Update app.py

app.py

@@ -2,6 +2,8 @@ import os
 import shutil
 import subprocess
 import signal
+import re
+
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 import gradio as gr
 
@@ -9,6 +11,7 @@ from huggingface_hub import create_repo, HfApi
 from huggingface_hub import snapshot_download
 from huggingface_hub import whoami
 from huggingface_hub import ModelCard
+from huggingface_hub.utils import RepositoryNotFoundError
 
 from gradio_huggingfacehub_search import HuggingfaceHubSearch
 
@@ -20,59 +23,35 @@ HF_TOKEN = os.environ.get("HF_TOKEN")
 
 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"
-
     os.chdir("llama.cpp")
-
-    print(f"Current working directory: {os.getcwd()}")
-    print(f"Files in the current directory: {os.listdir('.')}")
-
     if not os.path.isfile(f"../{model_path}"):
         raise Exception(f"Model file not found: {model_path}")
-
-    print("Running imatrix command...")
     process = subprocess.Popen(imatrix_command, shell=True)
-
     try:
-        process.wait(timeout=60)  # added wait
+        process.wait(timeout=60)
     except subprocess.TimeoutExpired:
-        print("Imatrix computation timed out. Sending SIGINT to allow graceful termination...")
         process.send_signal(signal.SIGINT)
         try:
-            process.wait(timeout=5)  # grace period
+            process.wait(timeout=5)
         except subprocess.TimeoutExpired:
-            print("Imatrix proc still didn't term. Forecfully terming process...")
             process.kill()
-
     os.chdir("..")
 
-    print("Importance matrix generation completed.")
-
-def split_upload_model(model_path, repo_id, oauth_token: gr.OAuthToken | None, split_max_tensors=256, split_max_size=None):
+def split_upload_model(model_path, repo_id, oauth_token, split_max_tensors=256, split_max_size=None):
     if oauth_token.token is None:
         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:
         split_cmd += f" --split-max-size {split_max_size}"
     split_cmd += f" {model_path} {model_path.split('.')[0]}"
-    
-    print(f"Split command: {split_cmd}") 
-    
     result = subprocess.run(split_cmd, shell=True, capture_output=True, text=True)
-    print(f"Split command stdout: {result.stdout}") 
-    print(f"Split command stderr: {result.stderr}") 
-    
     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=oauth_token.token)
         for file in sharded_model_files:
             file_path = os.path.join('.', file)
-            print(f"Uploading file: {file_path}")
             try:
                 api.upload_file(
                     path_or_fileobj=file_path,
@@ -83,123 +62,97 @@ def split_upload_model(model_path, repo_id, oauth_token: gr.OAuthToken | None, s
                 raise Exception(f"Error uploading file {file_path}: {e}")
     else:
         raise Exception("No sharded files found.")
-    
-    print("Sharded model has been uploaded successfully!")
 
-def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_repo, train_data_file, split_model, split_max_tensors, split_max_size, oauth_token: gr.OAuthToken | None):
+def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_repo, train_data_file, split_model, split_max_tensors, split_max_size, oauth_token):
     if oauth_token.token is None:
         raise ValueError("You must be logged in to use GGUF-my-repo")
     model_name = model_id.split('/')[-1]
-    fp16 = f"{model_name}.fp16.gguf"
-
     try:
         api = HfApi(token=oauth_token.token)
+        try:
+            # Attempt to download using the model ID directly
+            snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False)
+        except RepositoryNotFoundError:
+            # If the model ID is not found, search for it
+            print(f"Model ID not found directly. Searching for: {model_id}")
+            search_results = api.list_models(search=model_id, limit=1)
+            if search_results:
+                found_model_id = search_results[0].modelId
+                print(f"Found model ID: {found_model_id}")
+                snapshot_download(repo_id=found_model_id, local_dir=model_name, local_dir_use_symlinks=False)
+            else:
+                raise ValueError(f"Model not found: {model_id}")
 
-        dl_pattern = [
-            "*.safetensors", "*.bin", "*.pt", "*.onnx", "*.h5", "*.tflite",
-            "*.ckpt", "*.pb", "*.tar", "*.xml", "*.caffemodel",
-            "*.md", "*.json", "*.model"
-        ]
-
-        pattern = (
-            "*.safetensors"
-            if any(
-                file.path.endswith(".safetensors")
-                for file in api.list_repo_tree(
-                    repo_id=model_id,
-                    recursive=True,
-                )
-            )
-            else "*.bin"
-        )
-
-        dl_pattern += pattern
-
-        api.snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
-        print("Model downloaded successfully!")
-        print(f"Current working directory: {os.getcwd()}")
-        print(f"Model directory contents: {os.listdir(model_name)}")
+        # Find the model file
+        for filename in os.listdir(model_name):
+            if filename.endswith((".bin", ".pt", ".safetensors")):
+                model_file = os.path.join(model_name, filename)
+                break
+        else:
+            raise ValueError("No model file found in the downloaded files.")
 
+        # Convert to fp16
+        fp16 = f"{model_name}.fp16.gguf"
         conversion_script = "convert_hf_to_gguf.py"
-        fp16_conversion = f"python llama.cpp/{conversion_script} {model_name} --outtype f16 --outfile {fp16}"
+        fp16_conversion = f"python llama.cpp/{conversion_script} {model_file} --outtype f16 --outfile {fp16}"
         result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
-        print(result)
         if result.returncode != 0:
             raise Exception(f"Error converting to fp16: {result.stderr}")
-        print("Model converted to fp16 successfully!")
-        print(f"Converted model path: {fp16}")
 
+        # Quantization
         imatrix_path = "llama.cpp/imatrix.dat"
-
         if use_imatrix:
             if train_data_file:
                 train_data_path = train_data_file.name
             else:
-                train_data_path = "groups_merged.txt" # fallback calibration dataset
-
-            print(f"Training data file path: {train_data_path}")
-
+                train_data_path = "groups_merged.txt"
             if not os.path.isfile(train_data_path):
                 raise Exception(f"Training data file not found: {train_data_path}")
-
             generate_importance_matrix(fp16, train_data_path)
-        else:
-            print("Not using imatrix quantization.")
-        
+        quant_method = imatrix_q_method if use_imatrix else q_method
         username = whoami(oauth_token.token)["name"]
-        quantized_gguf_name = f"{model_name.lower()}-{imatrix_q_method.lower()}-imat.gguf" if use_imatrix else f"{model_name.lower()}-{q_method.lower()}.gguf"
+        quantized_gguf_name = f"{model_name.lower()}-{quant_method.lower()}-imat.gguf" if use_imatrix else f"{model_name.lower()}-{quant_method.lower()}.gguf"
         quantized_gguf_path = quantized_gguf_name
-        
-        if use_imatrix:
-            quantise_ggml = f"./llama.cpp/llama-quantize --imatrix {imatrix_path} {fp16} {quantized_gguf_path} {imatrix_q_method}"
-        else:
-            quantise_ggml = f"./llama.cpp/llama-quantize {fp16} {quantized_gguf_path} {q_method}"
-        
+        quantise_ggml = f"./llama.cpp/llama-quantize {'--imatrix' if use_imatrix else ''} {imatrix_path if use_imatrix else ''} {fp16} {quantized_gguf_path} {quant_method}"
         result = subprocess.run(quantise_ggml, shell=True, capture_output=True)
         if result.returncode != 0:
             raise Exception(f"Error quantizing: {result.stderr}")
-        print(f"Quantized successfully with {imatrix_q_method if use_imatrix else q_method} option!")
-        print(f"Quantized model path: {quantized_gguf_path}")
 
-        # Create empty repo
-        new_repo_url = api.create_repo(repo_id=f"{username}/{model_name}-{imatrix_q_method if use_imatrix else q_method}-GGUF", exist_ok=True, private=private_repo)
+        # Repo creation and upload
+        new_repo_url = api.create_repo(repo_id=f"{username}/{model_name}-{quant_method}-GGUF", exist_ok=True, private=private_repo)
         new_repo_id = new_repo_url.repo_id
-        print("Repo created successfully!", new_repo_url)
-
         try:
             card = ModelCard.load(model_id, token=oauth_token.token)
-        except:
+        except Exception:
             card = ModelCard("")
         if card.data.tags is None:
             card.data.tags = []
-        card.data.tags.append("llama-cpp")
-        card.data.tags.append("gguf-my-repo")
+        card.data.tags.extend(["llama-cpp", "gguf-my-repo"])
         card.data.base_model = model_id
         card.text = dedent(
             f"""
             # {new_repo_id}
             This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
             Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model.
-            
+
             ## Use with llama.cpp
             Install llama.cpp through brew (works on Mac and Linux)
-            
+
             ```bash
             brew install llama.cpp
-            
             ```
             Invoke the llama.cpp server or the CLI.
-            
+
             ### CLI:
             ```bash
             llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
             ```
-            
+
             ### Server:
             ```bash
             llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
             ```
-            
+
             Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
             Step 1: Clone llama.cpp from GitHub.
             ```
@@ -224,35 +177,22 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
         if split_model:
             split_upload_model(quantized_gguf_path, new_repo_id, oauth_token, split_max_tensors, split_max_size)
         else:
-            try:
-                print(f"Uploading quantized model: {quantized_gguf_path}")
-                api.upload_file(
-                    path_or_fileobj=quantized_gguf_path,
-                    path_in_repo=quantized_gguf_name,
-                    repo_id=new_repo_id,
-                )
-            except Exception as e:
-                raise Exception(f"Error uploading quantized model: {e}")
-        
-        imatrix_path = "llama.cpp/imatrix.dat"
+            api.upload_file(
+                path_or_fileobj=quantized_gguf_path,
+                path_in_repo=quantized_gguf_name,
+                repo_id=new_repo_id,
+            )
         if os.path.isfile(imatrix_path):
-            try:
-                print(f"Uploading imatrix.dat: {imatrix_path}")
-                api.upload_file(
-                    path_or_fileobj=imatrix_path,
-                    path_in_repo="imatrix.dat",
-                    repo_id=new_repo_id,
-                )
-            except Exception as e:
-                raise Exception(f"Error uploading imatrix.dat: {e}")
-
+            api.upload_file(
+                path_or_fileobj=imatrix_path,
+                path_in_repo="imatrix.dat",
+                repo_id=new_repo_id,
+            )
         api.upload_file(
             path_or_fileobj=f"README.md",
             path_in_repo=f"README.md",
             repo_id=new_repo_id,
         )
-        print(f"Uploaded successfully with {imatrix_q_method if use_imatrix else q_method} option!")
-
         return (
             f'Find your repo <a href=\'{new_repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
             "llama.png",
@@ -261,13 +201,12 @@ def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_rep
         return (f"Error: {e}", "error.png")
     finally:
         shutil.rmtree(model_name, ignore_errors=True)
-        print("Folder cleaned up successfully!")
 
-css="""/* Custom CSS to allow scrolling */
+css = """/* Custom CSS to allow scrolling */
 .gradio-container {overflow-y: auto;}
 """
-# Create Gradio interface
-with gr.Blocks(css=css) as demo: 
+
+with gr.Blocks(css=css) as demo:
     gr.Markdown("You must be logged in to use GGUF-my-repo.")
     gr.LoginButton(min_width=250)
 
@@ -290,7 +229,7 @@ with gr.Blocks(css=css) as demo:
         ["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", 
+        value="IQ4_NL",
         filterable=False,
         visible=False
     )
@@ -332,15 +271,25 @@ with gr.Blocks(css=css) as demo:
         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,
+        fn=lambda use_imatrix: {
+            q_method: gr.update(visible=not use_imatrix),
+            imatrix_q_method: gr.update(visible=use_imatrix),
+            train_data_file: gr.update(visible=use_imatrix),
+        },
         inputs=use_imatrix,
         outputs=[q_method, imatrix_q_method, train_data_file]
     )
 
+    split_model.change(
+        fn=lambda split_model: {
+            split_max_tensors: gr.update(visible=split_model),
+            split_max_size: gr.update(visible=split_model),
+        },
+        inputs=split_model,
+        outputs=[split_max_tensors, split_max_size]
+    )
+
     iface = gr.Interface(
         fn=process_model,
         inputs=[
@@ -363,15 +312,6 @@ with gr.Blocks(css=css) as demo:
         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)
 
@@ -379,5 +319,4 @@ 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)
+demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)