Update app.py

app.py

@@ -68,6 +68,7 @@ INTERVIEW_QUESTIONS_INSTRUCTIONS = """
 </div>
 """
 
+# Also update the disclaimer styles to match
 COVER_LETTER_DISCLAIMER = """
 <p style="font-style: italic; color: #cccccc; background-color: #000000; padding: 10px; border-radius: 5px;">
 Disclaimer: This cover letter is generated based on the provided job description and resume. 
@@ -85,13 +86,11 @@ They should be reviewed and adjusted to better fit the specific role, company cu
 TITLE = "<h1>📄 ATS Resume Analyzer 📄</h1>"
 PLACEHOLDER = "Chat with AI about your resume and job descriptions..."
 
-# ---------------- File parsers ----------------
-
 def extract_text_from_pdf(pdf_file):
     reader = PdfReader(pdf_file)
     text = ""
     for page in reader.pages:
-        text += page.extract_text() or ""
+        text += page.extract_text()
     return text
 
 def extract_text_from_docx(docx_file):
@@ -101,8 +100,6 @@ def extract_text_from_docx(docx_file):
         text += para.text + "\n"
     return text
 
-# ---------------- Groq chat helper ----------------
-
 def generate_response(message: str, system_prompt: str, temperature: float, max_tokens: int):
     conversation = [
         {"role": "system", "content": system_prompt},
@@ -110,7 +107,7 @@ def generate_response(message: str, system_prompt: str, temperature: float, max_
     ]
 
     response = client.chat.completions.create(
-        model="llama-3.1-8b-instant",
+        model="llama-3.1-8B-Instant",
         messages=conversation,
         temperature=temperature,
         max_tokens=max_tokens,
@@ -119,93 +116,34 @@ def generate_response(message: str, system_prompt: str, temperature: float, max_
 
     return response.choices[0].message.content
 
-# ---------------- NEW: Recruiter-style scoring ----------------
-
+    
 def analyze_resume_with_job_description(resume_text, job_description, temperature, max_tokens):
     prompt = f"""
-You are a senior technical recruiter with 12+ years hiring for AI, Data Science, Machine Learning,
-and Java development roles (10+ years experience required). Analyze this CV for a client role.
-
-Focus on:
-- Python/R, TensorFlow/PyTorch, scikit-learn
-- NLP/CV/LLMs
-- Java (Spring Boot, microservices)
-- AWS/GCP, APIs/databases
-- 10+ years hands-on experience
-
-CRITERIA (total 100):
-- SKILLS (40): ML frameworks (10), DS tools (8), Java advanced (10), cloud/DevOps (6), APIs/DB (6).
-- EDUCATION (20): 20 = MS/PhD CS/AI/ML, 15 = BS CS, 10 = strong certs (Google ML, AWS), 0 = irrelevant.
-- EXPERIENCE (40): 40 = 10+ yrs relevant with impact & leadership; scale linearly (e.g., 8 yrs = 32).
-
-TASK:
-1) Extract key skills, education, roles, years, and major achievements.
-2) Score skills/education/experience with brief justification.
-3) Compute overall score (sum) and round.
-4) Decide SHORTLIST = YES if overall ≥ 80 else NO.
-5) Provide 3–5 bullet points of feedback.
-
-Return a clear **recruiter report** in this format:
-
-Overall Score: XX/100
-Shortlisted: YES or NO
-
-Skills Score: AA/40 – short justification
-Education Score: BB/20 – short justification
-Experience Score: CC/40 – short justification
-
-Feedback:
-- point 1
-- point 2
-- point 3
-- point 4 (optional)
-- point 5 (optional)
-
-Then add a short summary (2–3 lines) for the hiring manager.
-
-Job Description:
-{job_description}
-
-Resume:
-{resume_text}
-"""
-    return generate_response(prompt, "You are an expert ATS recruiter and CV scorer.", temperature, max_tokens)
+    Please analyze the following resume in the context of the job description provided. Strictly check every single line in the job description and analyze the resume for exact matches. Maintain high ATS standards and give scores only to the correct matches. Focus on missing core skills and soft skills. Provide the following details:
+    1. The match percentage of the resume to the job description.
+    2. A list of missing keywords.
+    3. Final thoughts on the resume's overall match with the job description in 3 lines.
+    4. Recommendations on how to add the missing keywords and improve the resume in 3-4 points with examples.
+    Job Description: {job_description}
+    Resume: {resume_text}
+    """
+    return generate_response(prompt, "You are an expert ATS resume analyzer.", temperature, max_tokens)
 
 def analyze_resume_without_job_description(resume_text, temperature, max_tokens):
     prompt = f"""
-You are a senior technical recruiter with 12+ years hiring for AI, Data Science, Machine Learning,
-and Java roles. Analyze this CV **without a specific JD**, but still score it for a generic
-AI/DS/ML/Java Senior Developer role.
-
-Use the same scoring:
-
-- Skills: 0–40
-- Education: 0–20
-- Experience: 0–40
-- Overall: 0–100, and SHORTLIST if ≥ 80.
+    Please analyze the following resume without a specific job description. Provide the following details:
+    1. An overall score out of 10 for the resume.
+    2. Suggestions for improvements based on the following criteria:
+       - Impact (quantification, repetition, verb usage, tenses, responsibilities, spelling & consistency)
+       - Brevity (length, bullet points, filler words)
+       - Style (buzzwords, dates, contact details, personal pronouns, active voice, consistency)
+       - Sections (summary, education, skills, unnecessary sections)
+    3. A cumulative assessment of all the above fields.
+    4. Recommendations for improving the resume in 3-4 points with examples.
+    Resume: {resume_text}
+    """
+    return generate_response(prompt, "You are an expert ATS resume analyzer.", temperature, max_tokens)
 
-Return in this format:
-
-Overall Score: XX/100
-Shortlisted: YES or NO
-
-Skills Score: AA/40 – short justification
-Education Score: BB/20 – short justification
-Experience Score: CC/40 – short justification
-
-Feedback:
-- point 1
-- point 2
-- point 3
-- point 4 (optional)
-- point 5 (optional)
-
-Then a 2–3 line summary for the hiring manager.
-
-Resume:
-{resume_text}
-"""
-    return generate_response(prompt, "You are an expert ATS recruiter and CV scorer.", temperature, max_tokens)
 
 def analyze_resume(resume_text, job_description, with_job_description, temperature, max_tokens):
     if with_job_description:
@@ -213,52 +151,43 @@ def analyze_resume(resume_text, job_description, with_job_description, temperatu
     else:
         return analyze_resume_without_job_description(resume_text, temperature, max_tokens)
 
-# ---------------- Other existing features (rephraser, CL, IQ) ----------------
+
 
 def rephrase_text(text, temperature, max_tokens):
     prompt = f"""
-Please rephrase the following text according to ATS standards, including quantifiable measures and
-improvements where possible. Maintain precise and concise points that pass ATS screening.
-
-Original Text:
-{text}
-"""
+    Please rephrase the following text according to ATS standards, including quantifiable measures and improvements where possible. Maintain precise and concise points which will pass ATS screening:
+    Original Text: {text}
+    """
     return generate_response(prompt, "You are an expert in rephrasing content for ATS optimization.", temperature, max_tokens)
 
 def clear_conversation():
     return [], None
 
+
 def generate_cover_letter(resume_text, job_description, temperature, max_tokens):
     prompt = f"""
-Using the provided resume and job description, create a compelling cover letter. The cover letter should:
-1. Be tailored to the specific job and company.
-2. Highlight relevant skills and experiences from the resume.
-3. Show enthusiasm for the role and company.
-4. Be professional and concise (about 250-300 words).
-
-Resume:
-{resume_text}
-
-Job Description:
-{job_description}
-"""
+    Using the provided resume and job description, create a compelling cover letter. The cover letter should:
+    1. Be tailored to the specific job and company.
+    2. Highlight relevant skills and experiences from the resume.
+    3. Show enthusiasm for the role and company.
+    4. Be professional and concise (about 250-300 words).
+    Resume: {resume_text}
+    Job Description: {job_description}
+    """
     return generate_response(prompt, "You are an expert in writing tailored cover letters.", temperature, max_tokens)
 
 def generate_interview_questions(job_description, temperature, max_tokens):
     prompt = f"""
-Based on the following job description, generate a list of 10 probable interview questions. Include a mix of:
-1. Role-specific technical questions (if applicable)
-2. Behavioral questions related to the required skills
-3. Questions about the candidate's experience and background
-4. Questions to assess cultural fit
-
-Job Description:
-{job_description}
-"""
+    Based on the following job description, generate a list of 10 probable interview questions. Include a mix of:
+    1. Role-specific technical questions (if applicable)
+    2. Behavioral questions related to the required skills
+    3. Questions about the candidate's experience and background
+    4. Questions to assess cultural fit
+    Ensure the questions are tailored to the specific job role and requirements.
+    Job Description: {job_description}
+    """
     return generate_response(prompt, "You are an expert in creating relevant interview questions based on job descriptions.", temperature, max_tokens)
 
-# ---------------- Gradio UI ----------------
-
 with gr.Blocks(css=CSS, theme="Nymbo/Nymbo_Theme") as demo:
     gr.HTML(TITLE)
 
@@ -301,11 +230,11 @@ with gr.Blocks(css=CSS, theme="Nymbo/Nymbo_Theme") as demo:
             minimum=0, maximum=1, step=0.1, value=0.5, label="Temperature",
         )
         max_tokens = gr.Slider(
-            minimum=50, maximum=1024, step=1, value=512, label="Max tokens",
+            minimum=50, maximum=1024, step=1, value=1024, label="Max tokens",
         )
         
-    def update_job_description_visibility(with_job_description_val):
-        return gr.update(visible=with_job_description_val)
+    def update_job_description_visibility(with_job_description):
+        return gr.update(visible=with_job_description)
 
     with_job_description.change(
         update_job_description_visibility,
@@ -351,4 +280,4 @@ with gr.Blocks(css=CSS, theme="Nymbo/Nymbo_Theme") as demo:
     gr.HTML(FOOTER_TEXT)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()