Update app.py (#1)

- Update app.py (526fc6581d88c35d03e3bb0b86a051a68c54993e)

app.py

@@ -1,64 +1,95 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import os
+import requests
+import json
+from huggingface_hub import HfApi, create_repo
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+def download_file(digest, image):
+    url = f"https://registry.ollama.ai/v2/library/{image}/blobs/{digest}"
+    file_name = f"blobs/{digest}"
 
+    # Create the directory if it doesn't exist
+    os.makedirs(os.path.dirname(file_name), exist_ok=True)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    # Download the file
+    print(f"Downloading {url} to {file_name}")
+    response = requests.get(url, allow_redirects=True)
+    if response.status_code == 200:
+        with open(file_name, 'wb') as f:
+            f.write(response.content)
+    else:
+        print(f"Failed to download {url}")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def fetch_manifest(image, tag):
+    manifest_url = f"https://registry.ollama.ai/v2/library/{image}/manifests/{tag}"
+    response = requests.get(manifest_url)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        return None
 
-    messages.append({"role": "user", "content": message})
+def upload_to_huggingface(repo_id, folder_path):
+    api = HfApi()
+    repo_path = create_repo(repo_id, "model", exists_ok=True)
+    print(f"Repo created {repo_path}")
+    try:
+        api.upload_folder(
+            folder_path=folder_path,
+            repo_id=repo_id,
+            repo_type="model",
+        )
+        return "Upload successful"
+    except Exception as e:
+        return f"Upload failed: {str(e)}"
 
-    response = ""
+def process_image_tag(image_tag, repo_id):
+    # Extract image and tag from the input
+    image, tag = image_tag.split(':')
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    # Fetch the manifest JSON
+    manifest_json = fetch_manifest(image, tag)
+    if not manifest_json or 'errors' in manifest_json:
+        return f"Failed to fetch the manifest for {image}:{tag}"
 
-        response += token
-        yield response
+    # Save the manifest JSON to the blobs folder
+    manifest_file_path = "blobs/manifest.json"
+    os.makedirs(os.path.dirname(manifest_file_path), exist_ok=True)
+    with open(manifest_file_path, 'w') as f:
+        json.dump(manifest_json, f)
 
+    # Extract the digest values from the JSON
+    digests = [layer['digest'] for layer in manifest_json.get('layers', [])]
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+    # Download each file
+    for digest in digests:
+        download_file(digest, image)
+
+    # Download the config file
+    config_digest = manifest_json.get('config', {}).get('digest')
+    if config_digest:
+        download_file(config_digest, image)
+
+    # Upload to Hugging Face Hub
+    upload_result = upload_to_huggingface(repo_id, 'blobs/*')
+
+    # Delete the blobs folder
+    try:
+        os.rmtree('blobs')
+        return f"Successfully fetched and downloaded files for {image}:{tag}\n{upload_result}\nBlobs folder deleted"
+    except Exception as e:
+        return f"Failed to delete blobs folder: {str(e)}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=process_image_tag,
+    inputs=[
+        gr.Textbox(placeholder="Enter image:tag", label="Image and Tag"),
+        gr.Textbox(placeholder="Enter Hugging Face repo ID", label="Hugging Face Repo ID")
     ],
+    outputs=gr.Textbox(label="Result"),
+    title="Registry File Downloader and Uploader",
+    description="Enter the image and tag to download the corresponding files from the registry and upload them to the Hugging Face Hub."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio app
+iface.launch()