Merge pull request #21 from cyberalertnepal/PujanDev

.gitignore

@@ -60,3 +60,9 @@ models/.gitattributes  #<-- This line can stay if you only want to ignore that f
 
 todo.md
 np_text_model
+IMG_Models
+notebooks
+# Ignore model and tokenizer files
+np_text_model/classifier/sentencepiece.bpe.model
+np_text_model/classifier/tokenizer.json
+

Dockerfile

@@ -1,12 +1,19 @@
 # Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
 # you will also find guides on how best to write your Dockerfile
 
-FROM python:3.9
+FROM python:3.10
 
+# Create user first
 RUN useradd -m -u 1000 user
+
+# Install system dependencies (requires root)
+RUN apt-get update && apt-get install -y libgl1
+
+# Switch to non-root user
 USER user
 ENV PATH="/home/user/.local/bin:$PATH"
 
+# Add TensorFlow environment variables to reduce logging noise
 WORKDIR /app
 
 COPY --chown=user ./requirements.txt requirements.txt
@@ -14,4 +21,6 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 RUN python -m spacy download en_core_web_sm || echo "Failed to download model"
 
 COPY --chown=user . /app
+
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+

README.md

@@ -1,9 +1,152 @@
+# AI-Contain-Checker
+
+A modular AI content detection system with support for **image classification**, **image edit detection**, **Nepali text classification**, and **general text classification**. Built for performance and extensibility, it is ideal for detecting AI-generated content in both visual and textual forms.
+
+
+## 🌟 Features
+
+### 🖼️ Image Classifier
+
+* **Purpose**: Classifies whether an image is AI-generated or a real-life photo.
+* **Model**: Fine-tuned **InceptionV3** CNN.
+* **Dataset**: Custom curated dataset with **\~79,950 images** for binary classification.
+* **Location**: [`features/image_classifier`](features/image_classifier)
+* **Docs**: [`docs/features/image_classifier.md`](docs/features/image_classifier.md)
+
+### 🖌️ Image Edit Detector
+
+* **Purpose**: Detects image tampering or post-processing.
+* **Techniques Used**:
+
+  * **Error Level Analysis (ELA)**: Visualizes compression artifacts.
+  * **Fast Fourier Transform (FFT)**: Detects unnatural frequency patterns.
+* **Location**: [`features/image_edit_detector`](features/image_edit_detector)
+* **Docs**:
+
+  * [ELA](docs/detector/ELA.md)
+  * [FFT](docs/detector/fft.md )
+  * [Metadata Analysis](docs/detector/meta.md)
+  * [Backend Notes](docs/detector/note-for-backend.md)
+
+### 📝 Nepali Text Classifier
+
+* **Purpose**: Determines if Nepali text content is AI-generated or written by a human.
+* **Model**: Based on `XLMRClassifier` fine-tuned on Nepali language data.
+* **Dataset**: Scraped dataset of **\~18,000** Nepali texts.
+* **Location**: [`features/nepali_text_classifier`](features/nepali_text_classifier)
+* **Docs**: [`docs/features/nepali_text_classifier.md`](docs/features/nepali_text_classifier.md)
+
+### 🌐 English Text Classifier
+
+* **Purpose**: Detects if English text is AI-generated or human-written.
+* **Pipeline**:
+
+  * Uses **GPT2 tokenizer** for input preprocessing.
+  * Custom binary classifier to differentiate between AI and human-written content.
+* **Location**: [`features/text_classifier`](features/text_classifier)
+* **Docs**: [`docs/features/text_classifier.md`](docs/features/text_classifier.md)
+
 ---
-title: Ai-Checker
-emoji: 🚀
-colorFrom: yellow
-colorTo: blue
-sdk: docker
-pinned: false
+
+## 🗂️ Project Structure
+
+```bash
+AI-Checker/
+│
+├── app.py                  # Main FastAPI entry point
+├── config.py               # Configuration settings
+├── Dockerfile              # Docker build script
+├── Procfile                # Deployment file for Heroku or similar
+├── requirements.txt        # Python dependencies
+├── README.md               # You are here 📘
+│
+├── features/               # Core detection modules
+│   ├── image_classifier/
+│   ├── image_edit_detector/
+│   ├── nepali_text_classifier/
+│   └── text_classifier/
+│
+├── docs/                   # Internal and API documentation
+│   ├── api_endpoints.md
+│   ├── deployment.md
+│   ├── detector/
+│   │   ├── ELA.md
+│   │   ├── fft.md
+│   │   ├── meta.md
+│   │   └── note-for-backend.md
+│   ├── functions.md
+│   ├── nestjs_integration.md
+│   ├── security.md
+│   ├── setup.md
+│   └── structure.md
+│
+├── IMG_Models/             # Saved image classifier model(s)
+│   └── latest-my_cnn_model.h5
+│
+├── notebooks/              # Experimental and debug notebooks
+├── static/                 # Static assets if needed
+└── test.md                 # Test notes
+````
+
 ---
 
+## 📚 Documentation Links
+
+* [API Endpoints](docs/api_endpoints.md)
+* [Deployment Guide](docs/deployment.md)
+* [Detector Documentation](docs/detector/)
+
+  * [Error Level Analysis (ELA)](docs/detector/ELA.md)
+  * [Fast Fourier Transform (FFT)](docs/detector/fft.md)
+  * [Metadata Analysis](docs/detector/meta.md)
+  * [Backend Notes](docs/detector/note-for-backend.md)
+* [Functions Overview](docs/functions.md)
+* [NestJS Integration Guide](docs/nestjs_integration.md)
+* [Security Details](docs/security.md)
+* [Setup Instructions](docs/setup.md)
+* [Project Structure](docs/structure.md)
+
+---
+
+## 🚀 Usage
+
+1. **Install dependencies**
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. **Run the API**
+
+   ```bash
+   uvicorn app:app --reload
+   ```
+
+3. **Build Docker (optional)**
+
+   ```bash
+   docker build -t ai-contain-checker .
+   docker run -p 8000:8000 ai-contain-checker
+   ```
+
+---
+
+## 🔐 Security & Integration
+
+* **Token Authentication** and **IP Whitelisting** supported.
+* NestJS integration guide: [`docs/nestjs_integration.md`](docs/nestjs_integration.md)
+* Rate limiting handled using `slowapi`.
+
+---
+
+## 🛡️ Future Plans
+
+* Add **video classifier** module.
+* Expand dataset for **multilingual** AI content detection.
+* Add **fine-tuning UI** for models.
+
+---
+
+## 📄 License
+
+See full license terms here: [`LICENSE.md`](license.md)

app.py

@@ -1,37 +1,62 @@
 from fastapi import FastAPI, Request
 from slowapi import Limiter, _rate_limit_exceeded_handler
+from fastapi.responses import FileResponse
 from slowapi.middleware import SlowAPIMiddleware
 from slowapi.errors import RateLimitExceeded
 from slowapi.util import get_remote_address
 from fastapi.responses import JSONResponse
 from features.text_classifier.routes import router as text_classifier_router
-from features.nepali_text_classifier.routes import router as nepali_text_classifier_router
+from features.nepali_text_classifier.routes import (
+    router as nepali_text_classifier_router,
+)
+from features.image_classifier.routes import router as image_classifier_router
+from features.image_edit_detector.routes import router as image_edit_detector_router
+from fastapi.staticfiles import StaticFiles
+
 from config import ACCESS_RATE
+
 import requests
+
 limiter = Limiter(key_func=get_remote_address, default_limits=[ACCESS_RATE])
 
 app = FastAPI()
-
+# added the robots.txt
 # Set up SlowAPI
 app.state.limiter = limiter
-app.add_exception_handler(RateLimitExceeded, lambda request, exc: JSONResponse(
-    status_code=429,
-    content={
-        "status_code": 429,
-        "error": "Rate limit exceeded",
-        "message": "Too many requests. Chill for a bit and try again"
-    }
-))
+app.add_exception_handler(
+    RateLimitExceeded,
+    lambda request, exc: JSONResponse(
+        status_code=429,
+        content={
+            "status_code": 429,
+            "error": "Rate limit exceeded",
+            "message": "Too many requests. Chill for a bit and try again",
+        },
+    ),
+)
 app.add_middleware(SlowAPIMiddleware)
 
 # Include your routes
 app.include_router(text_classifier_router, prefix="/text")
-app.include_router(nepali_text_classifier_router,prefix="/NP")
+app.include_router(nepali_text_classifier_router, prefix="/NP")
+app.include_router(image_classifier_router, prefix="/AI-image")
+app.include_router(image_edit_detector_router, prefix="/detect")
+
+
 @app.get("/")
 @limiter.limit(ACCESS_RATE)
 async def root(request: Request):
     return {
         "message": "API is working",
-        "endpoints": ["/text/analyse", "/text/upload", "/text/analyse-sentences", "/text/analyse-sentance-file"]
+        "endpoints": [
+            "/text/analyse",
+            "/text/upload",
+            "/text/analyse-sentences",
+            "/text/analyse-sentance-file",
+            "/NP/analyse",
+            "/NP/upload",
+            "/NP/analyse-sentences",
+            "/NP/file-sentences-analyse",
+            "/AI-image/analyse",
+        ],
     }
-

docs/api_endpoints.md

@@ -2,13 +2,13 @@
 
 ### English (GPT-2) - `/text/`
 
-| Endpoint                         | Method | Description                               |
-| --------------------------------- | ------ | ----------------------------------------- |
-| `/text/analyse`                  | POST   | Classify raw English text                 |
-| `/text/analyse-sentences`        | POST   | Sentence-by-sentence breakdown            |
-| `/text/analyse-sentance-file`    | POST   | Upload file, per-sentence breakdown       |
-| `/text/upload`                   | POST   | Upload file for overall classification    |
-| `/text/health`                   | GET    | Health check                             |
+| Endpoint                      | Method | Description                            |
+| ----------------------------- | ------ | -------------------------------------- |
+| `/text/analyse`               | POST   | Classify raw English text              |
+| `/text/analyse-sentences`     | POST   | Sentence-by-sentence breakdown         |
+| `/text/analyse-sentance-file` | POST   | Upload file, per-sentence breakdown    |
+| `/text/upload`                | POST   | Upload file for overall classification |
+| `/text/health`                | GET    | Health check                           |
 
 #### Example: Classify English text
 
@@ -20,6 +20,7 @@ curl -X POST http://localhost:8000/text/analyse \
 ```
 
 **Response:**
+
 ```json
 {
   "result": "AI-generated",
@@ -40,13 +41,13 @@ curl -X POST http://localhost:8000/text/upload \
 
 ### Nepali (SentencePiece) - `/NP/`
 
-| Endpoint                         | Method | Description                               |
-| --------------------------------- | ------ | ----------------------------------------- |
-| `/NP/analyse`                    | POST   | Classify Nepali text                      |
-| `/NP/analyse-sentences`          | POST   | Sentence-by-sentence breakdown            |
-| `/NP/upload`                     | POST   | Upload Nepali PDF for classification      |
-| `/NP/file-sentences-analyse`     | POST   | PDF upload, per-sentence breakdown        |
-| `/NP/health`                     | GET    | Health check                             |
+| Endpoint                     | Method | Description                          |
+| ---------------------------- | ------ | ------------------------------------ |
+| `/NP/analyse`                | POST   | Classify Nepali text                 |
+| `/NP/analyse-sentences`      | POST   | Sentence-by-sentence breakdown       |
+| `/NP/upload`                 | POST   | Upload Nepali PDF for classification |
+| `/NP/file-sentences-analyse` | POST   | PDF upload, per-sentence breakdown   |
+| `/NP/health`                 | GET    | Health check                         |
 
 #### Example: Nepali text classification
 
@@ -58,6 +59,7 @@ curl -X POST http://localhost:8000/NP/analyse \
 ```
 
 **Response:**
+
 ```json
 {
   "label": "Human",
@@ -73,3 +75,18 @@ curl -X POST http://localhost:8000/NP/upload \
   -F 'file=@NepaliText.pdf;type=application/pdf'
 ```
 
+### Image-Classification -`/verify-image/`
+
+| Endpoint                | Method | Description             |
+| ----------------------- | ------ | ----------------------- |
+| `/verify-image/analyse` | POST   | Classify Image using ML |
+
+#### Example: Image-Classification
+
+```bash
+curl -X POST http://localhost:8000/verify-image/analyse \
+  -H "Authorization: Bearer <SECRET_TOKEN>" \
+  -F 'file=@test1.png'
+```
+
+[🔙 Back to Main README](../README.md)

docs/deployment.md

@@ -103,3 +103,6 @@ CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
 
 Happy deploying!  
 **P.S.** Try not to break stuff. 😅
+
+
+[🔙 Back to Main README](../README.md)

docs/detector/ELA.md

@@ -0,0 +1,65 @@
+# Error Level Analysis (ELA) Detector
+
+This module provides a function to perform Error Level Analysis (ELA) on images to detect potential manipulations or edits.
+
+## Function: `run_ela`
+
+```python
+def run_ela(image: Image.Image, quality: int = 90, threshold: int = 15) -> bool:
+```
+
+### Description
+
+Error Level Analysis (ELA) works by recompressing an image at a specified JPEG quality level and comparing it to the original image. Differences between the two images reveal areas with inconsistent compression artifacts — often indicating image manipulation.
+
+The function computes the maximum pixel difference across all color channels and uses a threshold to determine if the image is likely edited.
+
+### Parameters
+
+| Parameter   | Type        | Default | Description                                                                                 |
+| ----------- | ----------- | ------- | ------------------------------------------------------------------------------------------- |
+| `image`     | `PIL.Image` | N/A     | Input image in RGB mode to analyze.                                                         |
+| `quality`   | `int`       | 90      | JPEG compression quality used for recompression during analysis (lower = more compression). |
+| `threshold` | `int`       | 15      | Pixel difference threshold to flag the image as edited.                                     |
+
+### Returns
+
+`bool`
+
+- `True` if the image is likely edited (max pixel difference > threshold).
+- `False` if the image appears unedited.
+
+### Usage Example
+
+```python
+from PIL import Image
+from detectors.ela import run_ela
+
+# Open and convert image to RGB
+img = Image.open("example.jpg").convert("RGB")
+
+# Run ELA detection
+is_edited = run_ela(img, quality=90, threshold=15)
+
+print("Image edited:", is_edited)
+```
+
+### Notes
+
+- The input image **must** be in RGB mode for accurate analysis.
+- ELA is a heuristic technique; combining it with other detection methods increases reliability.
+- Visualizing the enhanced difference image can help identify edited regions (not returned by this function but possible to add).
+
+### Installation
+
+Make sure you have Pillow installed:
+
+```bash
+pip install pillow
+```
+
+### Running Locally
+
+Just put the function in a notebook or script file and run it with your image. It works well for basic images.
+
+[🔙 Back to Main README](../README.md)

docs/detector/fft.md

@@ -0,0 +1,136 @@
+
+#  Fast Fourier Transform (FFT) Detector
+
+```python
+def run_fft(image: Image.Image, threshold: float = 0.92) -> bool:
+```
+
+## **Overview**
+
+The `run_fft` function performs a frequency domain analysis on an image using the **Fast Fourier Transform (FFT)** to detect possible **AI generation or digital manipulation**. It leverages the fact that artificially generated or heavily edited images often exhibit a distinct high-frequency pattern.
+
+---
+
+## **Parameters**
+
+| Parameter   | Type              | Description                                                                             |
+| ----------- | ----------------- | --------------------------------------------------------------------------------------- |
+| `image`     | `PIL.Image.Image` | Input image to analyze. It will be converted to grayscale and resized.                  |
+| `threshold` | `float`           | Proportion threshold of high-frequency components to flag the image. Default is `0.92`. |
+
+---
+
+## **Returns**
+
+| Type   | Description                                                            |
+| ------ | ---------------------------------------------------------------------- |
+| `bool` | `True` if image is likely AI-generated/manipulated; otherwise `False`. |
+
+---
+
+## **Step-by-Step Explanation**
+
+### 1. **Grayscale Conversion**
+
+All images are converted to grayscale:
+
+```python
+gray_image = image.convert("L")
+```
+
+### 2. **Resize**
+
+The image is resized to a fixed $512 \times 512$ for uniformity:
+
+```python
+resized_image = gray_image.resize((512, 512))
+```
+
+### 3. **FFT Calculation**
+
+Compute the 2D Discrete Fourier Transform:
+
+$$
+F(u, v) = \sum_{x=0}^{M-1} \sum_{y=0}^{N-1} f(x, y) \cdot e^{-2\pi i \left( \frac{ux}{M} + \frac{vy}{N} \right)}
+$$
+
+```python
+fft_result = fft2(image_array)
+```
+
+### 4. **Shift Zero Frequency to Center**
+
+Use `fftshift` to center the zero-frequency component:
+
+```python
+fft_shifted = fftshift(fft_result)
+```
+
+### 5. **Magnitude Spectrum**
+
+$$
+|F(u, v)| = \sqrt{\Re^2 + \Im^2}
+$$
+
+```python
+magnitude_spectrum = np.abs(fft_shifted)
+```
+
+### 6. **Normalization**
+
+Normalize the spectrum to avoid scale issues:
+
+$$
+\text{Normalized}(u,v) = \frac{|F(u,v)|}{\max(|F(u,v)|)}
+$$
+
+```python
+normalized_spectrum = magnitude_spectrum / max_magnitude
+```
+
+### 7. **High-Frequency Detection**
+
+High-frequency components are defined as:
+
+$$
+\text{Mask}(u,v) = 
+\begin{cases}
+1 & \text{if } \text{Normalized}(u,v) > 0.5 \\
+0 & \text{otherwise}
+\end{cases}
+$$
+
+```python
+high_freq_mask = normalized_spectrum > 0.5
+```
+
+### 8. **Proportion Calculation**
+
+$$
+\text{Ratio} = \frac{\sum \text{Mask}}{\text{Total pixels}}
+$$
+
+```python
+high_freq_ratio = np.sum(high_freq_mask) / normalized_spectrum.size
+```
+
+### 9. **Threshold Decision**
+
+If the ratio exceeds the threshold:
+
+$$
+\text{is\_fake} = (\text{Ratio} > \text{Threshold})
+$$
+
+```python
+is_fake = high_freq_ratio > threshold
+```
+
+it is implemented in the api
+
+### Running Locally
+
+Just put the function in a notebook or script file and run it with your image. It works well for basic images.
+
+
+[🔙 Back to Main README](../README.md)

docs/detector/meta.md

@@ -0,0 +1,20 @@
+# Metadata Analysis for Image Edit Detection
+
+This module inspects image metadata to detect possible signs of AI-generation or post-processing edits.
+
+## Overview
+
+- Many AI-generated images and edited images leave identifiable traces in their metadata.
+- This detector scans image EXIF metadata and raw bytes for known AI generation indicators and common photo editing software signatures.
+- It classifies images as `"ai_generated"`, `"edited"`, or `"undetermined"` based on detected markers.
+- Handles invalid image formats gracefully by reporting errors.
+
+## How It Works
+
+- Opens the image from raw bytes using the Python Pillow library (`PIL`).
+- Reads EXIF metadata and specifically looks for the "Software" tag that often contains the editing app name.
+- Checks for common image editors such as Photoshop, GIMP, Snapseed, etc.
+- Scans the entire raw byte content of the image for embedded AI generation identifiers like "midjourney", "stable-diffusion", "openai", etc.
+- Returns a status string indicating the metadata classification.
+
+[🔙 Back to Main README](../README.md)

docs/detector/note-for-backend.md

@@ -0,0 +1,94 @@
+
+# 📦API integration note
+
+## Overview
+
+This system integrates **three image forensics methods**—**ELA**, **FFT**, and **Metadata analysis**—into a single detection pipeline to determine whether an image is AI-generated, manipulated, or authentic.
+
+---
+
+## 🔍 Detection Modules
+
+### 1. **ELA (Error Level Analysis)**
+
+* **Purpose:** Detects tampering or editing by analyzing compression error levels.
+* **Accuracy:** ✅ *Most accurate method*
+* **Performance:** ❗ *Slowest method*
+* **Output:** `True` (edited) or `False` (authentic)
+
+### 2. **FFT (Fast Fourier Transform)**
+
+* **Purpose:** Identifies high-frequency patterns typical of AI-generated images.
+* **Accuracy:** ⚠️ *Moderately accurate*
+* **Performance:** ❗ *Moderate to slow*
+* **Output:** `True` (likely AI-generated) or `False` (authentic)
+
+### 3. **Metadata Analysis**
+
+* **Purpose:** Detects traces of AI tools or editors in image metadata or binary content.
+* **Accuracy:** ⚠️ *Fast but weaker signal*
+* **Performance:** 🚀 *Fastest method*
+* **Output:** One of:
+
+  * `"ai_generated"` – AI tool or generator identified
+  * `"edited"` – Edited using known software
+  * `"undetermined"` – No signature found
+
+---
+
+## 🧩 Integration Plan
+
+### ➕ Combine all three APIs into one unified endpoint:
+
+```bash
+POST /api/detect-image
+```
+
+### Input:
+
+* `image`: Image file (binary, any format supported by Pillow)
+
+### Output:
+
+```json
+{
+  "ela_result": true,
+  "fft_result": false,
+  "metadata_result": "ai_generated",
+  "final_decision": "ai_generated" 
+}
+```
+> NOTE:Optionally recommending a default logic (e.g., trust ELA > FFT > Metadata).
+
+## Result implementation
+| `ela_result` | `fft_result` | `metadata_result` | Suggested Final Decision | Notes                                                                   |
+| ------------ | ------------ | ----------------- | ------------------------ | ----------------------------------------------------------------------- |
+| `true`       | `true`       | `"ai_generated"`  | `ai_generated`           | Strong evidence from all three modules                                  |
+| `true`       | `false`      | `"edited"`        | `edited`                 | ELA confirms editing, no AI signals                                     |
+| `true`       | `false`      | `"undetermined"`  | `edited`                 | ELA indicates manipulation                                              |
+| `false`      | `true`       | `"ai_generated"`  | `ai_generated`           | No edits, but strong AI frequency & metadata signature                  |
+| `false`      | `true`       | `"undetermined"`  | `possibly_ai_generated`  | Weak metadata, but FFT indicates possible AI generation                 |
+| `false`      | `false`      | `"ai_generated"`  | `ai_generated`           | Metadata alone shows AI use                                             |
+| `false`      | `false`      | `"edited"`        | `possibly_edited`        | Weak signal—metadata shows editing but no structural or frequency signs |
+| `false`      | `false`      | `"undetermined"`  | `authentic`              | No detectable manipulation or AI indicators                             |
+
+
+### Decision Logic:
+
+* Use **ELA** as the **primary indicator** for manipulation.
+* Supplement with **FFT** and **Metadata** to improve reliability.
+* Combine using a simple rule-based or voting system.
+
+---
+
+## ⚙️ Performance Consideration
+
+| Method   | Speed       | Strength             |
+| -------- | ----------- | -------------------- |
+| ELA      | ❗ Slow      | ✅ Highly accurate    |
+| FFT      | ⚠️ Moderate | ⚠️ Somewhat reliable |
+| Metadata | 🚀 Fast     | ⚠️ Low confidence    |
+
+> For high-throughput systems, consider running Metadata first and conditionally applying ELA/FFT if suspicious.
+
+[🔙 Back to Main README](../README.md)

docs/features/image_classifier.md

@@ -0,0 +1,31 @@
+# Image Classifier
+
+## Overview
+
+This module classifies whether an input image is AI-generated or a real-life photograph.
+
+## Model
+
+- Architecture: InceptionV3
+- Type: Binary Classifier (AI vs Real)
+- Format: H5 model (`latest-my_cnn_model.h5`)
+
+## Dataset
+
+- Total images: ~79,950
+- Balanced between real and generated images
+- Preprocessing: Resizing, normalization
+
+## Code Location
+
+- Controller: `features/image_classifier/controller.py`
+- Model Loader: `features/image_classifier/model_loader.py`
+- Preprocessor: `features/image_classifier/preprocess.py`
+
+## API
+
+- Endpoint: [ENDPOINTS](../api_endpoints.md)
+- Input: Image file (PNG/JPG)
+- Output: JSON response with classification result and confidence
+
+[🔙 Back to Main README](../README.md)

docs/features/nepali_text_classifier.md

@@ -0,0 +1,30 @@
+# Nepali Text Classifier
+
+## Overview
+
+This classifier identifies whether Nepali-language text content is written by a human or AI.
+
+## Model
+
+- Base Model: XLM-Roberta (XLMRClassifier)
+- Language: Nepali (Multilingual model)
+- Fine-tuned with scraped web content (~18,000 samples)
+
+## Dataset
+
+- Custom scraped dataset with manual labeling
+- Includes news, blogs, and synthetic content from various LLMs
+
+## Code Location
+
+- Controller: `features/nepali_text_classifier/controller.py`
+- Inference: `features/nepali_text_classifier/inferencer.py`
+- Model Loader: `features/nepali_text_classifier/model_loader.py`
+
+## API
+
+- Endpoint: [ENDPOINTS](../api_endpoints.md)
+- Input: Raw text
+- Output: JSON classification with label and confidence score
+
+[🔙 Back to Main README](../README.md)

docs/features/text_classifier.md

@@ -0,0 +1,30 @@
+# English Text Classifier
+
+## Overview
+
+Detects whether English-language text is AI-generated or human-written.
+
+## Model Pipeline
+
+- Tokenizer: GPT-2 Tokenizer
+- Model: Custom trained binary classifier
+
+## Dataset
+
+- Balanced dataset: Human vs AI-generated (ChatGPT, Claude, etc.)
+- Tokenized and fed into the model using PyTorch/TensorFlow
+
+## Code Location
+
+- Controller: `features/text_classifier/controller.py`
+- Inference: `features/text_classifier/inferencer.py`
+- Model Loader: `features/text_classifier/model_loader.py`
+- Preprocessor: `features/text_classifier/preprocess.py`
+
+## API
+
+- Endpoint: [ENDPOINTS](../api_endpoints.md)
+- Input: Raw English text
+- Output: Prediction result with probability/confidence
+
+[🔙 Back to Main README](../README.md)

docs/functions.md

@@ -49,5 +49,14 @@
 
 - **`analyze_sentence_file()`**  
   Like `handle_file_sentence()`—analyzes sentences in uploaded files.
-
+---
 ## for image_classifier
+
+- **`Classify_Image_router()`** – Handles image classification requests by routing and coordinating preprocessing and inference.
+- **`classify_image()`** – Performs AI vs human image classification using the loaded model.
+- **`load_model()`** – Loads the pretrained model from Hugging Face at server startup.
+- **`preprocess_image()`** – Applies all required preprocessing steps to the input image.
+
+> Note: While many functions mirror those in the text classifier, the image classifier primarily uses TensorFlow rather than PyTorch.
+
+[🔙 Back to Main README](../README.md)

docs/nestjs_integration.md

@@ -80,3 +80,4 @@ export class AppController {
   }
 }
 ```
+[🔙 Back to Main README](../README.md)

docs/security.md

@@ -7,3 +7,4 @@ All endpoints require authentication via Bearer token:
 
 Unauthorized requests receive `403 Forbidden`.
 
+[🔙 Back to Main README](../README.md)

docs/setup.md

@@ -21,3 +21,4 @@ SECRET_TOKEN=your_secret_token_here
 ```bash
 uvicorn app:app --host 0.0.0.0 --port 8000
 ```
+[🔙 Back to Main README](../README.md)

docs/status_code.md

@@ -0,0 +1,68 @@
+# Error Codes Reference
+
+## 🔹 Summary Table
+
+| Code | Message                                               | Description                                |
+| ---- | ----------------------------------------------------- | ------------------------------------------ |
+| 400  | Text must contain at least two words                  | Input text too short                       |
+| 400  | Text should be less than 10,000 characters            | Input text too long                        |
+| 404  | The file is empty or only contains whitespace         | File has no usable content                 |
+| 404  | Invalid file type. Only .docx, .pdf, and .txt allowed | Unsupported file format                    |
+| 403  | Invalid or expired token                              | Authentication token is invalid or expired |
+| 413  | Text must contain at least two words                  | Text too short (alternative condition)     |
+| 413  | Text must be less than 10,000 characters              | Text too long (alternative condition)      |
+| 413  | The image error (preprocessing)                       | Image size/content issue                   |
+| 500  | Error processing the file                             | Internal server error while processing     |
+
+---
+
+## 🔍 Error Details
+
+### `400` - Bad Request
+
+- **Text must contain at least two words**  
+  The input text field is too short. Submit at least two words to proceed.
+
+- **Text should be less than 10,000 characters**  
+  Input text exceeds the maximum allowed character limit. Consider truncating or summarizing the content.
+
+---
+
+### `404` - Not Found
+
+- **The file is empty or only contains whitespace**  
+  The uploaded file is invalid due to lack of meaningful content. Ensure the file has readable, non-empty text.
+
+- **Invalid file type. Only .docx, .pdf, and .txt are allowed**  
+  The file format is not supported. Convert the file to one of the allowed formats before uploading.
+
+---
+
+### `403` - Forbidden
+
+- **Invalid or expired token**  
+  Your access token is either expired or incorrect. Try logging in again or refreshing the token.
+
+---
+
+### `413` - Payload Too Large
+
+- **Text must contain at least two words**  
+  The text payload is too small or malformed under a large upload context. Add more content.
+
+- **Text must be less than 10,000 characters**  
+  The payload exceeds the allowed character limit for a single request. Break it into smaller chunks if needed.
+
+- **The image error**  
+  The uploaded image is too large or corrupted. Try resizing or compressing it before retrying.
+
+---
+
+### `500` - Internal Server Error
+
+- **Error processing the file**  
+  An unexpected server-side failure occurred during file analysis. Retry later or contact support if persistent.
+
+---
+
+> 📌 **Note:** Always validate inputs, check token status, and follow file guidelines before making requests.

docs/structure.md

@@ -1,36 +1,58 @@
 ## 🏗️ Project Structure
 
-```
-├── app.py                   # Main FastAPI app entrypoint
-├── config.py                # Configuration loader (.env, settings)
-├── features/
-│   ├── text_classifier/     # English (GPT-2) classifier
+```bash
+AI-Checker/
+│
+├── app.py                  # Main FastAPI entry point
+├── config.py               # Configuration settings
+├── Dockerfile              # Docker build script
+├── Procfile                # Deployment entry for platforms like Heroku/Railway
+├── requirements.txt        # Python dependency list
+├── README.md               # Main project overview 📘
+│
+├── features/               # Core AI content detection modules
+│   ├── image_classifier/           # Classifies AI vs Real images
+│   │   ├── controller.py
+│   │   ├── model_loader.py
+│   │   └── preprocess.py
+│   ├── image_edit_detector/       # Detects tampered or edited images
+│   ├── nepali_text_classifier/    # Classifies Nepali text as AI or Human
 │   │   ├── controller.py
 │   │   ├── inferencer.py
 │   │   ├── model_loader.py
-│   │   ├── preprocess.py
-│   │   └── routes.py
-│   └── nepali_text_classifier/ # Nepali (sentencepiece) classifier
+│   │   └── preprocess.py
+│   └── text_classifier/           # Classifies English text as AI or Human
 │       ├── controller.py
 │       ├── inferencer.py
 │       ├── model_loader.py
-│       ├── preprocess.py
-│       └── routes.py
-├── np_text_model/           # Nepali model artifacts (auto-downloaded)
-│   ├── classifier/
-│   │   └── sentencepiece.bpe.model
-│   └── model_95_acc.pth
-├── models/                  # English GPT-2 model/tokenizer (auto-downloaded)
-│   ├── merges.txt
-│   ├── tokenizer.json
-│   └── model_weights.pth
-├── Dockerfile               # Container build config
-├── Procfile                 # Deployment entrypoint (for PaaS)
-├── requirements.txt         # Python dependencies
-├── README.md     
-├── Docs                     # documents
-└── .env                     # Secret token(s), environment config
+│       └── preprocess.py
+│
+├── docs/                   # Internal documentation and API references
+│   ├── api_endpoints.md
+│   ├── deployment.md
+│   ├── detector/
+│   │   ├── ELA.md
+│   │   ├── fft.md
+│   │   ├── meta.md
+│   │   └── note-for-backend.md
+│   ├── features/
+│   │   ├── image_classifier.md
+│   │   ├── nepali_text_classifier.md
+│   │   └── text_classifier.md
+│   ├── functions.md
+│   ├── nestjs_integration.md
+│   ├── security.md
+│   ├── setup.md
+│   └── structure.md
+│
+├── IMG_Models/             # Stored model weights
+│   └── latest-my_cnn_model.h5
+│
+├── notebooks/              # Experimental/debug Jupyter notebooks
+├── static/                 # Static files (e.g., UI assets, test inputs)
+└── test.md                 # Test usage notes
 ```
+
 ### 🌟 Key Files and Their Roles
 
 - **`app.py`**: Entry point initializing FastAPI app and routes.
@@ -39,16 +61,14 @@
 - **`__init__.py`**: Package initializer for the root module and submodules.
 - **`features/text_classifier/`**
   - **`controller.py`**: Handles logic between routes and the model.
-  - **`inferencer.py`**: Runs inference and returns predictions as well as file system 
-  utilities.
+  - **`inferencer.py`**: Runs inference and returns predictions as well as file system
+    utilities.
 - **`features/NP/`**
   - **`controller.py`**: Handles logic between routes and the model.
-  - **`inferencer.py`**: Runs inference and returns predictions as well as file system 
-  utilities.
+  - **`inferencer.py`**: Runs inference and returns predictions as well as file system
+    utilities.
   - **`model_loader.py`**: Loads the ML model and tokenizer.
   - **`preprocess.py`**: Prepares input text for the model.
   - **`routes.py`**: Defines API routes for text classification.
 
-
-
--[Main](../README.md)
+[🔙 Back to Main README](../README.md)

features/image_classifier/controller.py

@@ -0,0 +1,16 @@
+from fastapi import HTTPException, File, UploadFile
+from .preprocess import preprocess_image
+from .inferencer import classify_image
+
+
+async def Classify_Image_router(file: UploadFile = File(...)):
+    try:
+        image_array = preprocess_image(file)
+        try:
+            result = classify_image(image_array)
+            return result
+        except:
+            raise HTTPException(status_code=423, detail="something went wrong")
+
+    except Exception as e:
+        raise HTTPException(status_code=413, detail=str(e))

features/image_classifier/inferencer.py

@@ -0,0 +1,42 @@
+import numpy as np
+from .model_loader import get_model
+
+# Thresholds
+AI_THRESHOLD = 0.55
+HUMAN_THRESHOLD = 0.45
+
+
+def classify_image(image_array: np.ndarray) -> dict:
+    try:
+        model = get_model()
+        predictions = model.predict(image_array)
+
+        if predictions.ndim != 2 or predictions.shape[1] != 1:
+            raise ValueError(
+                "Model output shape is invalid. Expected shape: (batch, 1)"
+            )
+
+        ai_conf = float(np.clip(predictions[0][0], 0.0, 1.0))
+        human_conf = 1.0 - ai_conf
+
+        # Classification logic
+        if ai_conf > AI_THRESHOLD:
+            label = "AI Generated"
+        elif ai_conf < HUMAN_THRESHOLD:
+            label = "Human Generated"
+        else:
+            label = "Uncertain (Maybe AI)"
+
+        return {
+            "label": label,
+            "ai_confidence": round(ai_conf * 100, 2),
+            "human_confidence": round(human_conf * 100, 2),
+        }
+
+    except Exception as e:
+        return {
+            "error": str(e),
+            "label": "Classification Failed",
+            "ai_confidence": None,
+            "human_confidence": None,
+        }

features/image_classifier/model_loader.py

@@ -0,0 +1,58 @@
+import os
+import shutil
+import logging
+import tensorflow as tf
+from tensorflow.keras.layers import Layer
+from huggingface_hub import snapshot_download
+
+# Model config
+REPO_ID = "can-org/AI-VS-HUMAN-IMAGE-classifier"
+MODEL_DIR = "./IMG_Models"
+WEIGHTS_PATH = os.path.join(MODEL_DIR, "latest-my_cnn_model.h5")
+
+# Device info (for logging)
+gpus = tf.config.list_physical_devices("GPU")
+device = "cuda" if gpus else "cpu"
+
+# Global model reference
+_model_img = None
+
+# Custom layer used in the model
+class Cast(Layer):
+    def call(self, inputs):
+        return tf.cast(inputs, tf.float32)
+
+def warmup():
+    global _model_img
+    download_model_repo()
+    _model_img = load_model()
+    logging.info("Image model is ready.")
+
+def download_model_repo():
+    if os.path.exists(MODEL_DIR) and os.path.isdir(MODEL_DIR):
+        logging.info("Image model already exists, skipping download.")
+        return
+    snapshot_path = snapshot_download(repo_id=REPO_ID)
+    os.makedirs(MODEL_DIR, exist_ok=True)
+    shutil.copytree(snapshot_path, MODEL_DIR, dirs_exist_ok=True)
+
+def load_model():
+    global _model_img
+    if _model_img is not None:
+        return _model_img
+
+    print(f"{'GPU detected' if device == 'cuda' else 'No GPU detected'}, loading model on {device.upper()}.")
+
+    _model_img = tf.keras.models.load_model(
+        WEIGHTS_PATH, custom_objects={"Cast": Cast}
+    )
+    print("Model input shape:", _model_img.input_shape)
+    return _model_img
+
+def get_model():
+    global _model_img
+    if _model_img is None:
+        download_model_repo()
+        _model_img = load_model()
+    return _model_img
+

features/image_classifier/preprocess.py

@@ -0,0 +1,26 @@
+import numpy as np
+import cv2
+from fastapi import HTTPException
+
+
+def preprocess_image(file):
+    try:
+        file.file.seek(0)
+        image_bytes = file.file.read()
+        nparr = np.frombuffer(image_bytes, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        if img is None:
+            raise HTTPException(status_code=500, detail="Could not decode image.")
+
+        img = cv2.resize(img, (299, 299))
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = img / 255.0
+        img = np.expand_dims(img, axis=0).astype(np.float32)
+        return img
+
+    except HTTPException:
+        raise  # Re-raise already defined HTTP errors
+    except Exception as e:
+        raise HTTPException(
+            status_code=500, detail=f"Image preprocessing failed: {str(e)}"
+        )

features/image_classifier/routes.py

@@ -0,0 +1,26 @@
+from slowapi import Limiter
+from config import ACCESS_RATE
+from fastapi import APIRouter, File, Request, Depends, HTTPException, UploadFile
+from fastapi.security import HTTPBearer
+from slowapi import Limiter
+from slowapi.util import get_remote_address
+from .controller import Classify_Image_router
+router = APIRouter()
+limiter = Limiter(key_func=get_remote_address)
+security = HTTPBearer()
+
+@router.post("/analyse")
+@limiter.limit(ACCESS_RATE)
+async def analyse(
+    request: Request,
+    file: UploadFile = File(...),
+    token: str = Depends(security)
+):
+    result = await Classify_Image_router(file)  # await the async function
+    return result
+
+@router.get("/health")
+@limiter.limit(ACCESS_RATE)
+def health(request: Request):
+    return {"status": "ok"}
+

features/image_edit_detector/controller.py

@@ -0,0 +1,49 @@
+from PIL import Image
+import io
+from io import BytesIO
+from .detectors.fft import run_fft
+from .detectors.metadata import run_metadata
+from .detectors.ela import run_ela
+from .preprocess import preprocess_image
+from fastapi import HTTPException,status,Depends
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+security=HTTPBearer()
+import os
+async def process_image_ela(image_bytes: bytes, quality: int=90):
+    image = Image.open(io.BytesIO(image_bytes))
+
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    compressed_image = preprocess_image(image, quality)
+    ela_result = run_ela(compressed_image, quality)
+
+    return {
+        "is_edited": ela_result,
+        "ela_score": ela_result
+    }
+
+async def process_fft_image(image_bytes: bytes,threshold:float=0.95) -> dict:
+    image = Image.open(BytesIO(image_bytes)).convert("RGB")
+    result = run_fft(image,threshold)
+    return {"edited": bool(result)}
+
+
+async def process_meta_image(image_bytes: bytes) -> dict:
+    try:
+        result = run_metadata(image_bytes)
+        return {"source": result}  # e.g. "edited", "phone_capture", "unknown"
+    except Exception as e:
+        # Handle errors gracefully, return useful message or raise HTTPException if preferred
+        return {"error": str(e)}
+
+
+async def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
+    token = credentials.credentials
+    expected_token = os.getenv("MY_SECRET_TOKEN")
+    if token != expected_token:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Invalid or expired token"
+        )
+    return token

features/image_edit_detector/detectors/ela.py

@@ -0,0 +1,32 @@
+from PIL import Image, ImageChops, ImageEnhance
+import io
+
+
+def run_ela(image: Image.Image, quality: int = 90, threshold: int = 15) -> bool:
+    """
+    Perform Error Level Analysis to detect image manipulation.
+
+    Parameters:
+        image (PIL.Image): Input image (should be RGB).
+        quality (int): JPEG compression quality for ELA.
+        threshold (int): Maximum pixel difference threshold to classify as edited.
+
+    Returns:
+        bool: True if image appears edited, False otherwise.
+    """
+
+    # Recompress the image into JPEG format in memory
+    buffer = io.BytesIO()
+    image.save(buffer, format="JPEG", quality=quality)
+    buffer.seek(0)
+    recompressed = Image.open(buffer)
+
+    # Compute the pixel-wise difference
+    diff = ImageChops.difference(image, recompressed)
+    extrema = diff.getextrema()
+    max_diff = max([ex[1] for ex in extrema])
+
+    # Enhance difference image for debug (not returned)
+    _ = ImageEnhance.Brightness(diff).enhance(10)
+
+    return max_diff > threshold

features/image_edit_detector/detectors/fft.py

@@ -0,0 +1,40 @@
+import numpy as np
+from PIL import Image
+from scipy.fft import fft2, fftshift
+
+
+def run_fft(image: Image.Image, threshold: float = 0.92) -> bool:
+    """
+    Detects potential image manipulation or generation using FFT-based high-frequency analysis.
+
+    Parameters:
+        image (PIL.Image.Image): The input image.
+        threshold (float): Proportion of high-frequency components above which the image is flagged.
+
+    Returns:
+        bool: True if the image is likely AI-generated or manipulated, False otherwise.
+    """
+    gray_image = image.convert("L")
+
+    resized_image = gray_image.resize((512, 512))
+
+
+    image_array = np.array(resized_image)
+
+    fft_result = fft2(image_array)
+
+    fft_shifted = fftshift(fft_result)
+
+    magnitude_spectrum = np.abs(fft_shifted)
+    max_magnitude = np.max(magnitude_spectrum)
+    if max_magnitude == 0:
+        return False  # Avoid division by zero if image is blank
+    normalized_spectrum = magnitude_spectrum / max_magnitude
+
+    high_freq_mask = normalized_spectrum > 0.5
+
+    high_freq_ratio = np.sum(high_freq_mask) / normalized_spectrum.size
+
+    is_fake = high_freq_ratio > threshold
+    return is_fake
+

features/image_edit_detector/detectors/metadata.py

@@ -0,0 +1,82 @@
+from PIL import Image, UnidentifiedImageError
+import io
+
+# Common AI metadata identifiers in image files.
+AI_INDICATORS = [
+    b'c2pa', b'claim_generator', b'claim_generator_info',
+    b'created_software_agent', b'actions.v2', b'assertions',
+    b'urn:c2pa', b'jumd', b'jumb', b'jumdcbor', b'jumdc2ma',
+    b'jumdc2as', b'jumdc2cl', b'cbor', b'convertedsfwareagent',b'c2pa.version', 
+    b'c2pa.assertions', b'c2pa.actions',
+    b'c2pa.thumbnail', b'c2pa.signature', b'c2pa.manifest',
+    b'c2pa.manifest_store', b'c2pa.ingredient', b'c2pa.parent',
+    b'c2pa.provenance', b'c2pa.claim', b'c2pa.hash', b'c2pa.authority',
+    b'jumdc2pn', b'jumdrefs', b'jumdver', b'jumdmeta',
+    
+    
+   'midjourney'.encode('utf-8'),
+   'stable-diffusion'.encode('utf-8'),
+   'stable diffusion'.encode('utf-8'),
+   'stable_diffusion'.encode('utf-8'),
+   'artbreeder'.encode('utf-8'),
+   'runwayml'.encode('utf-8'),
+   'remix.ai'.encode('utf-8'),
+   'firefly'.encode('utf-8'),
+   'adobe_firefly'.encode('utf-8'),
+
+    # OpenAI / DALL·E indicators (all encoded to bytes)
+    'openai'.encode('utf-8'),
+    'dalle'.encode('utf-8'),
+    'dalle2'.encode('utf-8'),
+    'DALL-E'.encode('utf-8'),
+    'DALL·E'.encode('utf-8'),
+    'created_by: openai'.encode('utf-8'),
+    'tool: dalle'.encode('utf-8'),
+    'tool: dalle2'.encode('utf-8'),
+    'creator: openai'.encode('utf-8'),
+    'creator: dalle'.encode('utf-8'),
+    'openai.com'.encode('utf-8'),
+    'api.openai.com'.encode('utf-8'),
+    'openai_model'.encode('utf-8'),
+    'openai_gpt'.encode('utf-8'),
+    
+    #Further possible AI-Generation Indicators
+    'generated_by'.encode('utf-8'),
+    'model_id'.encode('utf-8'),
+    'model_version'.encode('utf-8'),
+    'model_info'.encode('utf-8'),
+    'tool_name'.encode('utf-8'),
+    'tool_creator'.encode('utf-8'),
+    'tool_version'.encode('utf-8'),
+    'model_signature'.encode('utf-8'),
+    'ai_model'.encode('utf-8'),
+    'ai_tool'.encode('utf-8'),
+    'generator'.encode('utf-8'),
+    'generated_by_ai'.encode('utf-8'),
+    'ai_generated'.encode('utf-8'),
+    'ai_art'.encode('utf-8')
+    ]
+
+
+def run_metadata(image_bytes: bytes) -> str:
+    try:
+        img = Image.open(io.BytesIO(image_bytes))
+        img.load()
+
+        exif = img.getexif()
+        software = str(exif.get(305, "")).strip()
+
+        suspicious_editors = ["Photoshop", "GIMP", "Snapseed", "Pixlr", "VSCO", "Editor", "Adobe", "Luminar"]
+
+        if any(editor.lower() in software.lower() for editor in suspicious_editors):
+            return "edited"
+
+        if any(indicator in image_bytes for indicator in AI_INDICATORS):
+            return "ai_generated"
+
+        return "undetermined"
+
+    except UnidentifiedImageError:
+        return "error: invalid image format"
+    except Exception as e:
+        return f"error: {str(e)}"

features/image_edit_detector/preprocess.py

@@ -0,0 +1,9 @@
+from PIL import Image
+import io
+
+def preprocess_image(img: Image.Image, quality: int) -> Image.Image:
+    buffer = io.BytesIO()
+    img.save(buffer, format="JPEG", quality=quality)
+    buffer.seek(0)
+    return Image.open(buffer)
+

features/image_edit_detector/routes.py

@@ -0,0 +1,53 @@
+from slowapi import Limiter
+from config import ACCESS_RATE
+from fastapi import APIRouter, File, Request, Depends, HTTPException, UploadFile
+from fastapi.security import HTTPBearer
+from slowapi import Limiter
+from slowapi.util import get_remote_address
+from io import BytesIO
+from .controller import process_image_ela , verify_token,process_fft_image, process_meta_image
+import requests
+router = APIRouter()
+limiter = Limiter(key_func=get_remote_address)
+security = HTTPBearer()
+
+
+
+@router.post("/ela")
+@limiter.limit(ACCESS_RATE)
+async def detect_ela(request:Request,file: UploadFile = File(...), quality: int = 90 ,token: str = Depends(verify_token)):
+    # Check file extension
+    allowed_types = ["image/jpeg", "image/png"]
+
+    if file.content_type not in allowed_types:
+        raise HTTPException(
+            status_code=400,
+            detail="Unsupported file type. Only JPEG and PNG images are allowed."
+        )
+    
+    content = await file.read()
+    result = await process_image_ela(content, quality)
+    return result
+
+@router.post("/fft")
+@limiter.limit(ACCESS_RATE)
+async def detect_fft(request:Request,file:UploadFile =File(...),threshold:float=0.95,token:str=Depends(verify_token)):
+    if file.content_type not in ["image/jpeg", "image/png"]:
+        raise HTTPException(status_code=400, detail="Unsupported image type.")
+    
+    content = await file.read()
+    result = await process_fft_image(content,threshold)
+    return result
+
+@router.post("/meta")
+@limiter.limit(ACCESS_RATE)
+async def detect_meta(request:Request,file:UploadFile=File(...),token:str=Depends(verify_token)):
+    if file.content_type not in ["image/jpeg", "image/png"]:
+        raise HTTPException(status_code=400, detail="Unsupported image type.")
+    content = await file.read()    
+    result = await process_meta_image(content)
+    return result
+@router.post("/health")
+@limiter.limit(ACCESS_RATE)
+def heath(request:Request):
+    return {"status":"ok"}

features/nepali_text_classifier/controller.py

@@ -3,7 +3,6 @@ from io import BytesIO
 from fastapi import HTTPException, UploadFile, status, Depends
 from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
 import os
-
 from features.nepali_text_classifier.inferencer import classify_text
 from  features.nepali_text_classifier.preprocess import *
 import re

features/nepali_text_classifier/model_loader.py

@@ -8,7 +8,7 @@ from huggingface_hub import snapshot_download
 from transformers import AutoTokenizer, AutoModel
 
 # Configs
-REPO_ID = "Pujan-Dev/Nepali-AI-VS-HUMAN"
+REPO_ID = "can-org/Nepali-AI-VS-HUMAN"
 BASE_DIR = "./np_text_model"
 TOKENIZER_DIR = os.path.join(BASE_DIR, "classifier")  # <- update this to match your uploaded folder
 WEIGHTS_PATH = os.path.join(BASE_DIR, "model_95_acc.pth")  # <- change to match actual uploaded weight

features/nepali_text_classifier/preprocess.py

@@ -20,19 +20,17 @@ def parse_pdf(file: BytesIO):
         for page_num in range(doc.page_count):
             page = doc.load_page(page_num)
             text += page.get_text()
-        return text
+        return text 
     except Exception as e:
         logging.error(f"Error while processing PDF: {str(e)}")
         raise HTTPException(
             status_code=500, detail="Error processing PDF file")
 
-
 def parse_txt(file: BytesIO):
     return file.read().decode("utf-8")
 
-
-def end_symbol_for_NP_text(text):
-        if not text.endswith("।"):
-            text += "।"
-
-
+def end_symbol_for_NP_text(text: str) -> str:
+    text = text.strip()
+    if not text.endswith("।"):
+        text += "।"
+    return text

features/text_classifier/model_loader.py

@@ -6,7 +6,7 @@ from huggingface_hub import snapshot_download
 import torch
 from dotenv import load_dotenv
 load_dotenv()
-REPO_ID = "Pujan-Dev/AI-Text-Detector"
+REPO_ID = "can-org/AI-Content-Checker"
 MODEL_DIR = "./models"
 TOKENIZER_DIR = os.path.join(MODEL_DIR, "model")
 WEIGHTS_PATH = os.path.join(MODEL_DIR, "model_weights.pth")

license.md

@@ -0,0 +1,20 @@
+# License - All Rights Reserved
+
+Copyright (c) 2025 CyberAlertNepal
+
+This software and all associated materials are **not open source** and are protected under a custom license.
+
+## Strict Usage Terms
+
+Unless explicit written permission is granted by **CyberAlertNepal**, **no individual or entity** is allowed to:
+
+- Use this codebase or its models in any capacity — personal, educational, or commercial.
+- Modify, copy, distribute, or sublicense any part of this project.
+- Deploy, mirror, or host this project, either publicly or privately.
+- Incorporate any component of this project into derivative works or other applications.
+
+This project is intended for **private, internal use by the author(s) only**.
+
+Any unauthorized usage, reproduction, or distribution is strictly prohibited and may result in legal action.
+
+**All rights reserved.**

readme.md

@@ -1,35 +0,0 @@
-# 🚀 FastAPI AI Detector
-
-A production-ready FastAPI app for detecting AI vs. human-written text in English and Nepali. It uses GPT-2 and SentencePiece-based models, with Bearer token security.
-
-## 📂 Documentation
-
-- [Project Structure](docs/structure.md)
-- [API Endpoints](docs/api_endpoints.md)
-- [Setup & Installation](docs/setup.md)
-- [Deployment](docs/deployment.md)
-- [Security](docs/security.md)
-- [NestJS Integration](docs/nestjs_integration.md)
-- [Core Functions](docs/functions.md)
-
-## ⚡ Quick Start
-```bash
-uvicorn app:app --host 0.0.0.0 --port 8000
-```
-## 🚀 Deployment
-
-- **Local**: Use `uvicorn` as above.
-- **Railway/Heroku**: Use the provided `Procfile`.
-- **Hugging Face Spaces**: Use the `Dockerfile` for container deployment.
-
----
-
-## 💡 Tips
-
-- **Model files auto-download at first start** if not found.
-- **Keep `requirements.txt` up-to-date** after adding dependencies.
-- **All endpoints require the correct `Authorization` header**.
-- **For security**: Avoid committing `.env` to public repos.
-
----
-

requirements.txt

@@ -11,3 +11,10 @@ python-multipart
 slowapi
 spacy
 nltk
+tensorflow
+opencv-python
+pillow
+scipy
+fitz
+frontend
+tools