Create app.py

app.py

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+import streamlit as st
+import io
+import base64
+from PIL import Image
+import tempfile
+import os
+import json
+
+# Import functions from your backend file
+from backend import (
+    detect_clones,
+    error_level_analysis,
+    extract_exif_metadata,
+    noise_analysis,
+    manipulation_likelihood,
+    get_clone_explanation,
+    pil_to_base64
+)
+
+# Create temp directory
+TEMP_DIR = tempfile.mkdtemp()
+
+# Helper functions
+def save_uploaded_image(uploaded_file):
+    """Save a Streamlit uploaded file and return the path"""
+    temp_path = os.path.join(TEMP_DIR, "temp_analyze.jpg")
+    with open(temp_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    return temp_path
+
+# Streamlit UI
+st.set_page_config(page_title="Image Forensic & Fraud Detection Tool", layout="wide")
+
+st.title("Image Forensic & Fraud Detection Tool")
+st.write("Upload an image to analyze it for potential manipulation using various forensic techniques.")
+
+# Sidebar for uploading and basic controls
+with st.sidebar:
+    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        analyze_button = st.button("Analyze Image", type="primary")
+    
+    # Display probability meter if analysis has been done
+    if "probability" in st.session_state:
+        st.markdown("### Manipulation Probability")
+        st.progress(st.session_state.probability)
+        st.write(f"{st.session_state.probability*100:.1f}%")
+
+# Main content area with tabs
+if uploaded_file is not None:
+    # Display the original image
+    image = Image.open(uploaded_file)
+    
+    # Create tabs for different analyses
+    tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([
+        "Analysis Results", 
+        "Original Image", 
+        "Error Level Analysis", 
+        "Noise Analysis",
+        "Clone Detection",
+        "AI Detection Heatmap"
+    ])
+    
+    with tab2:
+        st.image(image, caption="Original Image")
+    
+    # Run analysis when button is clicked
+    if analyze_button:
+        # Save the image
+        temp_path = save_uploaded_image(uploaded_file)
+        
+        with st.spinner("Analyzing image..."):
+            try:
+                # Run all analyses
+                exif_result = extract_exif_metadata(temp_path)
+                manipulation_result = manipulation_likelihood(temp_path)
+                clone_result, clone_count = detect_clones(temp_path)
+                ela_image = error_level_analysis(temp_path)
+                noise_image = noise_analysis(temp_path)
+                
+                # Store probability in session state
+                st.session_state.probability = manipulation_result["probability"]
+                
+                # Display results in each tab
+                with tab1:
+                    st.markdown(f"""
+                    ## Manipulation Analysis Results
+
+                    **Overall Assessment: {manipulation_result['probability']*100:.1f}% likelihood of manipulation**
+
+                    {manipulation_result['explanation']}
+
+                    ### Clone Detection Analysis:
+                    Found {clone_count} potential cloned regions in the image.
+                    {get_clone_explanation(clone_count)}
+
+                    ### EXIF Metadata Analysis:
+                    {exif_result['summary']}
+
+                    Indicators found: {len(exif_result['indicators'])}
+                    """)
+                    
+                    if exif_result['indicators']:
+                        st.markdown("### Detailed indicators:")
+                        for indicator in exif_result['indicators']:
+                            st.markdown(f"- {indicator}")
+                    
+                    # Display EXIF data as expandable JSON
+                    with st.expander("View EXIF Metadata"):
+                        st.json(exif_result["metadata"])
+                
+                with tab3:
+                    st.markdown("""
+                    Error Level Analysis reveals differences in compression levels. Areas with different compression levels 
+                    often indicate modifications. Brighter regions in the visualization suggest potential manipulations.
+                    """)
+                    st.image(ela_image, caption="Error Level Analysis Result")
+                
+                with tab4:
+                    st.markdown("""
+                    Noise Analysis examines the noise patterns in the image. Inconsistent noise patterns often indicate 
+                    areas that have been manipulated or added from different sources.
+                    """)
+                    st.image(noise_image, caption="Noise Pattern Analysis")
+                
+                with tab5:
+                    st.markdown("""
+                    Clone Detection identifies duplicated areas within the image. Red and blue rectangles highlight 
+                    matching regions that may indicate copy-paste manipulation.
+                    """)
+                    st.image(clone_result, caption="Clone Detection Result")
+                
+                with tab6:
+                    st.markdown("""
+                    This heatmap highlights regions identified by our AI model as potentially manipulated.
+                    Red areas indicate suspicious regions with a higher likelihood of manipulation.
+                    """)
+                    st.image(manipulation_result["heatmap_image"], caption="AI-Detected Suspicious Regions")
+            
+            except Exception as e:
+                st.error(f"Error during analysis: {str(e)}")
+else:
+    st.info("Please upload an image to begin analysis.")