Update app_fast_CPU.py

app_fast_CPU.py

@@ -16,9 +16,11 @@ HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/wan22TITV5B-image-analys
 # --- CPU-only upload function ---
 def upload_image_and_prompt_cpu(input_image, prompt_text) -> str:
     from datetime import datetime
-    import tempfile, os, uuid
-    from huggingface_hub import upload_file
-    import shutil
+    import tempfile, os, uuid, shutil
+    from huggingface_hub import HfApi
+
+    # Instantiate the HfApi class
+    api = HfApi()
 
     today_str = datetime.now().strftime("%Y-%m-%d")
     unique_subfolder = f"Upload-Image-{uuid.uuid4().hex[:8]}"
@@ -32,21 +34,35 @@ def upload_image_and_prompt_cpu(input_image, prompt_text) -> str:
             input_image.save(tmp_img.name, format="PNG")
         tmp_img_path = tmp_img.name
 
-    # Upload image
-    upload_file(tmp_img_path, f"{hf_folder}/input_image.png", repo_id=HF_MODEL,
-                repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN"))
+    # Upload image using HfApi instance
+    api.upload_file(
+        path_or_fileobj=tmp_img_path,
+        path_in_repo=f"{hf_folder}/input_image.png",
+        repo_id=HF_MODEL,
+        repo_type="model",
+        token=os.environ.get("HUGGINGFACE_HUB_TOKEN")
+    )
 
     # Save prompt as summary.txt
     summary_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name
     with open(summary_file, "w", encoding="utf-8") as f:
         f.write(prompt_text)
-    upload_file(summary_file, f"{hf_folder}/summary.txt", repo_id=HF_MODEL,
-                repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN"))
 
+    api.upload_file(
+        path_or_fileobj=summary_file,
+        path_in_repo=f"{hf_folder}/summary.txt",
+        repo_id=HF_MODEL,
+        repo_type="model",
+        token=os.environ.get("HUGGINGFACE_HUB_TOKEN")
+    )
+
+    # Cleanup
     os.remove(tmp_img_path)
     os.remove(summary_file)
+
     return hf_folder
 
+
 # --- Load pipelines ---
 vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
 text_to_video_pipe = WanPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16)