(chores) reorganised code

.gitignore

@@ -1 +1,131 @@
-.DS_Store
+.DS_Store
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

app.py

@@ -1,32 +1,19 @@
 import gradio as gr
 import base64
-
-
-# Function to encode the image
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode("utf-8")
-
-
+import prompts
 from openai import OpenAI
+from dotenv import load_dotenv
 
+load_dotenv()
 client = OpenAI()
 
-SINGLE_QCM_PROMPT = """
-You are an assistant for a primary or secondary school teacher. 
-The attached image is a photo or screenshot of a multiple choice question found by the teacher in an exercise book. 
-Your job is to convert this image into a JSON file containing the question and proposed answers. 
+PROMPT = prompts.SINGLE_QCM_PROMPT
 
-Let's proceed step by step:
-1. Start by identifying the question. Note that the question may be numbered with a digit (such as “1.”) or a letter (such as “a.”). This numbering is not part of the question.
-2. Identify all the proposed answers.
-3. Write the question and answers in a JSON file following the given example format.
 
-Example:
-- Input MCQ : "What year was America discovered? Answers: 1400, 1492, 1587, 1321"
-- Output JSON: {"Question": "What year was America discovered?", {"Answers": {"Answer":"1400"}, {"Answer":"1492"}, {"Answer":"1587"}, {"Answer":"1321"}}
-Answer only with the JSON file, don’t comment. Do not generate output that isn’t in properly formatted JSON. Respect the JSON format and especially the JSON keys “Question”,”Answers”,”Answer”.
-"""
+# Function to encode the image
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
 
 
 def process(image_path):
@@ -39,7 +26,7 @@ def process(image_path):
                 {
                     "role": "user",
                     "content": [
-                        {"type": "text", "text": SINGLE_QCM_PROMPT},
+                        {"type": "text", "text": PROMPT},
                         {
                             "type": "image_url",
                             "image_url": {
@@ -62,8 +49,6 @@ def process(image_path):
         return {"error": str(e)}
 
 
-import gradio as gr
-
 iface = gr.Interface(
     fn=process,
     inputs=gr.Image(type="filepath"),

prompts.py

@@ -0,0 +1,15 @@
+SINGLE_QCM_PROMPT = """
+You are an assistant for a primary or secondary school teacher. 
+The attached image is a photo or screenshot of a multiple choice question found by the teacher in an exercise book. 
+Your job is to convert this image into a JSON file containing the question and proposed answers. 
+
+Let's proceed step by step:
+1. Start by identifying the question. Note that the question may be numbered with a digit (such as “1.”) or a letter (such as “a.”). This numbering is not part of the question.
+2. Identify all the proposed answers.
+3. Write the question and answers in a JSON file following the given example format.
+
+Example:
+- Input MCQ : "What year was America discovered? Answers: 1400, 1492, 1587, 1321"
+- Output JSON: {"Question": "What year was America discovered?", {"Answers": {"Answer":"1400"}, {"Answer":"1492"}, {"Answer":"1587"}, {"Answer":"1321"}}
+Answer only with the JSON file, don’t comment. Do not generate output that isn’t in properly formatted JSON. Respect the JSON format and especially the JSON keys “Question”,”Answers”,”Answer”.
+"""