Create app.py

app.py

@@ -0,0 +1,144 @@
+import boto3
+import os
+import zipfile
+from glob import glob
+import shutil
+from huggingface_hub import HfApi
+import gradio as gr
+from tqdm.auto import tqdm
+import threading
+
+
+################################################################################
+
+# Declarations:
+print("Declaring variables.")
+# AWS S3 service name
+service_name = 's3'
+
+# AWS S3 bucket names
+biorxiv_bucket_name = 'biorxiv-src-monthly'
+medrxiv_bucket_name = 'medrxiv-src-monthly'
+
+# AWS region name
+region_name = 'us-east-1'
+
+# Hugging Face destination repository name
+destination_repo_name = 'xml-dump-monthly'
+
+################################################################################
+
+print("Initiating clients.")
+
+# Create a S3 client
+s3_client = boto3.client(
+    service_name='s3',
+    region_name=region_name,
+    aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'),
+    aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY')
+)
+paginator = s3_client.get_paginator('list_objects_v2')
+
+# Create a Hugging Face API client
+access_token =  os.getenv('HF_API_KEY')
+hugging_face_api = HfApi(token=access_token)
+
+# Create a dataset repo
+hugging_face_api.create_repo(
+    repo_id=destination_repo_name,
+    repo_type="dataset",
+    private=False,
+    exist_ok=True
+)
+
+# Extract Hugging facec username
+username = hugging_face_api.whoami()['name']
+repo_id = f"{username}/{destination_repo_name}"
+
+################################################################################
+
+
+
+################################################################################
+def download_medrxiv(Prefix=""):
+
+    print("Downloading Medrxiv files.")
+
+    # Output folders for downloaded files
+    medrxiv_output_folder = Prefix + 'medrxiv-xml-dump'
+
+    # Create output folders if they don't exist
+    os.makedirs(medrxiv_output_folder, exist_ok=True)
+
+    # Gather all objects from Medrxiv bucket
+    medrxiv_pages = paginator.paginate(
+        Bucket=medrxiv_bucket_name,
+        RequestPayer='requester',
+        Prefix=Prefix
+    ).build_full_result()
+
+    # Dowload all objects from Medrxiv bucket
+    for medrxiv_object in tqdm(medrxiv_pages['Contents'], desc=Prefix):
+
+        # Get the file name
+        file = medrxiv_object['Key']
+
+        # Check if the file is a zip file
+        if file.endswith(".meca"):
+
+            # Proccess the zip file
+            try:
+
+                # Download the file
+                s3_client.download_file(medrxiv_bucket_name, file, 'tmp_med.meca', ExtraArgs={'RequestPayer':'requester'})
+                    
+                # Unzip meca file
+                with zipfile.ZipFile('tmp_med.meca', 'r') as zip_ref:
+                    zip_ref.extractall("tmp_med")
+
+                # Gather the xml file
+                xml = glob('tmp_med/content/*.xml')
+
+                # Copy the xml file to the output folder
+                shutil.copy(xml[0], medrxiv_output_folder)
+
+                # Remove the tmp_med folder and file
+                shutil.rmtree('tmp_med')
+                os.remove('tmp_med.meca')
+
+            except Exception as e:
+                print(f"Error processing file {file}: {e}")
+
+
+    # Zip the output folder
+    shutil.make_archive(medrxiv_output_folder, 'zip', medrxiv_output_folder)
+
+    print(f"Uploading {medrxiv_output_folder}.zip to Hugging Face repo {repo_id}.")
+
+    hugging_face_api.upload_file(path_or_fileobj=f'{medrxiv_output_folder}.zip', path_in_repo=f'{medrxiv_output_folder}.zip', repo_id=repo_id, repo_type="dataset")
+
+    print("Medrxiv Done.")
+
+# Create separate threads function
+second_thread = threading.Thread(target=download_medrxiv, args=("Current_Content/September_2024/",))
+
+# Start thread
+second_thread.start()
+
+
+
+###############################################################################
+
+# Dummy app
+
+def greet(name, intensity):
+    return "Hello, " + name + "!" * int(intensity)
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=["text", "slider"],
+    outputs=["text"],
+)
+
+demo.launch()
+