OOP draft

app.py

@@ -56,6 +56,9 @@ class ModelProcessingConfig:
     quant_config: QuantizationConfig
     split_config: SplitConfig
     output_config: OutputConfig
+    # Generated values - These will be set during processing
+    new_repo_url: str = field(default="", init=False)
+    new_repo_id: str = field(default="", init=False)
 
 class GGUFConverterError(Exception):
     """Custom exception for GGUF conversion errors."""
@@ -321,6 +324,19 @@ class HuggingFaceModelProcessor:
         print(f"Quantized model path: {os.path.abspath(quant_config.quantized_gguf)}")
         return quant_config.quantized_gguf
 
+    def _create_empty_repo(self, processing_config: ModelProcessingConfig):
+        api = HfApi(token=processing_config.token)
+        new_repo_url = api.create_repo(
+            repo_id=processing_config.output_config.repo_name,
+            exist_ok=True,
+            private=processing_config.output_config.private_repo
+        )
+        processing_config.new_repo_url = new_repo_url.url
+        processing_config.new_repo_id = new_repo_url.repo_id
+        print("Repo created successfully!", processing_config.new_repo_url)
+
+        return new_repo_url
+
     def _generate_readme(self, outdir: str, token: str, model_id: str,
                         new_repo_id: str, gguf_name: str) -> str:
         """Generate README.md for the quantized model."""
@@ -384,23 +400,16 @@ llama-server --hf-repo "{new_repo_id}" --hf-file "{gguf_name}" -c 4096
         self._quantize_model(quant_config)
 
         # Create empty repo
-        api = HfApi(token=processing_config.token)
-        new_repo_url = api.create_repo(
-            repo_id=output_config.repo_name,
-            exist_ok=True,
-            private=output_config.private_repo
-        )
-        new_repo_id = new_repo_url.repo_id
-        print("Repo created successfully!", new_repo_url)
+        self._create_empty_repo(processing_config)
 
         # Upload model
         if split_config.enabled:
             print(f"Splitting quantized model: {os.path.abspath(quant_config.quantized_gguf)}")
-            self._split_and_upload_model(quant_config.quantized_gguf, processing_config.outdir, new_repo_id, processing_config.token, split_config)
+            self._split_and_upload_model(quant_config.quantized_gguf, processing_config.outdir, processing_config.new_repo_id, processing_config.token, split_config)
         else:
             try:
                 print(f"Uploading quantized model: {os.path.abspath(quant_config.quantized_gguf)}")
-                self._upload_file(processing_config.token, quant_config.quantized_gguf, output_config.filename, new_repo_id)
+                self._upload_file(processing_config.token, quant_config.quantized_gguf, output_config.filename, processing_config.new_repo_id)
             except Exception as e:
                 raise GGUFConverterError(f"Error uploading quantized model: {e}")
 
@@ -408,21 +417,16 @@ llama-server --hf-repo "{new_repo_id}" --hf-file "{gguf_name}" -c 4096
         if quant_config.use_imatrix and os.path.isfile(quant_config.imatrix_file):
             try:
                 print(f"Uploading imatrix.dat: {os.path.abspath(quant_config.imatrix_file)}")
-                self._upload_file(processing_config.token, quant_config.imatrix_file, f"{processing_config.model_name}-imatrix.dat", new_repo_id)
+                self._upload_file(processing_config.token, quant_config.imatrix_file, f"{processing_config.model_name}-imatrix.dat", processing_config.new_repo_id)
             except Exception as e:
                 raise GGUFConverterError(f"Error uploading imatrix.dat: {e}")
 
         # Upload README.md
-        readme_path = self._generate_readme(processing_config.outdir, processing_config.token, processing_config.model_id, new_repo_id, output_config.filename)
-        self._upload_file(processing_config.token, readme_path, "README.md", new_repo_id)
+        readme_path = self._generate_readme(processing_config.outdir, processing_config.token, processing_config.model_id, processing_config.new_repo_id, output_config.filename)
+        self._upload_file(processing_config.token, readme_path, "README.md", processing_config.new_repo_id)
 
         print(f"Uploaded successfully with {quant_config.imatrix_method if quant_config.use_imatrix else quant_config.method} option!")
 
-        return (
-            f'<h1>✅ DONE</h1><br/>Find your repo here: <a href="{new_repo_url}" target="_blank" style="text-decoration:underline">{new_repo_id}</a>',
-            "llama.png",
-        )
-
 
 class GGUFConverterUI:
     """Gradio UI for the GGUF Converter."""
@@ -793,7 +797,12 @@ class GGUFConverterUI:
                 )
 
                 # Call the processor's main method with the config object
-                return self.processor.process_model(processing_config)
+                self.processor.process_model(processing_config)
+
+                return (
+                    f'<h1>✅ DONE</h1><br/>Find your repo here: <a href="{processing_config.new_repo_url}" target="_blank" style="text-decoration:underline">{processing_config.new_repo_id}</a>',
+                    "llama.png",
+                )
 
         except Exception as e:
             print(f"Error processing model: {e}")