Update app.py

app.py

@@ -3,17 +3,18 @@ import shutil
 import subprocess
 import signal
 import gradio as gr
-from huggingface_hub import create_repo, HfApi, whoami, ModelCard
+from huggingface_hub import create_repo, HfApi, snapshot_download, whoami, ModelCard
 from gradio_huggingfacehub_search import HuggingfaceHubSearch
 from apscheduler.schedulers.background import BackgroundScheduler
 from textwrap import dedent
 
+# Ensure the token is set from the environment, if not prompt user
 HF_TOKEN = os.environ.get("HF_TOKEN")
 
 def ensure_valid_token(oauth_token):
-    if oauth_token is None or oauth_token.token is None:
+    if not oauth_token or not oauth_token.strip():
         raise ValueError("You must be logged in.")
-    return oauth_token.token
+    return oauth_token.strip()
 
 def generate_importance_matrix(model_path, train_data_path):
     imatrix_command = f"./llama-imatrix -m ../{model_path} -f {train_data_path} -ngl 99 --output-frequency 10"
@@ -37,14 +38,16 @@ def generate_importance_matrix(model_path, train_data_path):
         try:
             process.wait(timeout=5)
         except subprocess.TimeoutExpired:
-            print("Imatrix process still didn't terminate. Forcefully terminating process...")
+            print("Imatrix proc still didn't terminate. Forcefully terminating process...")
             process.kill()
 
     os.chdir("..")
+
     print("Importance matrix generation completed.")
 
 def split_upload_model(model_path, repo_id, oauth_token, split_max_tensors=256, split_max_size=None):
-    token = ensure_valid_token(oauth_token)
+    if not oauth_token or not oauth_token.strip():
+        raise ValueError("You have to be logged in.")
     
     split_cmd = f"llama.cpp/llama-gguf-split --split --split-max-tensors {split_max_tensors}"
     if split_max_size:
@@ -60,11 +63,11 @@ def split_upload_model(model_path, repo_id, oauth_token, split_max_tensors=256,
     if result.returncode != 0:
         raise Exception(f"Error splitting the model: {result.stderr}")
     print("Model split successfully!")
-     
+    
     sharded_model_files = [f for f in os.listdir('.') if f.startswith(model_path.split('.')[0])]
     if sharded_model_files:
         print(f"Sharded model files: {sharded_model_files}")
-        api = HfApi(token=token)
+        api = HfApi(token=oauth_token)
         for file in sharded_model_files:
             file_path = os.path.join('.', file)
             print(f"Uploading file: {file_path}")
@@ -201,7 +204,7 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
         card.save(f"README.md")
 
         if split_model:
-            split_upload_model(quantized_gguf_path, new_repo_id, oauth_token, split_max_tensors, split_max_size)
+            split_upload_model(quantized_gguf_path, new_repo_id, token, split_max_tensors, split_max_size)
         else:
             try:
                 print(f"Uploading quantized model: {quantized_gguf_path}")
@@ -241,123 +244,32 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
         shutil.rmtree(model_name, ignore_errors=True)
         print("Folder cleaned up successfully!")
 
-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 to use GGUF-my-repo.")
-    gr.LoginButton(min_width=250)
-
-    model_id = HuggingfaceHubSearch(
-        label="Hub Model ID",
-        placeholder="Search for model id on Huggingface",
-        search_type="model",
-    )
-
-    q_method = gr.Dropdown(
-        ["Q2_K", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"],
-        label="Quantization Method",
-        info="GGML quantization type",
-        value="Q4_K_M",
-        filterable=False,
-        visible=True
-    )
-
-    imatrix_q_method = gr.Dropdown(
-        ["IQ3_M", "IQ3_XXS", "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", "Q5_K_S"],
-        label="Imatrix Quantization Method",
-        info="GGML imatrix quants type",
-        value="IQ4_NL", 
-        filterable=False,
-        visible=False
-    )
-
-    use_imatrix = gr.Checkbox(
-        value=False,
-        label="Use Imatrix Quantization",
-        info="Use importance matrix for quantization."
-    )
-
-    private_repo = gr.Checkbox(
-        value=False,
-        label="Private Repo",
-        info="Create a private repo under your username."
-    )
-
-    train_data_file = gr.File(
-        label="Training Data File",
-        file_types=["txt"],
-        visible=False
+with gr.Blocks() as app:
+    gr.Markdown("# Model Processing")
+
+    # Input fields for model processing
+    with gr.Row():
+        model_id = gr.Textbox(label="Model ID", placeholder="e.g., user/model_name")
+        q_method = gr.Dropdown(["method1", "method2"], label="Quantization Method")
+        use_imatrix = gr.Checkbox(label="Use Importance Matrix")
+        imatrix_q_method = gr.Dropdown(["methodA", "methodB"], label="Importance Matrix Method")
+        private_repo = gr.Checkbox(label="Private Repository")
+        train_data_file = gr.File(label="Training Data File", type="file")
+        split_model = gr.Checkbox(label="Split Model")
+        split_max_tensors = gr.Number(label="Max Tensors (for splitting)", value=256)
+        split_max_size = gr.Number(label="Max Size (for splitting)", value=None)
+        oauth_token = gr.Textbox(label="Hugging Face Token", type="password")
+
+    # Output fields
+    result = gr.HTML()
+    img = gr.Image()
+
+    # Process button
+    process_button = gr.Button("Process Model")
+    process_button.click(
+        process_model,
+        inputs=[model_id, q_method, use_imatrix, imatrix_q_method, private_repo, train_data_file, split_model, split_max_tensors, split_max_size, oauth_token],
+        outputs=[result, img]
     )
 
-    split_model = gr.Checkbox(
-        value=False,
-        label="Split Model",
-        info="Shard the model using gguf-split."
-    )
-
-    split_max_tensors = gr.Number(
-        value=256,
-        label="Max Tensors per File",
-        info="Maximum number of tensors per file when splitting model.",
-        visible=False
-    )
-
-    split_max_size = gr.Textbox(
-        label="Max File Size",
-        info="Maximum file size when splitting model (--split-max-size). May leave empty to use the default.",
-        visible=False
-    )
-
-    def update_visibility(use_imatrix):
-        return gr.update(visible=not use_imatrix), gr.update(visible=use_imatrix), gr.update(visible=use_imatrix)
-    
-    use_imatrix.change(
-        fn=update_visibility,
-        inputs=use_imatrix,
-        outputs=[q_method, imatrix_q_method, train_data_file]
-    )
-
-    iface = gr.Interface(
-        fn=process_model,
-        inputs=[
-            model_id,
-            q_method,
-            use_imatrix,
-            imatrix_q_method,
-            private_repo,
-            train_data_file,
-            split_model,
-            split_max_tensors,
-            split_max_size,
-            gr.Textbox(value=HF_TOKEN, label="Hugging Face Token", type="password")  # Correct token input field
-        ],
-        outputs=[
-            gr.Markdown(label="output"),
-            gr.Image(show_label=False),
-        ],
-        title="Create your own GGUF Quants, blazingly fast ⚡!",
-        description="The space takes an HF repo as an input, quantizes it and creates a Public repo containing the selected quant under your HF user namespace.",
-        api_name=False
-    )
-
-    def update_split_visibility(split_model):
-        return gr.update(visible=split_model), gr.update(visible=split_model)
-
-    split_model.change(
-        fn=update_split_visibility,
-        inputs=split_model,
-        outputs=[split_max_tensors, split_max_size]
-    )
-
-def restart_space():
-    HfApi().restart_space(repo_id="ggml-org/gguf-my-repo", token=HF_TOKEN, factory_reboot=True)
-
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=21600)
-scheduler.start()
-
-# Launch the interface
-demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)
+app.launch()