Create app.py

app.py

@@ -0,0 +1,116 @@
+import os
+import subprocess
+import signal
+os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+import gradio as gr
+import tempfile
+
+from huggingface_hub import HfApi, ModelCard, whoami
+from gradio_huggingfacehub_search import HuggingfaceHubSearch
+from pathlib import Path
+from textwrap import dedent
+
+
+def process_model(ft_model_id: str, base_model_id: str, rank: str, private_repo, oauth_token: gr.OAuthToken | None):
+    if oauth_token is None or oauth_token.token is None:
+        raise gr.Error("You must be logged in")
+    model_name = ft_model_id.split('/')[-1]
+
+    if not os.path.exists("outputs"):
+        os.makedirs("outputs")
+
+    try:
+        api = HfApi(token=oauth_token.token)
+        
+        with tempfile.TemporaryDirectory(dir="outputs") as outputdir:
+            result = subprocess.run([
+                "mergekit-extract-lora",
+                ft_model_id,
+                base_model_id,
+                outputdir,
+                f"--rank={rank}",
+            ], shell=False, capture_output=True)
+            print(result)
+            if result.returncode != 0:
+                raise Exception(f"Error converting to LoRA PEFT {q_method}: {result.stderr}")
+            print("Model converted to LoRA PEFT successfully!")
+            print(f"Converted model path: {outputdir}")
+
+            # Check output dir
+            if not os.listdir(outputdir):
+                raise Exception("Output directory is empty!")
+
+            # Create repo
+            username = whoami(oauth_token.token)["name"]
+            new_repo_url = api.create_repo(repo_id=f"{username}/LoRA-{model_name}", exist_ok=True, private=private_repo)
+            new_repo_id = new_repo_url.repo_id
+            print("Repo created successfully!", new_repo_url)
+
+            # Upload files
+            api.upload_file(
+                folder_path=outputdir,
+                path_in_repo="",
+                repo_id=new_repo_id,
+            )
+            print("Uploaded", outputdir)
+
+        return (
+            f'<h1>✅ DONE</h1><br/><br/>Find your repo here: <a href="{new_repo_url}" target="_blank" style="text-decoration:underline">{new_repo_id}</a>'
+        )
+    except Exception as e:
+        return (f"<h1>❌ ERROR</h1><br/><br/>{e}")
+
+
+css="""/* Custom CSS to allow scrolling */
+.gradio-container {overflow-y: auto;}
+"""
+# Create Gradio interface
+with gr.Blocks(css=css) as demo: 
+    gr.Markdown("You must be logged in.")
+    gr.LoginButton(min_width=250)
+
+    ft_model_id = HuggingfaceHubSearch(
+        label="Fine tuned model repository",
+        placeholder="Search for repository on Huggingface",
+        search_type="model",
+    )
+
+    base_model_id = HuggingfaceHubSearch(
+        label="Base tuned model repository",
+        placeholder="Search for repository on Huggingface",
+        search_type="model",
+    )
+
+    rank = gr.Dropdown(
+        ["16", "32", "64", "128"],
+        label="LoRA rank",
+        info="Higher the rank, better the result, but heavier the adapter",
+        value="32",
+        filterable=False,
+        visible=True
+    )
+
+    private_repo = gr.Checkbox(
+        value=False,
+        label="Private Repo",
+        info="Create a private repo under your username."
+    )
+
+    iface = gr.Interface(
+        fn=process_model,
+        inputs=[
+            ft_model_id,
+            base_model_id,
+            rank,
+            private_repo,
+        ],
+        outputs=[
+            gr.Markdown(label="output"),
+        ],
+        title="Convert fine tuned model into LoRA with mergekit-extract-lora",
+        description="The space takes a fine tuned model, a base model, then make a PEFT-compatible LoRA adapter based on the difference between 2 models.",
+        api_name=False
+    )
+
+# Launch the interface
+demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)