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sources = []
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for result in results.get("organic", [])[:5]:
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source_url = result.get("url") or result.get("link")
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sources.append(source_url)
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summary = fetch_and_summarize(sources[0], sentences_count)
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return summary, sources
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except requests.exceptions.RequestException as e:
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return f"Error fetching search results: {e}"
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def fetch_and_summarize(url, sentences_count=3):
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try:
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response = session.get(url, timeout=10)
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response.raise_for_status()
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soup = BeautifulSoup(response.text, "html.parser")
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paragraphs = soup.find_all("p")
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text = "\n".join([p.get_text() for p in paragraphs])
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if not text.strip():
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return "Error: No readable content found on the page."
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return summarize_text(text, sentences_count)
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except requests.exceptions.RequestException as e:
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return f"Error: Failed to fetch webpage ({str(e)})"
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except Exception as e:
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return f"Error: {str(e)}"
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def summarize_text(text, sentences_count=3):
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if not text.strip():
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return "Error: No text provided for summarization."
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parser = PlaintextParser.from_string(text, Tokenizer("english"))
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summarizer = LsaSummarizer()
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summary = summarizer(parser.document, sentences_count)
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return " ".join(str(sentence) for sentence in summary)
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def fact_check_claim(claim_text: str) -> dict:
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try:
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classification = classify_claim(claim_text)
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tone_intent = detect_tone_and_intent(claim_text)
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classification_type = classification.get("category", "Unknown")
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tone = tone_intent.get("tone", "Unknown")
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intent = tone_intent.get("intent", "Unknown")
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wikipedia_results = search_wikipedia(claim_text)
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serper_summary, serper_sources = fetch_search_results(claim_text)
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wikipedia_evidence = " ".join([r["summary"] for r in wikipedia_results if "summary" in r])
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sources = []
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for r in wikipedia_results:
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if "url" in r:
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sources.append({"source": "Wikipedia", "url": r["url"]})
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for url in serper_sources:
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sources.append({"source": "Serper", "url": url})
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try:
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fact_check_result = fact_check_chain.invoke({
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"claim": claim_text,
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"classification": classification_type,
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"tone": tone,
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"intent": intent,
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"wikipedia_evidence": wikipedia_evidence,
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"serper_evidence": serper_summary
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})
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result = json.loads(fact_check_result.content.strip())
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result["sources"] = sources
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return result
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except Exception as primary_error:
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logging.warning(f"Primary LLM failed: {primary_error}. Falling back to DeepSeek.")
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deepseek_prompt = fact_check_prompt.template.format(
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claim=claim_text,
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classification=classification_type,
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tone=tone,
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intent=intent,
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wikipedia_evidence=wikipedia_evidence,
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serper_evidence=serper_summary
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)
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deepseek_result = deepseek_tool.invoke({"input": deepseek_prompt})
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logging.info(f"Raw DeepSeek Output: {deepseek_result}")
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cleaned_output = re.sub(r"```(?:json)?\s*([\s\S]*?)\s*```", r"\1", deepseek_result.strip())
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result = json.loads(cleaned_output)
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result["sources"] = sources
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return result
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except Exception as final_error:
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logging.error(f"Total failure in fact_check_claim: {final_error}")
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return {"error": f"Fact-checking failed due to: {str(final_error)}"}
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