Chnaged requirement text

app/routes.py

@@ -8,7 +8,7 @@ from huggingface_hub import InferenceApi
 from PIL import Image
 
 from app.config import Config
-from app.models import audio_model, sentiment_pipeline, emotion_pipeline
+from app.models import audio_model, sentiment_pipeline, emotion_pipeline, client
 from app.services import extract_tasks
 from app.utils import generate_tags, error_response
 
@@ -19,12 +19,6 @@ model_dir = os.getenv('EASYOCR_MODEL_STORAGE', None)
 
 # ── OCR via HF Inference API ─────────────────────────────────────────────────
 # We're using Microsoft's TrOCR for printed text:
-HF_API_TOKEN = Config.FIREWORKS_API_KEY
-ocr_api = InferenceApi(
-    repo_id="microsoft/trocr-base-printed",
-    token=HF_API_TOKEN,
-    inference_type="text-generation"  # TroCR is a seq2seq model
-)
 
 EMOTION_SCORE_THRESHOLD = 0.15  # Adjust based on your testing
 MIN_SENTIMENT_CONFIDENCE = 0.4  # Below this becomes "neutral"
@@ -104,7 +98,7 @@ def analyze_image():
 
             try:
                 # 1) Ask the vision-LLM to describe / extract text
-                completion = hf.chat.completions.create(
+                completion = client.chat.completions.create(
                     model="google/gemma-3-27b-it",
                     messages=[{
                         "role": "user",