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requirements.txt

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+
+transformers==4.40.1
+torch
+pytesseract
+Pillow
+newspaper3k

streamlit_app.py

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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.")