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import os
from langchain_huggingface import HuggingFacePipeline
from langchain_huggingface.llms import HuggingFacePipeline
from langchain.agents import AgentType, initialize_agent, load_tools
from transformers import pipeline
SERPAPI_API_KEY = '1e2cf72abd974364e933d854720ec3704bb86196ee9a25267e53947192afada6'
from serpapi import GoogleSearch
params = {
"engine": "google",
"q": "ESPN",
"api_key": "1e2cf72abd974364e933d854720ec3704bb86196ee9a25267e53947192afada6"
}
search = GoogleSearch(params)
results = search.get_dict()
organic_results = results["organic_results"]
llm = HuggingFacePipeline(pipeline=pipeline("text2text-generation",
model="google/flan-t5-xl",
model_kwargs={"temperature":0.5},
huggingfacehub_api_token='<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>'
))
tools = load_tools(["serpapi", "llm-math"], llm=llm, serpapi_api_key=SERPAPI_API_KEY)
class fact_checking_pipeline:
fact_checking_pipeline = pipeline("text-classification",
model="typeform/distilbert-base-uncased-mnli")
#Configure Langchain Agent
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
class RefereeAgent:
def __init__(self):
self.serpapi_api_key = "1e2cf72abd974364e933d854720ec3704bb86196ee9a25267e53947192afada6"
self.fact_checking_pipeline = pipeline("text-classification",
model="typeform/distilbert-base-uncased-mnli")
def search_serpapi(self, query):
search_url = "https://serpapi.com/search"
params = {
"q": query,
"api_key": self.serpapi_api_key,
}
response = requests.get(search_url, params=params)
return response.json()
def extract_relevant_info(self, serpapi_results):
relevant_info = []
for result in serpapi_results.get('organic_results', []):
title = result.get('title')
snippet = result.get('snippet')
link = result.get('link')
relevant_info.append({'title': title, 'snippet': snippet, 'link': link})
return relevant_info
def fact_check(self, statement):
serpapi_results = self.search_serpapi(statement)
relevant_info = self.extract_relevant_info(serpapi_results)
if not relevant_info:
return "No relevant data found."
context = " ".join([info['snippet'] for info in relevant_info])
result = self.fact_checking_pipeline(f"{statement} [SEP] {context}")
label = result[0]['label']
score = result[0]['score']
if label == "ENTAILMENT":
return f"The statement is likely true. Confidence score: {score}"
else:
return f"The statement is likely false. Confidence score: {score}"
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