LOT 1 - Optimizare script build -Instalare mono comanda

ai_platform/modules/forensic_features/docs/INTEGRATION.md

@@ -35,7 +35,7 @@ import requests
 import base64
 from pathlib import Path
 
-FORENSIC_API = "http://localhost:8080"
+FORENSIC_API = "http://localhost:8085"
 QWEN_API     = "http://your-qwen-host:14011/v1/chat/completions"
 
 def analyze_video(video_path: str, your_typologies: list[str]) -> dict:
@@ -97,7 +97,7 @@ def analyze_video(video_path: str, your_typologies: list[str]) -> dict:
 
     # ── Pas 4: Apelează LLM-ul tău ───────────────────────────────────
     qwen_payload = {
-        "model": "Qwen3.5-397B-A17B",
+        "model": "qwen3.5",
         "messages": [
             {"role": "system", "content": "Ești analist forensic. Răspunzi în JSON valid."},
             {"role": "user", "content": content},
@@ -138,7 +138,7 @@ async function analyzeWithForensic(videoFile) {
     fd.append("video", videoFile);
     fd.append("encode_images", "1");
 
-    const forensicResp = await fetch("http://localhost:8080/api/forensic-evidence", {
+    const forensicResp = await fetch("http://localhost:8085/api/forensic-evidence", {
         method: "POST",
         body: fd,
     });
@@ -351,10 +351,10 @@ else:
 
 ```bash
 # Verifică serviciu
-curl http://localhost:8080/health
+curl http://localhost:8085/health
 
 # Test cu un video real
-curl -X POST http://localhost:8080/api/forensic-evidence \
+curl -X POST http://localhost:8085/api/forensic-evidence \
      -F "video=@your_test_video.mp4" \
      -o response.json