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Update app.py
Browse files
app.py
CHANGED
@@ -55,30 +55,21 @@ def extract_text_from_docx(docx_file):
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text += para.text + "\n"
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return text
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def generate_response(message: str,
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conversation = [
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{"role": "system", "content": system_prompt}
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]
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for prompt, answer in history:
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conversation.extend([
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{"role": "user", "content": prompt},
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{"role": "assistant", "content": answer},
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])
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conversation.append({"role": "user", "content": message})
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response = client.chat.completions.create(
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model="llama-3.1-8B-Instant",
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messages=conversation,
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temperature=temperature,
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max_tokens=max_tokens,
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stream=
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)
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for chunk in response:
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if chunk.choices[0].delta.content is not None:
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partial_message += chunk.choices[0].delta.content
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yield partial_message
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def analyze_resume(resume_text, job_description):
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prompt = f"""
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@@ -92,7 +83,7 @@ def analyze_resume(resume_text, job_description):
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Job Description: {job_description}
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Resume: {resume_text}
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"""
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return generate_response(prompt,
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def rephrase_text(text):
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prompt = f"""
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@@ -100,7 +91,7 @@ def rephrase_text(text):
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Original Text: {text}
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"""
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return generate_response(prompt,
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def clear_conversation():
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return [], None
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@@ -124,10 +115,6 @@ with gr.Blocks(css=CSS, theme="Nymbo/Nymbo_Theme") as demo:
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rephrased_output = gr.Markdown()
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with gr.Accordion("⚙️ Parameters", open=False):
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system_prompt = gr.Textbox(
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value="You are a helpful ATS resume expert, specialized in resume analysis and optimization.",
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label="System Prompt",
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)
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temperature = gr.Slider(
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minimum=0, maximum=1, step=0.1, value=0.5, label="Temperature",
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)
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text += para.text + "\n"
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return text
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def generate_response(message: str, system_prompt: str, temperature: float = 0.5, max_tokens: int = 512):
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conversation = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": message}
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]
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response = client.chat.completions.create(
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model="llama-3.1-8B-Instant",
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messages=conversation,
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temperature=temperature,
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max_tokens=max_tokens,
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stream=False
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)
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return response.choices[0].message.content
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def analyze_resume(resume_text, job_description):
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prompt = f"""
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Job Description: {job_description}
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Resume: {resume_text}
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"""
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return generate_response(prompt, "You are an expert ATS resume analyzer.")
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def rephrase_text(text):
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prompt = f"""
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Original Text: {text}
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"""
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return generate_response(prompt, "You are an expert in rephrasing content for ATS optimization.")
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def clear_conversation():
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return [], None
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rephrased_output = gr.Markdown()
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with gr.Accordion("⚙️ Parameters", open=False):
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temperature = gr.Slider(
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minimum=0, maximum=1, step=0.1, value=0.5, label="Temperature",
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)
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