app.py

import streamlit as st
from PIL import Image, ImageDraw, ImageFont, ExifTags
import cv2
import numpy as np
from skimage.metrics import structural_similarity as ssim
import pandas as pd
import fitz  # PyMuPDF
import docx
from difflib import HtmlDiff, SequenceMatcher
import os
import uuid
import logging
import requests
import zipfile
from typing import Union, Dict, Any
import time     
import base64
import io
from io import BytesIO

icon_url = "https://raw.githubusercontent.com/noumanjavaid96/ai-as-an-api/refs/heads/master/image%20(39).png"

response = requests.get(icon_url)
icon_image = Image.open(BytesIO(response.content))


# Page configuration
st.set_page_config(
    page_title="DeepFake Detection",
    page_icon=icon_image
    # initial_sidebar_state="expanded"
)

# Custom CSS
st.html(
    """
    <style>
    .title-container {
        display: flex;
        align-items: center;
        margin-bottom: 20px; /* Add margin for spacing */
    }
    .title-icon {
        width: 50px;
        height: 50px;
        margin-right: 10px; /* Add margin between icon and title */
    }
    .title-text {
        font-size: 36px; /* Adjust font size as needed */
        font-weight: bold;
    }
    </style>
    """,

)
st.markdown(
    f"""
    <div class="title-container">
        <img class="title-icon" src="{icon_url}" alt="Icon">
        <div class="title-text">Centurion </div>
    </div>
    """,
    unsafe_allow_html=True
)


st.markdown("---")

# Constants
UPLOAD_DIR = "uploaded_files"
NVIDIA_API_KEY = "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"  # Store API key securely"

# Create upload directory if it doesn't exist
if not os.path.exists(UPLOAD_DIR):
    os.makedirs(UPLOAD_DIR)

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def main():
    # Title and icon using HTML for better control

    st.markdown(
        """
        <div class="title-container">
            <img class="title-icon" src="https://raw.githubusercontent.com/noumanjavaid96/ai-as-an-api/refs/heads/master/image%20(39).png">
        </div>
        """,
        unsafe_allow_html=True,
    )

    # Create tabs for different functionalities
    tabs = st.tabs(["Image Comparison", "Image Comparison with Watermarking", "Document Comparison Tool"])

    with tabs[0]:
        image_comparison()

    with tabs[1]:
        image_comparison_and_watermarking()

    with tabs[2]:
        document_comparison_tool()
        

def image_comparison():
    st.header("Image Comparison")
    st.write("""
    Upload two images to compare them and find differences.
    """)

    # Upload images
    col1, col2 = st.columns(2)

    with col1:
        st.subheader("Original Image")
        uploaded_file1 = st.file_uploader("Choose the original image", type=["png", "jpg", "jpeg"], key="comp1")

    with col2:
        st.subheader("Image to Compare")
        uploaded_file2 = st.file_uploader("Choose the image to compare", type=["png", "jpg", "jpeg"], key="comp2")

    if uploaded_file1 is not None and uploaded_file2 is not None:
        # Read images
        image1 = Image.open(uploaded_file1)
        image2 = Image.open(uploaded_file2)

        # Convert images to OpenCV format
        img1 = cv2.cvtColor(np.array(image1), cv2.COLOR_RGB2BGR)
        img2 = cv2.cvtColor(np.array(image2), cv2.COLOR_RGB2BGR)

        # Resize images to the same size if necessary
        if img1.shape != img2.shape:
            st.warning("Images are not the same size. Resizing the second image to match the first.")
            img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))

        # Convert to grayscale
        gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
        gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

        # Compute SSIM between two images
        score, diff = ssim(gray1, gray2, full=True)
        st.write(f"**Structural Similarity Index (SSIM): {score:.4f}**")
        diff = (diff * 255).astype("uint8")

        # Threshold the difference image
        thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

        # Find contours of the differences
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        # Create copies of the images to draw on
        img1_diff = img1.copy()
        img2_diff = img2.copy()

        # Draw rectangles around differences
        for cnt in contours:
            x, y, w, h = cv2.boundingRect(cnt)
            cv2.rectangle(img1_diff, (x, y), (x + w, y + h), (0, 0, 255), 2)
            cv2.rectangle(img2_diff, (x, y), (x + w, y + h), (0, 0, 255), 2)

        # Convert images back to RGB for displaying with Streamlit
        img1_display = cv2.cvtColor(img1_diff, cv2.COLOR_BGR2RGB)
        img2_display = cv2.cvtColor(img2_diff, cv2.COLOR_BGR2RGB)
        diff_display = cv2.cvtColor(diff, cv2.COLOR_GRAY2RGB)
        thresh_display = cv2.cvtColor(thresh, cv2.COLOR_GRAY2RGB)

        # Display images
        st.write("## Results")
        st.write("Differences are highlighted in red boxes.")

        st.image([img1_display, img2_display], caption=["Original Image with Differences", "Compared Image with Differences"], width=300)

        st.write("## Difference Image")
        st.image(diff_display, caption="Difference Image", width=300)

        st.write("## Thresholded Difference Image")
        st.image(thresh_display, caption="Thresholded Difference Image", width=300)

    else:
        st.info("Please upload both images.")

def image_comparison_and_watermarking():
    st.header("Image Comparison and Watermarking")
    st.write("""
    Upload two images to compare them, find differences, add a watermark, and compare metadata.
    """)

    # Upload images
    st.subheader("Upload Images")
    col1, col2 = st.columns(2)

    with col1:
        st.subheader("Original Image")
        uploaded_file1 = st.file_uploader("Choose the original image", type=["png", "jpg", "jpeg"], key="wm1")

    with col2:
        st.subheader("Image to Compare")
        uploaded_file2 = st.file_uploader("Choose the image to compare", type=["png", "jpg", "jpeg"], key="wm2")

    watermark_text = st.text_input("Enter watermark text (optional):", value="")

    if uploaded_file1 is not None and uploaded_file2 is not None:
        # Read images
        image1 = Image.open(uploaded_file1).convert("RGB")
        image2 = Image.open(uploaded_file2).convert("RGB")

        # Display original images
        st.write("### Uploaded Images")
        st.image([image1, image2], caption=["Original Image", "Image to Compare"], width=300)

        # Add watermark if text is provided
        if watermark_text:
            st.write("### Watermarked Original Image")
            image1_watermarked = add_watermark(image1, watermark_text)
            st.image(image1_watermarked, caption="Original Image with Watermark", width=300)
        else:
            image1_watermarked = image1.copy()

        # Convert images to OpenCV format
        img1 = cv2.cvtColor(np.array(image1_watermarked), cv2.COLOR_RGB2BGR)
        img2 = cv2.cvtColor(np.array(image2), cv2.COLOR_RGB2BGR)

        # Resize images to the same size if necessary
        if img1.shape != img2.shape:
            st.warning("Images are not the same size. Resizing the second image to match the first.")
            img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))

        # Convert to grayscale
        gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
        gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

        # Compute SSIM between two images
        score, diff = ssim(gray1, gray2, full=True)
        st.write(f"**Structural Similarity Index (SSIM): {score:.4f}**")
        diff = (diff * 255).astype("uint8")

        # Threshold the difference image
        thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

        # Find contours of the differences
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        # Create copies of the images to draw on
        img1_diff = img1.copy()
        img2_diff = img2.copy()

        # Draw rectangles around differences
        for cnt in contours:
            x, y, w, h = cv2.boundingRect(cnt)
            cv2.rectangle(img1_diff, (x, y), (x + w, y + h), (0, 0, 255), 2)
            cv2.rectangle(img2_diff, (x, y), (x + w, y + h), (0, 0, 255), 2)

        # Convert images back to RGB for displaying with Streamlit
        img1_display = cv2.cvtColor(img1_diff, cv2.COLOR_BGR2RGB)
        img2_display = cv2.cvtColor(img2_diff, cv2.COLOR_BGR2RGB)
        diff_display = cv2.cvtColor(diff, cv2.COLOR_GRAY2RGB)
        thresh_display = cv2.cvtColor(thresh, cv2.COLOR_GRAY2RGB)

        # Display images with differences highlighted
        st.write("## Results")
        st.write("Differences are highlighted in red boxes.")

        st.image([img1_display, img2_display], caption=["Original Image with Differences", "Compared Image with Differences"], width=300)

        st.write("## Difference Image")
        st.image(diff_display, caption="Difference Image", width=300)

        st.write("## Thresholded Difference Image")
        st.image(thresh_display, caption="Thresholded Difference Image", width=300)

        # Metadata comparison
        st.write("## Metadata Comparison")
        metadata1 = get_metadata(image1)
        metadata2 = get_metadata(image2)

        if metadata1 and metadata2:
            metadata_df = compare_metadata(metadata1, metadata2)
            if metadata_df is not None:
                st.write("### Metadata Differences")
                st.dataframe(metadata_df)
            else:
                st.write("No differences in metadata.")
        else:
            st.write("Metadata not available for one or both images.")

    else:
        st.info("Please upload both images.")

def add_watermark(image, text):
    # Create a blank image for the text with transparent background
    txt = Image.new('RGBA', image.size, (255, 255, 255, 0))
    draw = ImageDraw.Draw(txt)

    # Choose a font and size
    font_size = max(20, image.size[0] // 20)
    try:
        font = ImageFont.truetype("arial.ttf", font_size)
    except IOError:
        font = ImageFont.load_default()

    # Calculate text bounding box
    bbox = font.getbbox(text)
    textwidth = bbox[2] - bbox[0]
    textheight = bbox[3] - bbox[1]

    # Position the text at the bottom right
    x = image.size[0] - textwidth - 10
    y = image.size[1] - textheight - 10

    # Draw text with semi-transparent fill
    draw.text((x, y), text, font=font, fill=(255, 255, 255, 128))

    # Combine the original image with the text overlay
    watermarked = Image.alpha_composite(image.convert('RGBA'), txt)

    return watermarked.convert('RGB')

def get_metadata(image):
    exif_data = {}
    info = image.getexif()
    if info:
        for tag, value in info.items():
            decoded = ExifTags.TAGS.get(tag, tag)
            exif_data[decoded] = value
    return exif_data

def compare_metadata(meta1, meta2):
    keys = set(meta1.keys()).union(set(meta2.keys()))
    data = []
    for key in keys:
        value1 = meta1.get(key, "Not Available")
        value2 = meta2.get(key, "Not Available")
        if value1 != value2:
            data.append({"Metadata Field": key, "Original Image": value1, "Compared Image": value2})
    if data:
        df = pd.DataFrame(data)
        return df
    else:
        return None

def document_comparison_tool():
    st.header("📄 Advanced Document Comparison Tool")
    st.markdown("### Compare documents and detect changes with AI-powered OCR")

    # Sidebar settings
    with st.sidebar:
        st.header("ℹ️ About")
        st.markdown("""
        This tool allows you to:
        - Compare PDF and Word documents
        - Process images using NVIDIA's OCR
        - Detect and highlight changes
        - Generate similarity metrics
        """)

        st.header("🛠️ Settings")
        show_metadata = st.checkbox("Show Metadata", value=True, key='doc_show_metadata')
        show_detailed_diff = st.checkbox("Show Detailed Differences", value=True, key='doc_show_detailed_diff')

    # Main content
    col1, col2 = st.columns(2)

    with col1:
        st.markdown("### Original Document")
        original_file = st.file_uploader(
            "Upload original document",
            type=["pdf", "docx", "jpg", "jpeg", "png"],
            key='doc_original_file',
            help="Supported formats: PDF, DOCX, JPG, PNG"
        )

    with col2:
        st.markdown("### Modified Document")
        modified_file = st.file_uploader(
            "Upload modified document",
            type=["pdf", "docx", "jpg", "jpeg", "png"],
            key='doc_modified_file',
            help="Supported formats: PDF, DOCX, JPG, PNG"
        )

    if original_file and modified_file:
        try:
            with st.spinner("Processing documents..."):
                # Initialize OCR handler
                ocr_handler = NVIDIAOCRHandler()

                # Process files
                original_file_path = save_uploaded_file(original_file)
                modified_file_path = save_uploaded_file(modified_file)

                # Extract text based on file type
                original_ext = os.path.splitext(original_file.name)[1].lower()
                modified_ext = os.path.splitext(modified_file.name)[1].lower()

                # Process original document
                if original_ext in ['.jpg', '.jpeg', '.png']:
                    original_result = ocr_handler.process_image(original_file_path, f"{UPLOAD_DIR}/original_ocr")
                    with open(f"{UPLOAD_DIR}/original_ocr/text.txt", "r") as f:
                        original_text = f.read()
                elif original_ext == '.pdf':
                    original_text = extract_text_pdf(original_file_path)
                else:
                    original_text = extract_text_word(original_file_path)

                # Process modified document
                if modified_ext in ['.jpg', '.jpeg', '.png']:
                    modified_result = ocr_handler.process_image(modified_file_path, f"{UPLOAD_DIR}/modified_ocr")
                    with open(f"{UPLOAD_DIR}/modified_ocr/text.txt", "r") as f:
                        modified_text = f.read()
                elif modified_ext == '.pdf':
                    modified_text = extract_text_pdf(modified_file_path)
                else:
                    modified_text = extract_text_word(modified_file_path)

                # Calculate similarity
                similarity_score = calculate_similarity(original_text, modified_text)

                # Display results
                st.markdown("### 📊 Analysis Results")

                metrics_col1, metrics_col2, metrics_col3 = st.columns(3)
                with metrics_col1:
                    st.metric("Similarity Score", f"{similarity_score:.2%}")
                with metrics_col2:
                    st.metric("Changes Detected", "Yes" if similarity_score < 1 else "No")
                with metrics_col3:
                    st.metric("Processing Status", "Complete ✅")

                if show_detailed_diff:
                    st.markdown("### 🔍 Detailed Comparison")
                    diff_html = compare_texts(original_text, modified_text)
                    st.components.v1.html(diff_html, height=600, scrolling=True)

                # Download results
                st.markdown("### 💾 Download Results")
                if st.button("Generate Report"):
                    with st.spinner("Generating report..."):
                        # Simulate report generation
                        time.sleep(2)
                        st.success("Report generated successfully!")
                        st.download_button(
                            label="Download Report",
                            data=diff_html,
                            file_name="comparison_report.html",
                            mime="text/html"
                        )

        except Exception as e:
            st.error(f"An error occurred: {str(e)}")
            logger.error(f"Error processing documents: {str(e)}")
    else:
        st.info("👆 Please upload both documents to begin comparison")

class NVIDIAOCRHandler:
    def __init__(self):
        self.api_key = NVIDIA_API_KEY
        self.nvai_url = "https://ai.api.nvidia.com/v1/cv/nvidia/ocdrnet"
        self.assets_url = "https://api.nvcf.nvidia.com/v2/nvcf/assets"
        self.header_auth = f"Bearer {self.api_key}"

    def upload_asset(self, input_data: bytes, description: str) -> uuid.UUID:
        try:
            with st.spinner("Uploading document to NVIDIA OCR service..."):
                headers = {
                    "Authorization": self.header_auth,
                    "Content-Type": "application/json",
                    "accept": "application/json",
                }
                s3_headers = {
                    "x-amz-meta-nvcf-asset-description": description,
                    "content-type": "image/jpeg",
                }
                payload = {"contentType": "image/jpeg", "description": description}
                
                response = requests.post(self.assets_url, headers=headers, json=payload, timeout=30)
                response.raise_for_status()
                
                upload_data = response.json()
                response = requests.put(
                    upload_data["uploadUrl"],
                    data=input_data,
                    headers=s3_headers,
                    timeout=300,
                )
                response.raise_for_status()
                return uuid.UUID(upload_data["assetId"])
        except Exception as e:
            st.error(f"Error uploading asset: {str(e)}")
            raise

    def process_image(self, image_path: str, output_dir: str) -> Dict[str, Any]:
        try:
            with st.spinner("Processing document with OCR..."):
                with open(image_path, "rb") as f:
                    asset_id = self.upload_asset(f.read(), "Input Image")

                inputs = {"image": f"{asset_id}", "render_label": False}
                asset_list = f"{asset_id}"
                headers = {
                    "Content-Type": "application/json",
                    "NVCF-INPUT-ASSET-REFERENCES": asset_list,
                    "NVCF-FUNCTION-ASSET-IDS": asset_list,
                    "Authorization": self.header_auth,
                }

                response = requests.post(self.nvai_url, headers=headers, json=inputs)
                response.raise_for_status()

                zip_path = f"{output_dir}.zip"
                with open(zip_path, "wb") as out:
                    out.write(response.content)

                with zipfile.ZipFile(zip_path, "r") as z:
                    z.extractall(output_dir)

                os.remove(zip_path)
                return {
                    "status": "success",
                    "output_directory": output_dir,
                    "files": os.listdir(output_dir)
                }
        except Exception as e:
            st.error(f"Error processing image: {str(e)}")
            raise

def save_uploaded_file(uploaded_file):
    file_path = os.path.join(UPLOAD_DIR, uploaded_file.name)
    with open(file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
    return file_path

def extract_text_pdf(file_path):
    doc = fitz.open(file_path)
    text = ""
    for page in doc:
        text += page.get_text()
    return text

def extract_text_word(file_path):
    doc = docx.Document(file_path)
    text = "\n".join([para.text for para in doc.paragraphs])
    return text

def compare_texts(text1, text2):
    differ = HtmlDiff()
    return differ.make_file(
        text1.splitlines(),
        text2.splitlines(),
        fromdesc="Original",
        todesc="Modified",
        context=True,
        numlines=2
    )

def draw_bounding_box(image, vertices, confidence, is_deepfake):
    img = np.array(image)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    # Extract coordinates
    x1, y1 = int(vertices[0]['x']), int(vertices[0]['y'])
    x2, y2 = int(vertices[1]['x']), int(vertices[1]['y'])
    
    # Calculate confidence percentages
    deepfake_conf = is_deepfake * 100
    bbox_conf = confidence * 100
    
    # Choose color based on deepfake confidence (red for high confidence)
    color = (0, 0, 255) if deepfake_conf > 70 else (0, 255, 0)
    
    # Draw bounding box
    cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
    
    # Add text with confidence scores
    label = f"Deepfake ({deepfake_conf:.1f}%), Face ({bbox_conf:.1f}%)"
    cv2.putText(img, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
    
    # Convert back to RGB for Streamlit
    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

def process_image(image_bytes):
    """Process image through NVIDIA's deepfake detection API"""
    image_b64 = base64.b64encode(image_bytes).decode()
    
    headers = {
        "Authorization": f"Bearer {NVIDIA_API_KEY}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }
    
    payload = {
        "input": [f"data:image/png;base64,{image_b64}"]
    }
    
    try:
        response = requests.post(
            "https://ai.api.nvidia.com/v1/cv/hive/deepfake-image-detection",
            headers=headers,
            json=payload
        )
        response.raise_for_status()
        return response.json()
    except Exception as e:
        st.error(f"Error processing image: {str(e)}")
        return None

def main():
    st.title("Deepfake Detection")
st.markdown("""
<div style="background-color: #f9f9f9; padding: 10px; border-radius: 5px; border: 1px solid #ddd;">
    <h3 style="color: #ff6347;">NOTE:</h3>
    <p style="font-size: 14px; color: #333;">
        <span style="font-size: 16px; font-weight: bold;">In case</span> there would be no changes detected, the space would not show anything as a result, returning back empty results. For this POC.
    </p>
</div>
""", unsafe_allow_html=True)

uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
    image_bytes = uploaded_file.getvalue()
    image = Image.open(io.BytesIO(image_bytes))
    col1, col2 = st.columns(2)
    
    with col1:
            st.subheader("Original Image")
            st.image(image, use_container_width=True)
        
        # Process image
    with st.spinner("Analyzing image..."):
            result = process_image(image_bytes)
        
    if result and 'data' in result:
            data = result['data'][0]
        
    if 'bounding_boxes' in data:
                for box in data['bounding_boxes']:
                    # Draw bounding box on image
                    annotated_image = draw_bounding_box(
                        image,
                        box['vertices'],
                        box['bbox_confidence'],
                        box['is_deepfake']
                    )
                    
                with col2:
                    st.subheader("Analysis Result")
                    st.image(annotated_image, use_container_width=True)
                
                # Display confidence metrics
                deepfake_conf = box['is_deepfake'] * 100
                bbox_conf = box['bbox_confidence'] * 100
                
                st.write("### Detection Confidence")
                col3, col4 = st.columns(2)
                
                with col3:
                    st.metric("Deepfake Confidence", f"{deepfake_conf:.1f}%")
                    st.progress(deepfake_conf/100)
                
                with col4:
                    st.metric("Face Detection Confidence", f"{bbox_conf:.1f}%")
                    st.progress(bbox_conf/100)
                
                if deepfake_conf > 90:
                    st.error("⚠️ High probability of deepfake detected!")
                elif deepfake_conf > 70:
                    st.warning("⚠️ Moderate probability of deepfake detected!")
                else:
                    st.success("✅ Low probability of deepfake")
                
                # Display raw JSON data in expander
                with st.expander("View Raw JSON Response"):
                    st.json(result)
    
    else:
        st.warning("No faces detected in the image")
        
        def calculate_similarity(text1, text2):
            matcher = SequenceMatcher(None, text1, text2)
            return matcher.ratio()
            if __name__ == "__main__":
                main()