Update app_fast.py

app_fast.py

@@ -11,6 +11,72 @@ import random
 
 MODEL_ID = "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers"
 
+HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/wan22TITV5B-image-analysis")
+
+def upload_image_and_prompt(input_image, prompt_text) -> str:
+    """
+    Upload an image and a prompt text to Hugging Face Hub in a date-based folder.
+    
+    Args:
+        input_image (PIL.Image.Image or path-like): The image to upload.
+        prompt_text (str): Text prompt or summary associated with the image.
+
+    Returns:
+        str: Hugging Face folder path where the image and prompt were uploaded.
+    """
+    import tempfile
+    import os
+    import uuid
+    from datetime import datetime
+    from huggingface_hub import upload_file
+
+    # Create a date-based folder on HF
+    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 the image temporarily
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_img:
+        if isinstance(input_image, str):
+            # If path provided, just copy
+            import shutil
+            shutil.copy(input_image, tmp_img.name)
+        else:
+            # PIL.Image.Image
+            input_image.save(tmp_img.name, format="PNG")
+        tmp_img_path = tmp_img.name
+
+    # Upload image
+    image_filename = "input_image.png"
+    image_hf_path = f"{hf_folder}/{image_filename}"
+    upload_file(
+        path_or_fileobj=tmp_img_path,
+        path_in_repo=image_hf_path,
+        repo_id=HF_MODEL,
+        repo_type="model",
+        token=os.environ.get("HUGGINGFACE_HUB_TOKEN"),
+    )
+
+    # Upload 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)
+    summary_hf_path = f"{hf_folder}/summary.txt"
+    upload_file(
+        path_or_fileobj=summary_file,
+        path_in_repo=summary_hf_path,
+        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
+
+
 #MODEL_ID ="linoyts/Wan2.2-VACE-Fun-14B-diffusers"
 vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
 
@@ -132,6 +198,9 @@ def generate_video(input_image, prompt, height, width, negative_prompt=default_n
     current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
     print("prompt is")
     print(prompt)
+    # After generating or receiving input image
+    hf_folder = upload_image_and_prompt(input_image, prompt)
+
 
     if input_image is not None:
         resized_image = input_image.resize((target_w, target_h))