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| import os | |
| from google import genai | |
| from google.genai import types | |
| from PIL import Image | |
| import json | |
| class VLMAnalyzer: | |
| def __init__(self): | |
| self.api_key = os.getenv("GEMINI_API_KEY") | |
| self.enabled = bool(self.api_key) | |
| if self.enabled: | |
| self.client = genai.Client(api_key=self.api_key) | |
| else: | |
| print("VLMAnalyzer disabled: GEMINI_API_KEY not found in .env") | |
| def analyze_frame(self, image_array): | |
| if not self.enabled: | |
| return None | |
| try: | |
| pil_img = Image.fromarray(image_array).convert("RGB") | |
| w, h = pil_img.width, pil_img.height | |
| prompt = f""" | |
| You are an expert deepfake detection AI. | |
| Analyze the provided image for AI-generation artifacts, unnatural physics, logical inconsistencies, face swaps, or morphed hands/limbs. | |
| Return a JSON strictly following this schema: | |
| {{ | |
| "is_ai_generated": boolean, | |
| "semantic_fake_score": float (0.0 to 1.0), | |
| "reasoning": string (Detailed explanation of WHY it is fake based on physics, anatomy, or context. e.g. "The dog is walking on two feet". If real, explain why.), | |
| "anomaly_regions": [ | |
| {{ | |
| "box": [x, y, w, h] (approximate integer pixel coordinates relative to image width {w} and height {h}), | |
| "label": string (e.g., "morphed hand", "unnatural posture") | |
| }} | |
| ] | |
| }} | |
| """ | |
| response = self.client.models.generate_content( | |
| model='gemini-2.5-flash', | |
| contents=[prompt, pil_img], | |
| config=types.GenerateContentConfig( | |
| response_mime_type="application/json", | |
| ), | |
| ) | |
| text = response.text | |
| if "```json" in text: | |
| text = text.split("```json")[1].split("```")[0].strip() | |
| elif "```" in text: | |
| text = text.split("```")[1].strip() | |
| return json.loads(text) | |
| except Exception as e: | |
| print(f"VLM Analysis Error: {e}") | |
| return None | |