Spaces:
Sleeping
Sleeping
improve prompts, templates, debugs, etc.
Browse files- app.py +45 -38
- config.py +2 -2
- data_test.py +4 -4
- taskAI.py +7 -7
- taskNonAI.py +4 -2
- template_letter.tmpl +4 -4
app.py
CHANGED
@@ -49,25 +49,26 @@ def run_refine(api_base, api_key, api_model, jd_info, cv_text):
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cheapAPI = {"base": api_base, "key": api_key, "model": api_model}
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taskAI = TaskAI(cheapAPI, temperature=0.2, max_tokens=2048) # max_tokens=2048
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info("API initialized")
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-
gen = (
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taskAI.jd_preprocess(input=jd),
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taskAI.cv_preprocess(input=cv),
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)
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-
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yield result
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-
if stop:
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info("tasks done")
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break
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def run_compose(api_base, api_key, api_model, min_jd, min_cv):
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strongAPI = {"base": api_base, "key": api_key, "model": api_model}
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@@ -78,28 +79,34 @@ def run_compose(api_base, api_key, api_model, min_jd, min_cv):
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result += response.delta
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yield result
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-
def finalize_letter_txt(api_base, api_key, api_model, debug_CoT
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cheapAPI = {"base": api_base, "key": api_key, "model": api_model}
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taskAI = TaskAI(cheapAPI, temperature=0.2, max_tokens=2048)
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info("Finalizing letter ...")
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-
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-
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-
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)
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-
for result in gen:
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yield result
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-
def finalize_letter_pdf(
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pdf_context = json.loads(meta_data)
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pdf_context["letter_body"] = cover_letter_text
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-
return compile_pdf(pdf_context,tmpl_path="template_letter.tmpl",output_path=f"/tmp/
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with gr.Blocks(
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title=DEMO_TITLE,
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theme=gr.themes.Base(primary_hue="blue", secondary_hue="sky", neutral_hue="slate"),
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) as app:
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intro = f"""# {DEMO_TITLE}
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-
> You provide job description and résumé. I write Cover letter for you!
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Before you use, please fisrt setup API for 2 AI agents': Cheap AI and Strong AI.
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"""
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gr.Markdown(intro)
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@@ -114,7 +121,7 @@ with gr.Blocks(
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cheap_base = gr.Textbox(
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value=CHEAP_API_BASE, label="API BASE"
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)
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-
cheap_key = gr.Textbox(value=CHEAP_API_KEY, label="API key")
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cheap_model = gr.Textbox(value=CHEAP_MODEL, label="Model ID")
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gr.Markdown(
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"---\n**Strong AI**, a thoughtful wordsmith, generates perfect cover letters to make both you and recruiters happy."
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@@ -137,17 +144,17 @@ with gr.Blocks(
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)
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with gr.Group():
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gr.Markdown("## Applicant - CV / Résumé")
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-
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-
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-
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with gr.Column(scale=2):
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gr.Markdown("## Result")
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with gr.Accordion("Reformatting", open=True) as reformat_zone:
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@@ -179,8 +186,8 @@ with gr.Blocks(
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).then(fn=lambda:[gr.Accordion("Expert Zone", open=True),gr.Accordion("Reformatting", open=False)],inputs=None, outputs=[expert_zone, reformat_zone]
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).then(fn=run_compose, inputs=[strong_base, strong_key, strong_model, min_jd, min_cv], outputs=[debug_CoT]
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).then(fn=lambda:gr.Accordion("Expert Zone", open=False),inputs=None, outputs=[expert_zone]
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-
).then(fn=finalize_letter_txt, inputs=[cheap_base, cheap_key, cheap_model, debug_CoT
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-
).then(fn=finalize_letter_pdf, inputs=[
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if __name__ == "__main__":
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cheapAPI = {"base": api_base, "key": api_key, "model": api_model}
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taskAI = TaskAI(cheapAPI, temperature=0.2, max_tokens=2048) # max_tokens=2048
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info("API initialized")
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+
gen = stream_together(
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taskAI.jd_preprocess(input=jd),
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taskAI.cv_preprocess(input=cv),
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)
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+
# result = [""] * 2
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# while 1:
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# stop: bool = True
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# for i in range(len(gen)):
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# try:
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# result[i] += next(gen[i]).delta
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# stop = False
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# except StopIteration:
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# # info(f"gen[{i}] exhausted")
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# pass
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# yield result
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# if stop:
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# info("tasks done")
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# break
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for result in gen:
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yield result
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def run_compose(api_base, api_key, api_model, min_jd, min_cv):
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strongAPI = {"base": api_base, "key": api_key, "model": api_model}
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result += response.delta
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yield result
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+
def finalize_letter_txt(api_base, api_key, api_model, debug_CoT):
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cheapAPI = {"base": api_base, "key": api_key, "model": api_model}
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taskAI = TaskAI(cheapAPI, temperature=0.2, max_tokens=2048)
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info("Finalizing letter ...")
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result=""
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for response in taskAI.purify_letter(full_text=debug_CoT):
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result += response.delta
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yield result
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# gen = stream_together(
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# taskAI.purify_letter(full_text=debug_CoT),
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# )
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# for result in gen:
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# yield result
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def finalize_letter_pdf(api_base, api_key, api_model, jd, cv, cover_letter_text):
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cheapAPI = {"base": api_base, "key": api_key, "model": api_model}
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taskAI = TaskAI(cheapAPI, temperature=0.1, max_tokens=100)
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meta_data = next(taskAI.get_jobapp_meta(JD=jd, CV=cv))
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pdf_context = json.loads(meta_data)
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pdf_context["letter_body"] = cover_letter_text
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return meta_data, compile_pdf(pdf_context,tmpl_path="template_letter.tmpl",output_path=f"/tmp/cover_letter_by_{pdf_context['applicantFullName']}_to_{pdf_context['companyFullName']}.pdf")
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with gr.Blocks(
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title=DEMO_TITLE,
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theme=gr.themes.Base(primary_hue="blue", secondary_hue="sky", neutral_hue="slate"),
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) as app:
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intro = f"""# {DEMO_TITLE}
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+
> You provide job description and résumé. I write Cover letter for you!
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Before you use, please fisrt setup API for 2 AI agents': Cheap AI and Strong AI.
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"""
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gr.Markdown(intro)
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cheap_base = gr.Textbox(
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value=CHEAP_API_BASE, label="API BASE"
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)
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cheap_key = gr.Textbox(value=CHEAP_API_KEY, label="API key", type="password")
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cheap_model = gr.Textbox(value=CHEAP_MODEL, label="Model ID")
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gr.Markdown(
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"---\n**Strong AI**, a thoughtful wordsmith, generates perfect cover letters to make both you and recruiters happy."
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)
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with gr.Group():
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gr.Markdown("## Applicant - CV / Résumé")
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# with gr.Row():
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cv_file = gr.File(
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label="Allowed formats: " + " ".join(CV_EXT),
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file_count="single",
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file_types=CV_EXT,
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type="filepath",
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)
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cv_text = gr.TextArea(
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label="Or enter text",
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placeholder="If attempting to both upload a file and enter text, only this text will be used.",
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)
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with gr.Column(scale=2):
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gr.Markdown("## Result")
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with gr.Accordion("Reformatting", open=True) as reformat_zone:
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).then(fn=lambda:[gr.Accordion("Expert Zone", open=True),gr.Accordion("Reformatting", open=False)],inputs=None, outputs=[expert_zone, reformat_zone]
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).then(fn=run_compose, inputs=[strong_base, strong_key, strong_model, min_jd, min_cv], outputs=[debug_CoT]
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).then(fn=lambda:gr.Accordion("Expert Zone", open=False),inputs=None, outputs=[expert_zone]
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).then(fn=finalize_letter_txt, inputs=[cheap_base, cheap_key, cheap_model, debug_CoT], outputs=[cover_letter_text]
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).then(fn=finalize_letter_pdf, inputs=[cheap_base, cheap_key, cheap_model, jd_info, cv_text, cover_letter_text], outputs=[debug_jobapp, cover_letter_pdf])
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if __name__ == "__main__":
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config.py
CHANGED
@@ -5,11 +5,11 @@ OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or ""
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CHEAP_API_BASE = os.getenv("CHEAP_API_BASE") or OPENAI_API_BASE
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CHEAP_API_KEY = os.getenv("CHEAP_API_KEY") or OPENAI_API_KEY
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CHEAP_MODEL = os.getenv("CHEAP_MODEL") or "gpt-
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STRONG_API_BASE = os.getenv("STRONG_API_BASE") or OPENAI_API_BASE
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STRONG_API_KEY = os.getenv("STRONG_API_KEY") or OPENAI_API_KEY
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STRONG_MODEL = os.getenv("STRONG_MODEL") or "
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IS_SHARE = bool(os.getenv("IS_SHARE")) or False
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CHEAP_API_BASE = os.getenv("CHEAP_API_BASE") or OPENAI_API_BASE
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CHEAP_API_KEY = os.getenv("CHEAP_API_KEY") or OPENAI_API_KEY
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CHEAP_MODEL = os.getenv("CHEAP_MODEL") or "gpt-3.5-turbo"
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STRONG_API_BASE = os.getenv("STRONG_API_BASE") or OPENAI_API_BASE
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STRONG_API_KEY = os.getenv("STRONG_API_KEY") or OPENAI_API_KEY
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STRONG_MODEL = os.getenv("STRONG_MODEL") or "gpt-4"
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IS_SHARE = bool(os.getenv("IS_SHARE")) or False
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data_test.py
CHANGED
@@ -66,10 +66,10 @@ References
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Available upon request.
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"""
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pdf_context = {
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-
"
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-
"
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-
"
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"
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"letter_body": "text,\n\ntest test"
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}
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Available upon request.
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"""
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pdf_context = {
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"companyFullName": "Mastercard",
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"jobTitle": "Project Management Intern",
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"applicantFullName": "Dorothy Gale",
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"applicantContactInformation": "123 Main St., Emerald City, KS 12345, (123) 456-7890, dorothy@wizardofoz.com",
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"letter_body": "text,\n\ntest test"
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}
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taskAI.py
CHANGED
@@ -13,7 +13,7 @@ EXTRACT_INFO = ChatPromptTemplate(
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[
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ChatMessage(
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role="system",
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content="You are a content extractor. You never paraphrase; you only reduce content at the sentence level. Your mission is to extract {to_extract} from user input.
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),
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ChatMessage(role="user", content="{input}"),
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]
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@@ -35,7 +35,7 @@ JSON_API = ChatPromptTemplate(
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[
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ChatMessage(
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role="system",
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content="You are
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),
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ChatMessage(role="user", content="{content}"),
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]
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@@ -51,7 +51,7 @@ LETTER_COMPOSE = ChatPromptTemplate(
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Before officially write the letter, think step by step. First, list what makes a perfect cover letter in general, and in order to write a perfect cover letter, what key points do you have to learn from the RESUME and JOB_DESCRIPTION. Then, carefully analyze the given RESUME and JOB_DESCRIPTION, take a deep breath and propose 3 best tactics to convince recruiter believe the applicant fit for the role. Ensure your thoughts are express clearly and then write the complete cover letter.""",
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),
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ChatMessage(role="user", content="<RESUME>\n{resume}\n</RESUME>\n\n<JOB_DESCRIPTION>\n{jd}</JOB_DESCRIPTION>\n"),
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]
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)
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@@ -82,7 +82,7 @@ class TaskAI(OpenAILike):
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)
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def jd_preprocess(self, input: str):
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return self.stream_chat(EXTRACT_INFO.format_messages(to_extract="
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def cv_preprocess(self, input: str):
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return self.stream_chat(SIMPLIFY_MD.format_messages(input=input))
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@@ -91,9 +91,9 @@ class TaskAI(OpenAILike):
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return self.stream_chat(LETTER_COMPOSE.format_messages(resume=resume, jd=jd))
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def get_jobapp_meta(self, JD, CV):
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meta_JD = self.chat(JSON_API.format_messages(template=keys_to_template(["
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# yield meta_JD
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-
meta_CV = self.chat(JSON_API.format_messages(template=keys_to_template(["
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# yield meta_JD+'\n'+meta_CV
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try:
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meta_JD = json.loads(meta_JD.strip())
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@@ -106,5 +106,5 @@ class TaskAI(OpenAILike):
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yield json.dumps(meta, indent=2)
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def purify_letter(self, full_text):
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return self.stream_chat(EXTRACT_INFO.format_messages(to_extract="the letter
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[
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ChatMessage(
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role="system",
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content="You are a content extractor. You never paraphrase; you only reduce content at the sentence level. Your mission is to extract {to_extract} from user input. Reformat the extraction in a clean style if extraction looks messey.",
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),
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ChatMessage(role="user", content="{input}"),
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]
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[
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ChatMessage(
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role="system",
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content="You are a JSON API. Your mission is to convert user input into a JSON object exactly in this template: {template}",
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),
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ChatMessage(role="user", content="{content}"),
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]
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Before officially write the letter, think step by step. First, list what makes a perfect cover letter in general, and in order to write a perfect cover letter, what key points do you have to learn from the RESUME and JOB_DESCRIPTION. Then, carefully analyze the given RESUME and JOB_DESCRIPTION, take a deep breath and propose 3 best tactics to convince recruiter believe the applicant fit for the role. Ensure your thoughts are express clearly and then write the complete cover letter.""",
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),
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+
ChatMessage(role="user", content="<RESUME>\n{resume}\n</RESUME>\n\n<JOB_DESCRIPTION>\n{jd}</JOB_DESCRIPTION>\n<ANALYSIS_REPORT>"),
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]
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)
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)
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def jd_preprocess(self, input: str):
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return self.stream_chat(EXTRACT_INFO.format_messages(to_extract="the job description part`", input=input))
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def cv_preprocess(self, input: str):
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return self.stream_chat(SIMPLIFY_MD.format_messages(input=input))
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return self.stream_chat(LETTER_COMPOSE.format_messages(resume=resume, jd=jd))
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def get_jobapp_meta(self, JD, CV):
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meta_JD = self.chat(JSON_API.format_messages(template=keys_to_template(["companyFullName", "jobTitle"]), content=JD)).message.content
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# yield meta_JD
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meta_CV = self.chat(JSON_API.format_messages(template=keys_to_template(["applicantFullNname", "applicantContactInformation"]), content=CV)).message.content
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# yield meta_JD+'\n'+meta_CV
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try:
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meta_JD = json.loads(meta_JD.strip())
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yield json.dumps(meta, indent=2)
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def purify_letter(self, full_text):
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+
return self.stream_chat(EXTRACT_INFO.format_messages(to_extract="the cover letter section starting from 'Dear Hiring Manager' or similar to 'Sincerely,' or similar ", input=full_text))
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taskNonAI.py
CHANGED
@@ -18,8 +18,10 @@ def extract_url(url: str) -> Optional[str]:
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cmd = f"""shot-scraper javascript -b firefox \
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"{url}" "
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async () => {{
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const readability = await import('https://cdn.skypack.dev/@mozilla/readability');
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-
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}}"
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"""
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result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
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@@ -43,7 +45,7 @@ def date():
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f"%B %d{'th' if 4 <= current_date.day <= 20 or 24 <= current_date.day <= 30 else ['st', 'nd', 'rd'][current_date.day % 10 - 1]} , %Y")
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def typst_escape(s):
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-
return s.replace('@','\@').replace('#','\#')
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48 |
def compile_pdf(context: dict, tmpl_path: str, output_path="/tmp/cover_letter.pdf"):
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with open(tmpl_path, "r", encoding='utf8') as f:
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cmd = f"""shot-scraper javascript -b firefox \
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"{url}" "
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async () => {{
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+
const sleep = duration => new Promise(resolve => setTimeout(() => resolve(), duration));
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const readability = await import('https://cdn.skypack.dev/@mozilla/readability');
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23 |
+
await sleep(3000);
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24 |
+
return new readability.Readability(document).parse();
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25 |
}}"
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"""
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27 |
result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
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45 |
f"%B %d{'th' if 4 <= current_date.day <= 20 or 24 <= current_date.day <= 30 else ['st', 'nd', 'rd'][current_date.day % 10 - 1]} , %Y")
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46 |
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47 |
def typst_escape(s):
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48 |
+
return str(s).replace('@','\@').replace('#','\#')
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49 |
|
50 |
def compile_pdf(context: dict, tmpl_path: str, output_path="/tmp/cover_letter.pdf"):
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with open(tmpl_path, "r", encoding='utf8') as f:
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template_letter.tmpl
CHANGED
@@ -1,15 +1,15 @@
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1 |
#import "template_base.typ": *
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2 |
#show: letter.with(
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sender: [
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4 |
-
${
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],
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6 |
recipient: [
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7 |
Hiring Manager \
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8 |
-
${
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9 |
],
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10 |
date: [${date_string}],
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11 |
-
subject: [Cover Letter for ${
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12 |
-
name: [${
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13 |
)
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14 |
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15 |
${letter_body}
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1 |
#import "template_base.typ": *
|
2 |
#show: letter.with(
|
3 |
sender: [
|
4 |
+
${applicantContactInformation}
|
5 |
],
|
6 |
recipient: [
|
7 |
Hiring Manager \
|
8 |
+
${companyFullName} \
|
9 |
],
|
10 |
date: [${date_string}],
|
11 |
+
subject: [Cover Letter for ${jobTitle}],
|
12 |
+
name: [${applicantFullName}],
|
13 |
)
|
14 |
|
15 |
${letter_body}
|