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import streamlit as st
import os
import cv2
import torch
import torchaudio
import torchvision
import tensorflow as tf
from transformers import pipeline
from groq import Groq
from openai import OpenAI
# Set up the Groq client
client = Groq(api_key=os.environ.get("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"))
# Load a fake news detection model from Hugging Face
fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
# Streamlit UI
st.set_page_config(page_title="Fake News Detector", layout="wide")
st.title("📰 Fake News Detector")
# Tabs for Input and Results
tab1, tab2 = st.tabs(["Input", "Results"])
# Function to fetch real news links (mocked for now)
def fetch_real_news_links():
return ["https://www.bbc.com/news", "https://www.cnn.com", "https://www.reuters.com"]
with tab1:
st.sidebar.title("Select Input Type")
option = st.sidebar.radio("Choose an option", ["Text", "Image", "Video Link"])
if option == "Text":
news_text = st.text_area("Enter the news content to check:", height=200)
if st.button("Analyze News"):
if not news_text.strip():
st.warning("Please enter some text.")
else:
st.session_state["news_text"] = news_text
st.session_state["analyze"] = True
st.experimental_rerun()
elif option == "Image":
uploaded_file = st.file_uploader("Upload an image of news article", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
st.info("🔍 Image analysis coming soon!")
elif option == "Video Link":
video_url = st.text_input("Enter a video news link to check")
if st.button("Analyze Video"):
if not video_url.strip():
st.warning("Please enter a valid URL.")
else:
st.info("🔍 Video analysis coming soon!")
with tab2:
if st.session_state.get("analyze", False):
news_text = st.session_state.get("news_text", "")
with st.spinner("Analyzing..."):
# Check using Groq API
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": f"Classify this news as Real or Fake: {news_text}"}],
model="llama-3.3-70b-versatile",
stream=False,
)
groq_result = chat_completion.choices[0].message.content.strip().lower()
# Check using Hugging Face model
hf_result = fake_news_pipeline(news_text)[0]['label'].lower()
# Display result
if "fake" in groq_result or hf_result == "fake":
st.error("❌ This news is likely **Fake**!", icon="⚠️")
st.markdown('<style>div.stAlert {background-color: #ffdddd;}</style>', unsafe_allow_html=True)
conclusion = "The analysis suggests that this news might be fabricated or misleading. Please verify from credible sources."
elif "real" in groq_result or hf_result == "real":
st.success("✅ This news is likely **Real**!", icon="✅")
st.markdown('<style>div.stAlert {background-color: #ddffdd;}</style>', unsafe_allow_html=True)
conclusion = "The analysis indicates that this news appears to be credible and factual."
else:
st.info("🤔 The result is uncertain. Please verify from trusted sources.")
conclusion = "There is uncertainty in the classification. Further verification is recommended."
# Conclusion Section
st.subheader("📌 Conclusion")
st.write(conclusion)
# Display real news sources
st.subheader("🔗 Reliable News Sources")
for link in fetch_real_news_links():
st.markdown(f"[🔗 {link}]({link})")
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