Initial project upload via Python API for Flask Space

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo/demo.mp4 filter=lfs diff=lfs merge=lfs -text
+demo/demo1.png filter=lfs diff=lfs merge=lfs -text
+demo/demo2.png filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) 2025 Eslam Tarek
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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 ---
-title: ImageProof
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
+title: ImageProof – Deep learning AI-generated Image Detection
+emoji: 🚗
+colorFrom: yellow
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.40.0"   # latest stable streamlit
+app_file: app.py
 pinned: false
-short_description: 'Deep Learning Model for Detecting AI-Generated Images using '
 license: mit
 ---
 
-# Welcome to Streamlit!
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# ImageProof - AI Image Authenticity Detector 🧠
+
+A Streamlit-based web application that uses a fine-tuned EfficientNet-B3 model to detect whether images are AI-generated or real.
+
+## Table of Contents
+- [Demo](#demo)
+- [Features](#features)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Demo
+Check out the application in action with these demo files:
+
+<video controls>
+  <source src="demo/demo.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
+
+![Demo Image 1](demo/demo1.png)
+------------------------
+![Demo Image 2](demo/demo2.png)
+
+
+
+## Features
+- **Image Upload**: Support for JPG, JPEG, and PNG files.
+- **URL Input**: Analyze images directly from web URLs.
+- **Real-time Prediction**: Instant classification with confidence scores.
+- **Interactive UI**: Built with Streamlit for easy use.
+- **Model Integration**: Leverages EfficientNet-B3 for accurate detection.
+
+## Installation
+To get started, clone the repository and set up a virtual environment.
+
+```bash
+# Create a virtual environment
+python -m venv .venv
+
+# Activate it
+# On Linux/Mac:
+source .venv/bin/activate
+# On Windows:
+.venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Usage
+Run the application using Streamlit:
+
+```bash
+streamlit run app.py
+```
+
+1. Open the app in your browser.
+2. Choose to upload an image or enter an image URL.
+3. View the prediction results, including the label (AI-generated or Real) and confidence score.
+
+Example prediction output:
+- Label: 🧠 AI-generated
+- Confidence: 0.95
+
+## Contributing
+Contributions are welcome! Please fork the repository and submit a pull request. Ensure code follows best practices and includes tests.
+
+## License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgements
+- Built with [Streamlit](https://streamlit.io/) for the web interface.
+- Model based on [EfficientNet](https://github.com/lukemelas/EfficientNet-PyTorch) and [timm](https://github.com/rwightman/pytorch-image-models).
+- Thanks to the open-source community for PyTorch and related libraries.

app.py

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+import streamlit as st
+from utils import load_model, load_image, preprocess_image, predict
+from ui import show_header, show_image
+import os
+
+# ========================================
+# 🔧 Configuration
+# ========================================
+MODEL_DIR = "models"
+MODEL_PATH = os.path.join(MODEL_DIR, "efficientnet_b3_full_ai_image_classifier.pt")
+
+# ========================================
+# 🚀 Streamlit App
+# ========================================
+def main():
+    st.set_page_config(page_title="AI Image Detector", page_icon="🧠", layout="centered")
+    show_header()
+
+    # Load model once and cache
+    @st.cache_resource
+    def get_model():
+        return load_model(MODEL_PATH)
+
+    model = get_model()
+
+    # User options
+    option = st.radio("Choose Input Type:", ("Upload Image", "From URL"))
+
+    img = None
+    if option == "Upload Image":
+        uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+        if uploaded_file:
+            img = load_image(uploaded_file)
+    else:
+        url = st.text_input("Enter Image URL")
+        if url:
+            img = load_image(url)
+
+    # Predict
+    if img is not None:
+        img_tensor = preprocess_image(img)
+        label, prob = predict(model, img_tensor)
+        show_image(img, label, prob)
+    else:
+        st.info("👆 Upload an image or enter a URL to start.")
+
+
+if __name__ == "__main__":
+    main()

demo/demo.mp4

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models/efficientnet_b3_full_ai_image_classifier.pt

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requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+streamlit==1.28.1
+torch==2.0.1
+timm==0.9.7
+torchvision==0.15.2
+pillow==10.0.1
+requests==2.31.0
+matplotlib==3.8.0

ui.py

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+import streamlit as st
+import matplotlib.pyplot as plt
+
+def show_header():
+    """Display app title and description."""
+    st.title("🧠 AI-Generated Image Detector")
+    st.markdown("""
+    **Upload an image or enter an image URL** to detect whether it’s AI-generated or real.  
+    The model is based on **EfficientNet-B3**, fine-tuned for image authenticity detection.
+    """)
+
+
+def show_image(img, label, prob):
+    """Display image and prediction result."""
+    st.image(img, caption="Uploaded Image", use_container_width=True)
+
+    st.subheader("Prediction Results")
+    st.write(f"**Label:** {label}")
+    
+    # Optional: display confidence bar
+    st.progress(min(int(prob * 100), 100))

utils.py

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+import torch
+import timm
+from torchvision import transforms
+from PIL import Image
+import requests
+from io import BytesIO
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# 🔹 Preprocessing (same as training)
+transform = transforms.Compose([
+    transforms.Resize((300, 300)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+
+
+def load_model(model_path: str):
+    """Load EfficientNet-B3 model from file."""
+    if model_path.endswith(".pt"):
+        model = torch.load(model_path, map_location=DEVICE, weights_only=False)
+    else:
+        model = timm.create_model("efficientnet_b3", pretrained=False, num_classes=1)
+        model.load_state_dict(torch.load(model_path, map_location=DEVICE))
+    model.to(DEVICE)
+    model.eval()
+    return model
+
+
+def load_image(input_source):
+    """Load and preprocess image from file upload or URL."""
+    if isinstance(input_source, str):  # URL
+        response = requests.get(input_source)
+        img = Image.open(BytesIO(response.content)).convert("RGB")
+    else:  # Uploaded file
+        img = Image.open(input_source).convert("RGB")
+    return img
+
+
+def preprocess_image(img):
+    """Apply transform and return tensor."""
+    return transform(img).unsqueeze(0)
+
+
+def predict(model, img_tensor):
+    """Predict class and confidence score."""
+    with torch.no_grad():
+        outputs = model(img_tensor.to(DEVICE))
+        probs = torch.sigmoid(outputs)
+        prob = probs.item()
+        print(prob)
+        label = "🧠 AI-generated" if prob >= 0.00001 else "📸 Real"
+    return label, prob