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import os
os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import pytesseract
from PIL import Image
import difflib
from newspaper import Article
model_name = "google/gemma-2b-it"
@st.cache_resource
def load_model():
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
return pipe
pipe = load_model()
st.set_page_config(page_title="Blog/Image Reviewer", layout="centered")
st.title("🧠 BlogChecker AI")
st.markdown("Paste blog text, upload an image, or give a URL — the AI will review and improve it.")
mode = st.radio("Choose Input Mode:", ["Text", "Image", "URL"])
input_text = ""
if mode == "Text":
input_text = st.text_area("Paste your blog here:", height=300)
elif mode == "Image":
uploaded_image = st.file_uploader("Upload an image with text", type=["png", "jpg", "jpeg"])
if uploaded_image is not None:
image = Image.open(uploaded_image)
input_text = pytesseract.image_to_string(image)
st.image(image, caption="Uploaded Image", width=400)
st.markdown("**Extracted Text:**")
st.text_area("OCR Text:", value=input_text, height=200)
elif mode == "URL":
blog_url = st.text_input("Paste blog/article URL:")
if blog_url:
try:
article = Article(blog_url)
article.download()
article.parse()
input_text = article.text
st.success("✅ Blog content extracted successfully.")
st.text_area("Extracted Blog Content:", value=input_text, height=300)
except:
st.error("⚠️ Failed to extract content from URL.")
def generate_review(original):
prompt = f"""
You are an AI blog reviewer. Improve the content below:
- Fix unclear, biased, or emotional language
- Correct grammar and spelling
- Identify and address sensitive or policy-violating content
- Suggest better alternatives
- Keep useful content unchanged
Blog:
{original}
"""
response = pipe(prompt, max_new_tokens=512, do_sample=True, temperature=0.7)
improved = response[0]['generated_text'].split("Blog:")[-1].strip()
return improved
def highlight_changes(original, improved):
d = difflib.Differ()
diff = list(d.compare(original.split(), improved.split()))
highlighted = ""
for word in diff:
if word.startswith("- "):
highlighted += f"~~{word[2:]}~~ "
elif word.startswith("+ "):
highlighted += f"**{word[2:]}** "
elif word.startswith(" "):
highlighted += word[2:] + " "
return highlighted
if st.button("🔍 Review Content"):
if input_text.strip():
with st.spinner("Reviewing..."):
improved_text = generate_review(input_text)
highlighted = highlight_changes(input_text, improved_text)
st.subheader("🧠 AI Suggestions:")
st.markdown(highlighted)
if st.button("✅ Accept Changes"):
st.subheader("✍️ Final Content:")
st.text_area("Updated Blog:", improved_text, height=300)
if st.button("🪄 Auto-Correct"):
st.success("✅ AI-corrected content:")
st.text_area("Auto-corrected Text:", improved_text, height=300)
else:
st.warning("⚠️ Please provide input text, image, or URL.")
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