Update app.py

app.py

@@ -11,6 +11,7 @@ from gradio_huggingfacehub_search import HuggingfaceHubSearch
 from apscheduler.schedulers.background import BackgroundScheduler
 from datetime import datetime
 import numpy as np
+import shutil
 HF_TOKEN = os.environ.get("HF_TOKEN")
 
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
@@ -60,16 +61,22 @@ Run them directly with [llama.cpp](https://github.com/ggml-org/llama.cpp), or an
 First, make sure you have hugginface-cli installed:
 ```
 pip install -U "huggingface_hub[cli]"
+
 ```
 Then, you can target the specific file you want:
+
 ```
 huggingface-cli download {new_repo_url} --include "{gguf_files[0][0]}" --local-dir ./
+
 ```
 If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
+
 ```
 huggingface-cli download {new_repo_url} --include "{gguf_files[0][0]}/*" --local-dir ./
+
 ```
 You can either specify a new local-dir (deepseek-ai_DeepSeek-V3-0324-Q8_0) or download them all in place (./)
+
 </details>
 """
         return text
@@ -229,7 +236,8 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
           fp16 = str(Path(outdir)/f"{model_name}.fp16.gguf")
 
           with tempfile.TemporaryDirectory(dir=downloads_dir) as tmpdir:
-              print("Downloading")
+              print(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Start download")
+              logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Start download")
               local_dir = Path(tmpdir)/model_name
               api.snapshot_download(repo_id=model_id, local_dir=local_dir, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
               
@@ -237,12 +245,16 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
               adapter_config_dir = local_dir/"adapter_config.json"
               if os.path.exists(adapter_config_dir) and not os.path.exists(config_dir):
                   raise Exception("adapter_config.json is present. If converting LoRA, use GGUF-my-lora.")
+              print(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Download successfully")
+              logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Download successfully")
 
-              print("Download successfully")
               result = subprocess.run(["python", CONVERSION_SCRIPT, local_dir, "--outtype", "f16", "--outfile", fp16], shell=False, capture_output=True)
-              print("Converted to f16")
+              print(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Converted to f16")
+              logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Converted to f16")
+
               if result.returncode != 0:
                   raise Exception(f"Error converting to fp16: {result.stderr.decode()}")
+          shutil.rmtree(downloads_dir)
 
           imatrix_path = Path(outdir)/"imatrix.dat"
           if use_imatrix:
@@ -256,7 +268,9 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
 
           gguf_files = []
           for method in quant_methods:
-              print("Begin quantize")
+              print(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Begin quantize")
+              logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Begin quantize")
+
               name = f"{model_name.lower()}-{method.lower()}-{suffix}.gguf" if suffix else f"{model_name.lower()}-{method.lower()}.gguf"
               path = str(Path(outdir)/name)
               quant_cmd = ["./llama.cpp/llama-quantize", "--imatrix", imatrix_path, fp16, path, method] if use_imatrix else ["./llama.cpp/llama-quantize", fp16, path, method]
@@ -266,7 +280,9 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
               size = os.path.getsize(path)/1024/1024/1024
               gguf_files.append((name, path, size, method))
 
-          print("Quantize successfully!")
+          print(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Quantize successfully!")
+          logger.info(datetime.now().strftime("%Y-%m-%d %H:%M:%S") + " Quantize successfully!")
+
           suffix_for_repo = f"{imatrix_q_method}-imat" if use_imatrix else "-".join(quant_methods)
           repo_id = f"{repo_namespace}/{model_name}-GGUF"
           new_repo_url = api.create_repo(repo_id=repo_id, exist_ok=True, private=private_repo)