Create dowloading_models.py

Ollama-Companion/modules/dowloading_models.py

@@ -0,0 +1,141 @@
+import os
+import subprocess
+import streamlit as st
+import requests
+from apscheduler.schedulers.background import BackgroundScheduler
+from pathlib import Path
+from threading import Lock
+
+# Initialize APScheduler
+scheduler = BackgroundScheduler()
+scheduler.start()
+
+# Global variables
+scheduled_jobs = []
+downloaded_files = []
+download_queue = []
+queue_lock = Lock()
+
+def download_file_task():
+    global download_queue, downloaded_files
+    if download_queue:
+        file_url, download_path, filename = download_queue.pop(0)
+        file_path = download_path / filename
+        command = [
+            "aria2c", file_url,
+            "--max-connection-per-server=16", "--split=8", "--min-split-size=1M", "--allow-overwrite=true",
+            "-d", str(download_path), "-o", filename,
+            "--continue=true"
+        ]
+        try:
+            subprocess.run(command, check=True)
+            downloaded_files.append(str(file_path))
+        except subprocess.CalledProcessError as e:
+            print(f"Error downloading {filename}: {str(e)}")
+
+        # Schedule next download
+        if download_queue:
+            scheduler.add_job(download_file_task)
+
+def queue_download(file_links_dict, model_name):
+    global download_queue
+    folder_name = model_name.split("/")[-1]
+    current_dir = Path(__file__).parent
+    download_path = current_dir.parent / f"/content/Ollama-Companion/llama.cpp/models/{folder_name}"
+    download_path.mkdir(parents=True, exist_ok=True)
+
+    with queue_lock:
+        for file_name, file_url in file_links_dict.items():
+            filename = Path(file_name).name
+            download_queue.append((file_url, download_path, filename))
+
+    # Start the first download
+    if download_queue:
+        scheduler.add_job(download_file_task)
+
+    return "Download tasks have been queued."
+def cancel_downloads():
+    global scheduled_jobs, downloaded_files
+    for job in scheduled_jobs:
+        job.remove()
+    scheduled_jobs.clear()
+
+    for file_path in downloaded_files:
+        if os.path.exists(file_path):
+            os.remove(file_path)
+    downloaded_files.clear()
+
+    return "All queued downloads have been cancelled and files removed."
+
+def construct_hf_repo_url(model_name):
+    base_url = "https://huggingface.co/api/models/"
+    return f"{base_url}{model_name}/tree/main"
+
+def get_files_from_repo(url, repo_name):
+    try:
+        response = requests.get(url)
+        if response.status_code == 200:
+            files_info = response.json()
+            file_info_dict = {}
+            file_links_dict = {}
+
+            base_url = f"https://huggingface.co/{repo_name}/resolve/main/"
+            for file in files_info:
+                name = file.get('path', 'Unknown')
+                size = file.get('size', 0)
+                human_readable_size = f"{size / 1024 / 1024:.2f} MB"
+                file_info_dict[name] = human_readable_size
+                file_links_dict[name] = base_url + name
+
+            return file_info_dict, file_links_dict
+        else:
+            return {}, {}
+    except Exception as e:
+        return {}, {}
+
+def show_downloading_models_page():
+    st.title("Model Downloader")
+
+    model_name = st.text_input("Download PyTorch models from Hugginface", "Use the HuggingfaceUsername/Modelname")
+    instruction = st.text("Instructions  . For example")
+    if st.button("Get File List"):
+        _, file_links = get_files_from_repo(construct_hf_repo_url(model_name), model_name)
+        if file_links:
+            st.session_state['file_links_dict'] = file_links
+            files_info = "\n".join(f"{name}, Size: {size}" for name, size in file_links.items())
+            st.text_area("Files Information", files_info, height=300)
+        else:
+            st.error("Unable to retrieve file links.")
+            if 'file_links_dict' in st.session_state:
+                del st.session_state['file_links_dict']
+
+    if st.button("Download Files"):
+        if 'file_links_dict' in st.session_state and st.session_state['file_links_dict']:
+            queue_message = queue_download(st.session_state['file_links_dict'], model_name)
+            st.text(queue_message)
+        else:
+            st.error("No files to download. Please get the file list first.")
+
+    if st.button("Stop Downloads"):
+        cancel_message = cancel_downloads()
+        st.text(cancel_message)
+    with st.expander("How to Download Model Files from Hugging Face", expanded=False):
+        st.markdown("""
+        **How to Download Model Files from Hugging Face**
+
+        - First, visit the Hugging Face model page that you want to download. For example, if you want to download the model at this link: [https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
+
+        - On the model page, locate the icon next to the username of the model's author. This icon typically looks like a clipboard or a copy symbol. Click on this icon to copy the Username/RepositoryName, which in this example is `mistralai/Mistral-7B-Instruct-v0.2`.
+
+        - Paste the copied Username/RepositoryName `mistralai/Mistral-7B-Instruct-v0.2` directly into the input field.
+
+        - Click the "Get file list" button or option to retrieve the list of files available in this repository.
+
+        - Review the list of files to ensure you have the correct model files that you want to download.
+
+        - Finally, click the "Download Model" button or option to initiate the download process for the selected model files.
+
+        - The model files will be saved in the `llama.cpp/models` directory on your device.
+
+        - Now you have successfully downloaded the model files from Hugging Face, and they are stored in the `llama.cpp/models` directory for your use.
+        """)