app.py

import gradio as gr
from PIL import Image
import imagehash
import hashlib
import pandas as pd
import os

# -------------------------
# MD5 HASH FUNCTION
# -------------------------
def get_md5(file_path):
    with open(file_path, "rb") as f:
        return hashlib.md5(f.read()).hexdigest()

# -------------------------
# SIMILARITY SCORE FUNCTION
# -------------------------
def get_similarity_score(hash1, hash2):
    diff = hash1 - hash2
    similarity = (1 - diff / 64) * 100
    return round(similarity, 2)

# -------------------------
# MAIN FUNCTION
# -------------------------
def find_duplicates(files, filter_type):
    if not files:
        return [], pd.DataFrame(columns=["Image 1", "Image 2", "Type", "Similarity (%)"]), None

    md5_map = {}
    dhash_map = {}
    results = []
    images_preview = []

    # Ensure temp folder exists
    temp_folder = "temp_uploads"
    os.makedirs(temp_folder, exist_ok=True)

    # Save uploaded files locally
    for file in files:
        file_path = os.path.join(temp_folder, os.path.basename(file.name))
        with open(file_path, "wb") as f:
            f.write(file.read())

        img = Image.open(file_path).convert("RGB")
        images_preview.append(img)

        # MD5 check
        md5 = get_md5(file_path)
        if md5 in md5_map:
            results.append([file_path, md5_map[md5], "Exact Duplicate", 100])
        else:
            md5_map[md5] = file_path

        # dHash
        dhash_map[file_path] = imagehash.dhash(img)

    # Compare dHash
    file_names = list(dhash_map.keys())
    for i in range(len(file_names)):
        for j in range(i + 1, len(file_names)):
            hash1 = dhash_map[file_names[i]]
            hash2 = dhash_map[file_names[j]]

            diff = hash1 - hash2
            similarity = get_similarity_score(hash1, hash2)

            if diff < 10:
                results.append([file_names[i], file_names[j], "Similar", similarity])

    # Create DataFrame
    df = pd.DataFrame(results, columns=["Image 1", "Image 2", "Type", "Similarity (%)"])

    # Apply filter
    if filter_type == "Exact":
        df = df[df["Type"] == "Exact Duplicate"]
    elif filter_type == "Similar":
        df = df[df["Type"] == "Similar"]

    # Save CSV
    csv_path = os.path.join(temp_folder, "duplicate_results.csv")
    df.to_csv(csv_path, index=False)

    return images_preview, df, csv_path

# -------------------------
# GRADIO UI
# -------------------------
with gr.Blocks() as demo:

    gr.Markdown("## 🖼️ Image Duplicate Finder (Advanced)")

    with gr.Row():
        file_input = gr.File(
            file_types=["image"],
            file_count="multiple",
            label="Upload Images"
        )

        filter_option = gr.Radio(
            ["All", "Exact", "Similar"],
            value="All",
            label="Filter Results"
        )

    run_btn = gr.Button("Find Duplicates 🔍")

    gr.Markdown("### 📸 Uploaded Images")
    gallery = gr.Gallery()

    gr.Markdown("### 📊 Results Table")
    table = gr.Dataframe()

    download_btn = gr.File(label="Download CSV")

    # Action
    run_btn.click(
        fn=find_duplicates,
        inputs=[file_input, filter_option],
        outputs=[gallery, table, download_btn]
    )

# -------------------------
# LAUNCH
# -------------------------
demo.launch(server_name="0.0.0.0", server_port=7860)