Update app_lora_cpu.py

app_lora_cpu.py

@@ -302,52 +302,99 @@ HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/wan22-aot-image-2026-jan
 # --- CPU-only upload function ---
 def upload_image_and_prompt_cpu(input_image, prompt_text) -> str:
     from datetime import datetime
-    import tempfile, os, uuid, shutil
+    import tempfile
+    import os
+    import uuid
+    import shutil
+    import unicodedata
+
     from huggingface_hub import HfApi
 
-    # Instantiate the HfApi class
     api = HfApi()
 
+    # -----------------------------
+    # CLEAN TEXT
+    # -----------------------------
+    if prompt_text is None:
+        prompt_text = ""
+
+    # Replace problematic unicode chars
+    prompt_text = (
+        unicodedata.normalize("NFKC", str(prompt_text))
+        .replace("\xa0", " ")
+        .replace("\u200b", "")
+    )
+
+    # -----------------------------
+    # CREATE SAFE HF FOLDER
+    # -----------------------------
     today_str = datetime.now().strftime("%Y-%m-%d")
     unique_subfolder = f"Upload-Image-{uuid.uuid4().hex[:8]}"
     hf_folder = f"{today_str}/{unique_subfolder}"
 
-    # Save image temporarily
+    # Ensure ASCII-safe repo path
+    hf_folder = hf_folder.encode("ascii", "ignore").decode()
+
+    # -----------------------------
+    # SAVE IMAGE TEMP
+    # -----------------------------
     with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_img:
         if isinstance(input_image, str):
             shutil.copy(input_image, tmp_img.name)
         else:
             input_image.save(tmp_img.name, format="PNG")
+
         tmp_img_path = tmp_img.name
 
-    # Upload image using HfApi instance
+    # -----------------------------
+    # UPLOAD IMAGE
+    # -----------------------------
     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")
+        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)
-
+    # -----------------------------
+    # SAVE SUMMARY TXT
+    # -----------------------------
+    with tempfile.NamedTemporaryFile(
+        mode="w",
+        encoding="utf-8",
+        suffix=".txt",
+        delete=False,
+    ) as tmp_txt:
+
+        tmp_txt.write(prompt_text)
+        summary_file = tmp_txt.name
+
+    # -----------------------------
+    # UPLOAD TXT
+    # -----------------------------
     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")
+        token=os.environ.get("HUGGINGFACE_HUB_TOKEN"),
     )
 
-    # Cleanup
-    os.remove(tmp_img_path)
-    os.remove(summary_file)
+    # -----------------------------
+    # CLEANUP
+    # -----------------------------
+    try:
+        os.remove(tmp_img_path)
+    except:
+        pass
 
-    return hf_folder
+    try:
+        os.remove(summary_file)
+    except:
+        pass
 
+    return hf_folder
 def get_num_frames(duration_seconds: float):
     return 1 + int(np.clip(
         int(round(duration_seconds * FIXED_FPS)),