bug fix.

edit fundamental logic of passing openai key.
support user's key input.

app.py

@@ -1,6 +1,8 @@
 import gradio as gr
 import os
-from auto_backgrounds import generate_backgrounds, fake_generate_backgrounds
+import openai
+from auto_backgrounds import generate_backgrounds, fake_generator
+from auto_draft import generate_draft
 
 openai_key = os.getenv("OPENAI_API_KEY")
 access_key_id = os.getenv('AWS_ACCESS_KEY_ID')
@@ -16,7 +18,12 @@ if openai_key is None:
     IS_OPENAI_API_KEY_AVAILABLE = False
 else:
     # todo: check if this key is available or not
-    IS_OPENAI_API_KEY_AVAILABLE = True
+    openai.api_key = openai_key
+    try:
+        openai.Model.list()
+        IS_OPENAI_API_KEY_AVAILABLE = True
+    except Exception as e:
+        IS_OPENAI_API_KEY_AVAILABLE = False
 
 
 
@@ -24,11 +31,19 @@ def clear_inputs(text1, text2):
     return "", ""
 
 
-def wrapped_generate_backgrounds(title, description, openai_key = None, cache_mode = True):
+def wrapped_generator(title, description, openai_key = None,
+                      template = "ICLR2022",
+                      cache_mode = IS_CACHE_AVAILABLE, generator=None):
     # if `cache_mode` is True, then follow the following steps:
     #        check if "title"+"description" have been generated before
     #        if so, download from the cloud storage, return it
     #        if not, generate the result.
+    if generator is None:
+        generator = generate_backgrounds
+    if openai_key is not None:
+        openai.api_key = openai_key
+        openai.Model.list()
+
     if cache_mode:
         from utils.storage import list_all_files, hash_name, download_file, upload_file
         # check if "title"+"description" have been generated before
@@ -41,21 +56,26 @@ def wrapped_generate_backgrounds(title, description, openai_key = None, cache_mo
         else:
             # generate the result.
             # output = fake_generate_backgrounds(title, description, openai_key)
-            output = generate_backgrounds(title, description, openai_key) #todo: change the output of this function to hashed title
+            output = generate_backgrounds(title, description,  template, "gpt-4")
             upload_file(file_name)
             return output
     else:
         # output = fake_generate_backgrounds(title, description, openai_key)
-        output = generate_backgrounds(title, description, openai_key) #todo: change the output of this function to hashed title
+        output = generate_backgrounds(title, description,  template, "gpt-4")
         return output
 
 
+theme = gr.themes.Monochrome(font=gr.themes.GoogleFont("Questrial")).set(
+    background_fill_primary='#F6F6F6',
+    button_primary_background_fill="#281A39",
+    input_background_fill='#E5E4E2'
+)
 
-with gr.Blocks() as demo:
+with gr.Blocks(theme=theme) as demo:
     gr.Markdown('''
     # Auto-Draft: 文献整理辅助工具-限量免费使用
     
-    本Demo提供对[Auto-Draft](https://github.com/CCCBora/auto-draft)的auto_backgrounds功能的测试。通过输入一个领域的名称（比如Deep Reinforcement Learning)，即可自动对这个领域的相关文献进行归纳总结.  
+    本Demo提供对[Auto-Draft](https://github.com/CCCBora/auto-draft)的auto_backgrounds功能的测试。通过输入一个领域的名称（比如Deep Reinforcement Learning)，即可自动对这个领域的相关文献进行归纳总结.    
     
     ***2023-04-30 Update***: 如果有更多想法和建议欢迎加入群里交流, 群号: ***249738228***.  
     
@@ -66,26 +86,26 @@ with gr.Blocks() as demo:
     输入一个领域的名称（比如Deep Reinforcement Learning), 点击Submit, 等待大概十分钟, 下载output.zip，在Overleaf上编译浏览.  
     ''')
     with gr.Row():
-        with gr.Column():
-            # key =  gr.Textbox(value=openai_key, lines=1, max_lines=1, label="OpenAI Key", visible=not IS_OPENAI_API_KEY_AVAILABLE)
-            key =  gr.Textbox(value=openai_key, lines=1, max_lines=1, label="OpenAI Key", visible=False)
+        with gr.Column(scale=2):
+            key =  gr.Textbox(value=openai_key, lines=1, max_lines=1, label="OpenAI Key", visible=not IS_OPENAI_API_KEY_AVAILABLE)
+            # key =  gr.Textbox(value=openai_key, lines=1, max_lines=1, label="OpenAI Key", visible=False)
             title = gr.Textbox(value="Deep Reinforcement Learning", lines=1, max_lines=1, label="Title")
             description = gr.Textbox(lines=5, label="Description (Optional)")
 
             with gr.Row():
                 clear_button = gr.Button("Clear")
                 submit_button = gr.Button("Submit")
-        with gr.Column():
+        with gr.Column(scale=1):
             style_mapping = {True: "color:white;background-color:green", False: "color:white;background-color:red"} #todo: to match website's style
-            availablity_mapping = {True: "AVAILABLE", False: "NOT AVAILABLE"}
+            availability_mapping = {True: "AVAILABLE", False: "NOT AVAILABLE"}
             gr.Markdown(f'''## Huggingface Space Status  
              当`OpenAI API`显示AVAILABLE的时候这个Space可以直接使用.    
-             当`OpenAI API`显示UNAVAILABLE的时候这个Space可以通过在左侧输入OPENAI KEY来使用 (暂时不支持). 
-            `OpenAI API`: <span style="{style_mapping[IS_OPENAI_API_KEY_AVAILABLE]}">{availablity_mapping[IS_OPENAI_API_KEY_AVAILABLE]}</span>.  `Cache`: <span style="{style_mapping[IS_CACHE_AVAILABLE]}">{availablity_mapping[IS_CACHE_AVAILABLE]}</span>.''')
+             当`OpenAI API`显示NOT AVAILABLE的时候这个Space可以通过在左侧输入OPENAI KEY来使用. 
+            `OpenAI API`: <span style="{style_mapping[IS_OPENAI_API_KEY_AVAILABLE]}">{availability_mapping[IS_OPENAI_API_KEY_AVAILABLE]}</span>.  `Cache`: <span style="{style_mapping[IS_CACHE_AVAILABLE]}">{availability_mapping[IS_CACHE_AVAILABLE]}</span>.''')
             file_output = gr.File(label="Output")
 
     clear_button.click(fn=clear_inputs, inputs=[title, description], outputs=[title, description])
-    submit_button.click(fn=wrapped_generate_backgrounds, inputs=[title, description, key], outputs=file_output)
+    submit_button.click(fn=wrapped_generator, inputs=[title, description, key], outputs=file_output)
 
 demo.queue(concurrency_count=1, max_size=5, api_open=False)
 demo.launch()

auto_backgrounds.py

@@ -1,29 +1,12 @@
 from utils.references import References
-from utils.prompts import generate_bg_keywords_prompts, generate_bg_summary_prompts
-from utils.gpt_interaction import get_responses, extract_responses, extract_keywords, extract_json
-from utils.tex_processing import replace_title
-import datetime
-import shutil
-import time
+from utils.file_operations import hash_name, make_archive, copy_templates
+from section_generator import section_generation_bg, keywords_generation
 import logging
-import os
 
 TOTAL_TOKENS = 0
 TOTAL_PROMPTS_TOKENS = 0
 TOTAL_COMPLETION_TOKENS = 0
 
-
-def hash_name(title, description):
-    '''
-    For same title and description, it should return the same value.
-    '''
-    name = title + description
-    name = name.lower()
-    md5 = hashlib.md5()
-    md5.update(name.encode('utf-8'))
-    hashed_string = md5.hexdigest()
-    return hashed_string
-
 def log_usage(usage, generating_target, print_out=True):
     global TOTAL_TOKENS
     global TOTAL_PROMPTS_TOKENS
@@ -43,70 +26,19 @@ def log_usage(usage, generating_target, print_out=True):
         print(message)
     logging.info(message)
 
-def make_archive(source, destination):
-    base = os.path.basename(destination)
-    name = base.split('.')[0]
-    format = base.split('.')[1]
-    archive_from = os.path.dirname(source)
-    archive_to = os.path.basename(source.strip(os.sep))
-    shutil.make_archive(name, format, archive_from, archive_to)
-    shutil.move('%s.%s'%(name,format), destination)
-    return destination
-
-def pipeline(paper, section, save_to_path, model, openai_key=None):
-    """
-    The main pipeline of generating a section.
-        1. Generate prompts.
-        2. Get responses from AI assistant.
-        3. Extract the section text.
-        4. Save the text to .tex file.
-    :return usage
-    """
-    print(f"Generating {section}...")
-    prompts = generate_bg_summary_prompts(paper, section)
-    gpt_response, usage = get_responses(prompts, model)
-    output = extract_responses(gpt_response)
-    paper["body"][section] = output
-    tex_file = save_to_path + f"{section}.tex"
-    if section == "abstract":
-        with open(tex_file, "w") as f:
-            f.write(r"\begin{abstract}")
-        with open(tex_file, "a") as f:
-            f.write(output)
-        with open(tex_file, "a") as f:
-            f.write(r"\end{abstract}")
-    else:
-        with open(tex_file, "w") as f:
-            f.write(f"\section{{{section.upper()}}}\n")
-        with open(tex_file, "a") as f:
-            f.write(output)
-    time.sleep(5)
-    print(f"{section} has been generated. Saved to {tex_file}.")
-    return usage
-
-
-
-def generate_backgrounds(title, description="", template="ICLR2022", model="gpt-4", openai_key=None):
+def generate_backgrounds(title, description="", template="ICLR2022", model="gpt-4"):
     paper = {}
     paper_body = {}
 
     # Create a copy in the outputs folder.
-    now = datetime.datetime.now()
-    target_name = now.strftime("outputs_%Y%m%d_%H%M%S")
-    source_folder = f"latex_templates/{template}"
-    destination_folder = f"outputs/{target_name}"
-    shutil.copytree(source_folder, destination_folder)
-
-    bibtex_path = destination_folder + "/ref.bib"
-    save_to_path = destination_folder +"/"
-    replace_title(save_to_path, "A Survey on " + title)
-    logging.basicConfig( level=logging.INFO, filename=save_to_path+"generation.log")
+    bibtex_path, destination_folder = copy_templates(template, title)
+    logging.basicConfig(level=logging.INFO, filename=destination_folder + "/generation.log")
 
     # Generate keywords and references
     print("Initialize the paper information ...")
-    prompts = generate_bg_keywords_prompts(title, description)
-    gpt_response, usage = get_responses(prompts, model)
-    keywords = extract_keywords(gpt_response)
+    input_dict = {"title": title, "description": description}
+    keywords, usage = keywords_generation(input_dict, model="gpt-3.5-turbo")
+    print(f"keywords: {keywords}")
     log_usage(usage, "keywords")
 
     ref = References(load_papers = "")
@@ -123,28 +55,23 @@ def generate_backgrounds(title, description="", template="ICLR2022", model="gpt-
 
     for section in ["introduction", "related works", "backgrounds"]:
         try:
-            usage = pipeline(paper, section, save_to_path, model=model)
+            # usage = pipeline(paper, section, destination_folder, model=model)
+            usage = section_generation_bg(paper, section, destination_folder, model=model)
             log_usage(usage, section)
         except Exception as e:
             print(f"Failed to generate {section} due to the error: {e}")
-    print(f"The paper {title} has been generated. Saved to {save_to_path}.")
+    print(f"The paper {title} has been generated. Saved to {destination_folder}.")
     # shutil.make_archive("output.zip", 'zip', save_to_path)
-    return make_archive(destination_folder, "output.zip")
+
+    input_dict = {"title": title, "description": description, "generator": "generate_backgrounds"}
+    filename = hash_name(input_dict) + ".zip"
+    return make_archive(destination_folder, filename)
 
 
-def fake_generate_backgrounds(title, description, openai_key = None):
+def fake_generator(title, description="", template="ICLR2022", model="gpt-4"):
     """
     This function is used to test the whole pipeline without calling OpenAI API.
     """
-    filename = hash_name(title, description) + ".zip"
+    input_dict = {"title": title, "description": description, "generator": "generate_backgrounds"}
+    filename = hash_name(input_dict) + ".zip"
     return make_archive("sample-output.pdf", filename)
-
-
-if __name__ == "__main__":
-    title = "Reinforcement Learning"
-    description = ""
-    template = "Summary"
-    model = "gpt-4"
-    # model = "gpt-3.5-turbo"
-
-    generate_backgrounds(title, description, template, model)

auto_draft.py

@@ -88,6 +88,7 @@ def generate_draft(title, description="", template="ICLR2022", model="gpt-4"):
     paper_body = {}
 
     # Create a copy in the outputs folder.
+    # todo: use copy_templates function instead.
     now = datetime.datetime.now()
     target_name = now.strftime("outputs_%Y%m%d_%H%M%S")
     source_folder = f"latex_templates/{template}"
@@ -105,16 +106,15 @@ def generate_draft(title, description="", template="ICLR2022", model="gpt-4"):
     gpt_response, usage = get_responses(prompts, model)
     keywords = extract_keywords(gpt_response)
     log_usage(usage, "keywords")
-
-    ref = References(load_papers = "")
-    ref.collect_papers(keywords, method="arxiv")
+    ref = References(load_papers = "") #todo: allow users to upload bibfile.
+    ref.collect_papers(keywords, method="arxiv") #todo: add more methods to find related papers
     all_paper_ids = ref.to_bibtex(bibtex_path) #todo: this will used to check if all citations are in this list
 
     print(f"The paper information has been initialized. References are saved to {bibtex_path}.")
 
     paper["title"] = title
     paper["description"] = description
-    paper["references"] = ref.to_prompts() # to_prompts(top_papers)
+    paper["references"] = ref.to_prompts() #todo: see if this prompts can be compressed.
     paper["body"] = paper_body
     paper["bibtex"] = bibtex_path
 

section_generator.py

@@ -0,0 +1,58 @@
+from utils.prompts import generate_paper_prompts, generate_keywords_prompts, generate_experiments_prompts, generate_bg_summary_prompts
+from utils.gpt_interaction import get_responses, extract_responses, extract_keywords, extract_json
+import time
+import os
+
+#  three GPT-based content generator:
+#       1. section_generation: used to generate main content of the paper
+#       2. keywords_generation: used to generate a json output {key1: output1, key2: output2} for multiple purpose.
+#       3. figure_generation: used to generate sample figures.
+#  all generator should return the token usage.
+
+
+def section_generation_bg(paper, section, save_to_path, model):
+    """
+    The main pipeline of generating a section.
+        1. Generate prompts.
+        2. Get responses from AI assistant.
+        3. Extract the section text.
+        4. Save the text to .tex file.
+    :return usage
+    """
+    print(f"Generating {section}...")
+    prompts = generate_bg_summary_prompts(paper, section)
+    gpt_response, usage = get_responses(prompts, model)
+    output = extract_responses(gpt_response)
+    paper["body"][section] = output
+    tex_file = os.path.join(save_to_path, f"{section}.tex")
+    # tex_file = save_to_path + f"/{section}.tex"
+    if section == "abstract":
+        with open(tex_file, "w") as f:
+            f.write(r"\begin{abstract}")
+        with open(tex_file, "a") as f:
+            f.write(output)
+        with open(tex_file, "a") as f:
+            f.write(r"\end{abstract}")
+    else:
+        with open(tex_file, "w") as f:
+            f.write(f"\section{{{section.upper()}}}\n")
+        with open(tex_file, "a") as f:
+            f.write(output)
+    time.sleep(5)
+    print(f"{section} has been generated. Saved to {tex_file}.")
+    return usage
+
+
+def keywords_generation(input_dict,  model):
+    title = input_dict.get("title")
+    description = input_dict.get("description", "")
+    if title is not None:
+        prompts = generate_keywords_prompts(title, description)
+        gpt_response, usage = get_responses(prompts, model)
+        keywords = extract_keywords(gpt_response)
+        return keywords, usage
+    else:
+        raise ValueError("`input_dict` must include the key 'title'.")
+
+def figures_generation():
+    pass

utils/file_operations.py

@@ -0,0 +1,45 @@
+import hashlib
+import os, shutil
+import datetime
+from utils.tex_processing import replace_title
+
+def hash_name(input_dict):
+    '''
+    input_dict= {"title": title, "description": description}
+
+    For same input_dict, it should return the same value.
+    '''
+    name = str(input_dict)
+    name = name.lower()
+    md5 = hashlib.md5()
+    md5.update(name.encode('utf-8'))
+    hashed_string = md5.hexdigest()
+    return hashed_string
+
+
+
+def make_archive(source, destination):
+    base = os.path.basename(destination)
+    name = base.split('.')[0]
+    format = base.split('.')[1]
+    archive_from = os.path.dirname(source)
+    archive_to = os.path.basename(source.strip(os.sep))
+    shutil.make_archive(name, format, archive_from, archive_to)
+    shutil.move('%s.%s'%(name,format), destination)
+    return destination
+
+def copy_templates(template, title):
+    # Create a copy in the outputs folder.
+    #   1. create a folder "outputs_%Y%m%d_%H%M%S" (destination_folder)
+    #   2. copy all contents in "latex_templates/{template}" to that folder
+    #   3. return (bibtex_path, destination_folder)
+    now = datetime.datetime.now()
+    target_name = now.strftime("outputs_%Y%m%d_%H%M%S")
+    source_folder = f"latex_templates/{template}"
+    destination_folder = f"outputs/{target_name}"
+    shutil.copytree(source_folder, destination_folder)
+    bibtex_path = os.path.join(destination_folder, "ref.bib")
+    # bibtex_path = destination_folder + "/ref.bib"
+    replace_title(destination_folder, title)
+    return bibtex_path, destination_folder
+

utils/gpt_interaction.py

@@ -1,12 +1,10 @@
 import openai
 import re
-import os
 import json
 import logging
+
 log = logging.getLogger(__name__)
 
-# todo: 将api_key通过函数传入; 需要改很多地方
-# openai.api_key = os.environ['OPENAI_API_KEY']
 
 def extract_responses(assistant_message):
     # pattern = re.compile(r"f\.write\(r'{1,3}(.*?)'{0,3}\){0,1}$", re.DOTALL)
@@ -19,9 +17,10 @@ def extract_responses(assistant_message):
         log.info(f"assistant_message: {assistant_message}")
         return assistant_message
 
+
 def extract_keywords(assistant_message, default_keywords=None):
     if default_keywords is None:
-        default_keywords = {"machine learning":5}
+        default_keywords = {"machine learning": 5}
 
     try:
         keywords = json.loads(assistant_message)
@@ -31,6 +30,7 @@ def extract_keywords(assistant_message, default_keywords=None):
         return default_keywords
     return keywords
 
+
 def extract_section_name(assistant_message, default_section_name=""):
     try:
         keywords = json.loads(assistant_message)
@@ -55,7 +55,7 @@ def extract_json(assistant_message, default_output=None):
     return dict.keys()
 
 
-def get_responses(user_message, model="gpt-4", temperature=0.4, openai_key = None):
+def get_responses(user_message, model="gpt-4", temperature=0.4, openai_key=None):
     if openai.api_key is None and openai_key is None:
         raise ValueError("OpenAI API key must be provided.")
     if openai_key is not None:
@@ -79,8 +79,8 @@ def get_responses(user_message, model="gpt-4", temperature=0.4, openai_key = Non
 
 if __name__ == "__main__":
     test_strings = [r"f.write(r'hello world')", r"f.write(r'''hello world''')", r"f.write(r'''hello world",
-                     r"f.write(r'''hello world'", r'f.write(r"hello world")', r'f.write(r"""hello world""")',
-                     r'f.write(r"""hello world"', r'f.write(r"""hello world']
+                    r"f.write(r'''hello world'", r'f.write(r"hello world")', r'f.write(r"""hello world""")',
+                    r'f.write(r"""hello world"', r'f.write(r"""hello world']
     for input_string in test_strings:
         print("input_string: ", input_string)
         pattern = re.compile(r"f\.write\(r['\"]{1,3}(.*?)['\"]{0,3}\){0,1}$", re.DOTALL)
@@ -90,4 +90,4 @@ if __name__ == "__main__":
             extracted_string = match.group(1)
             print("Extracted string:", extracted_string)
         else:
-            print("No match found")
+            print("No match found")

utils/prompts.py

@@ -16,8 +16,8 @@ BG_INSTRUCTIONS = {"introduction": "Please include four paragraph: Establishing
 
 def generate_keywords_prompts(title, description="", num_refs=5):
     prompts = f"I am writing a machine learning paper with the title '{title}'. {description}\n" \
-                f"Please generate three to five keywords. For each keyword, rate it from 1 to {num_refs}; the larger number means more important." \
-                r"Response in a dictionary format like {\"keyword1\":1, \"keyword2\":3}."
+                f"Generate three to five keywords. For each keyword, rate it from 1 to {num_refs}; the larger number means more important." \
+                r"Your response must be in JSON format like {\"keyword1\":1, \"keyword2\":3}."
     return prompts
 
 def generate_rename_prompts(paper_info, section):
@@ -72,15 +72,6 @@ def generate_paper_prompts(paper_info, section):
     return prompts
 
 
-
-
-def generate_bg_keywords_prompts(title, description="", num_refs=5):
-    prompts = f"I am writing a survey on the topic '{title}'. {description}\n" \
-                f"Please generate three to five keywords. For each keyword, rate it from 1 to {num_refs}; the larger number means more important." \
-                r"Response in a dictionary format like {keyword1:1, keyword2:3}."
-    return prompts
-
-
 def generate_bg_summary_prompts(paper_info, section):
     title = paper_info["title"]
     description = paper_info["description"]

utils/storage.py

@@ -1,50 +1,45 @@
+# This script `storage.py` is used to handle the cloud storage.
+#   `upload_file`:
+#   `list_all_files`:
+#   `download_file`:
+
 import os
 import boto3
-import hashlib
 
-access_key_id = os.environ['AWS_ACCESS_KEY_ID']
-secret_access_key = os.environ['AWS_SECRET_ACCESS_KEY']
+access_key_id = os.getenv('AWS_ACCESS_KEY_ID')
+secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY')
 bucket_name = "hf-storage"
 
-session = boto3.Session(
-    aws_access_key_id=access_key_id,
-    aws_secret_access_key=secret_access_key,
-)
-
-s3 = session.resource('s3')
-bucket = s3.Bucket(bucket_name)
-
-def upload_file(file_name, target_name=None):
-    if target_name is None:
-        target_name = file_name
-    try:
-        s3.meta.client.upload_file(Filename=file_name, Bucket=bucket_name, Key=target_name)
-        print(f"The file {file_name} has been uploaded!")
-    except:
-        print("Uploading failed!")
-
-def list_all_files():
-    return [obj.key for obj in bucket.objects.all()]
-
-def download_file(file_name):
-    ''' Download `file_name` from the bucket. todo:check existence before downloading!
-    Bucket (str) – The name of the bucket to download from.
-    Key (str) – The name of the key to download from.
-    Filename (str) – The path to the file to download to.
-    '''
-    try:
-        s3.meta.client.download_file(Bucket=bucket_name, Key=file_name, Filename=file_name)
-        print(f"The file {file_name} has been downloaded!")
-    except:
-        print("Uploading failed!")
-
-def hash_name(title, description):
-    '''
-    For same title and description, it should return the same value.
-    '''
-    name = title + description
-    name = name.lower()
-    md5 = hashlib.md5()
-    md5.update(name.encode('utf-8'))
-    hashed_string = md5.hexdigest()
-    return hashed_string
+if (access_key_id is not None) and (secret_access_key is not None):
+    session = boto3.Session(
+        aws_access_key_id=access_key_id,
+        aws_secret_access_key=secret_access_key,
+    )
+
+    s3 = session.resource('s3')
+    bucket = s3.Bucket(bucket_name)
+
+
+    def upload_file(file_name, target_name=None):
+        if target_name is None:
+            target_name = file_name
+        try:
+            s3.meta.client.upload_file(Filename=file_name, Bucket=bucket_name, Key=target_name)
+            print(f"The file {file_name} has been uploaded!")
+        except:
+            print("Uploading failed!")
+
+    def list_all_files():
+        return [obj.key for obj in bucket.objects.all()]
+
+    def download_file(file_name):
+        ''' Download `file_name` from the bucket. todo:check existence before downloading!
+        Bucket (str) – The name of the bucket to download from.
+        Key (str) – The name of the key to download from.
+        Filename (str) – The path to the file to download to.
+        '''
+        try:
+            s3.meta.client.download_file(Bucket=bucket_name, Key=file_name, Filename=file_name)
+            print(f"The file {file_name} has been downloaded!")
+        except:
+            print("Uploading failed!")

utils/tex_processing.py

@@ -1,7 +1,11 @@
+import os
+
 def replace_title(save_to_path, title):
     # Define input and output file names
-    input_file_name = save_to_path + "template.tex"
-    output_file_name = save_to_path + "main.tex"
+    # input_file_name =  save_to_path + "/template.tex"
+    # output_file_name = save_to_path + "/main.tex"
+    input_file_name = os.path.join(save_to_path, "template.tex")
+    output_file_name = os.path.join(save_to_path , "main.tex")
 
     # Open the input file and read its content
     with open(input_file_name, 'r') as infile:
@@ -15,6 +19,9 @@ def replace_title(save_to_path, title):
         outfile.write(content)
 
 
-# return all string in \cite{...}
+# return all string in \cite{...}.
+
+# check if citations are in bibtex.
+
 
 # replace citations