Added :Added the support for the lastest model

.gitignore

@@ -13,6 +13,7 @@ __pycache__/
 .vscode/
 .idea/
 *.swp
+*Model/
 
 # ---- Jupyter / IPython ----
 .ipynb_checkpoints/

test.md

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-
-**Update: Edited & AI-Generated Content Detection – Project Plan**
-
-### 🔍 Phase 1: Rule-Based Image Detection (In Progress)
-
-We're implementing three core techniques to individually flag edited or AI-generated images:
-
-* **ELA (Error Level Analysis):** Highlights inconsistencies via JPEG recompression.
-* **FFT (Frequency Analysis):** Uses 2D Fourier Transform to detect unnatural image frequency patterns.
-* **Metadata Analysis:** Parses EXIF data to catch clues like editing software tags.
-
- These give us visual + interpretable results for each image, and currently offer \~60–70% accuracy on typical AI-edited content.
-
----
-
-###  Phase 2: AI vs Human Detection System (Coming Soon)
-
-**Goal:** Build an AI model that classifies whether content is AI- or human-made — initially focusing on **images**, and later expanding to **text**.
-
-**Data Strategy:**
-
-* Scraping large volumes of recent AI-gen images (e.g. SDXL, Gibbli, MidJourney).
-* Balancing with high-quality human images.
-
-**Model Plan:**
-
-* Use ELA, FFT, and metadata as feature extractors.
-* Feed these into a CNN or ensemble model.
-* Later, unify into a full web-based platform (upload → get AI/human probability).
-
-