Update app.py

app.py

@@ -12,7 +12,7 @@ from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Set page title and hide sidebar
 st.set_page_config(
-    page_title="Resume Analyzer and Company Suitability Checker",
+    page_title="Resume-Google Job Match Analyzer",
     initial_sidebar_state="collapsed"
 )
 
@@ -24,6 +24,9 @@ st.markdown("""
 </style>
 """, unsafe_allow_html=True)
 
+# Pre-defined company description for Google
+GOOGLE_DESCRIPTION = """Google LLC, a global leader in technology and innovation, specializes in internet services, cloud computing, artificial intelligence, and software development. As part of Alphabet Inc., Google seeks candidates with strong problem-solving skills, adaptability, and collaboration abilities. Technical roles require proficiency in programming languages such as Python, Java, C++, Go, or JavaScript, with expertise in data structures, algorithms, and system design. Additionally, skills in AI, cybersecurity, UX/UI design, and digital marketing are highly valued. Google fosters a culture of innovation, expecting candidates to demonstrate creativity, analytical thinking, and a passion for cutting-edge technology."""
+
 #####################################
 # Preload Models - Optimized
 #####################################
@@ -177,14 +180,15 @@ def extract_skills_and_work(text):
     """Extract both skills and work experience at once to save processing time"""
     # Common skill categories - reduced keyword list for speed
     skill_categories = {
-        "Programming": ["Python", "Java", "JavaScript", "HTML", "CSS", "SQL", "C++", "C#"],
-        "Data Science": ["Machine Learning", "Data Analysis", "Statistics", "TensorFlow", "PyTorch"],
-        "Database": ["SQL", "MySQL", "MongoDB", "Database"],
-        "Web Development": ["React", "Angular", "Node.js", "Frontend", "Backend"],
-        "Software Development": ["Agile", "Scrum", "Git", "DevOps", "Docker"],
-        "Cloud": ["AWS", "Azure", "Google Cloud", "Cloud"],
-        "Business": ["Project Management", "Business Analysis", "Leadership"],
-        "Tools": ["Excel", "PowerPoint", "Tableau", "Power BI", "JIRA"]
+        "Programming": ["Python", "Java", "JavaScript", "HTML", "CSS", "SQL", "C++", "C#", "Go"],
+        "Data Science": ["Machine Learning", "Data Analysis", "Statistics", "TensorFlow", "PyTorch", "AI", "Algorithms"],
+        "Database": ["SQL", "MySQL", "MongoDB", "Database", "NoSQL", "PostgreSQL"],
+        "Web Development": ["React", "Angular", "Node.js", "Frontend", "Backend", "Full-Stack"],
+        "Software Development": ["Agile", "Scrum", "Git", "DevOps", "Docker", "System Design"],
+        "Cloud": ["AWS", "Azure", "Google Cloud", "Cloud Computing"],
+        "Security": ["Cybersecurity", "Network Security", "Encryption", "Security"],
+        "Business": ["Project Management", "Business Analysis", "Leadership", "Teamwork"],
+        "Design": ["UX/UI", "User Experience", "Design Thinking", "Adobe"]
     }
     
     # Work experience extraction
@@ -301,12 +305,12 @@ def summarize_resume_text(resume_text):
     return formatted_summary, execution_time
 
 #####################################
-# Function: Evaluate with TinyLlama
+# Function: Evaluate Google Fit with TinyLlama
 #####################################
 @st.cache_data(show_spinner=False)
-def evaluate_with_tiny_llama(candidate_summary, company_info, _evaluator=None):
+def evaluate_google_fit(candidate_summary, _evaluator=None):
     """
-    Use TinyLlama to evaluate the match between a candidate's resume and company requirements.
+    Use TinyLlama to evaluate how well the candidate matches with Google's requirements.
     """
     start_time = time.time()
     
@@ -314,22 +318,21 @@ def evaluate_with_tiny_llama(candidate_summary, company_info, _evaluator=None):
     
     # Format the chat prompt for TinyLlama's chat format
     prompt = f"""<|im_start|>system
-You are an expert HR recruiter. Your task is to evaluate how well a candidate's profile matches with a company's requirements. Be concise but thorough in your evaluation.
+You are an expert technical recruiter at Google. Your task is to evaluate how well a candidate's profile matches with Google's hiring requirements. Be focused and specific in your evaluation.
 <|im_end|>
 
 <|im_start|>user
-I need to evaluate a job candidate against company requirements. Please:
-1. Analyze the match between the candidate and the position
-2. Give a suitability score from 0-100
-3. Provide 2-3 sentences explaining your evaluation
-4. List the top 3 strengths of the candidate for this role
-5. List 1-2 potential gaps if any
+I need to evaluate if this candidate is a good fit for Google. Please:
+1. Score the candidate's fit for Google from 0-100
+2. Write a brief evaluation (2-3 sentences) explaining why they would or wouldn't be a good fit
+3. Mention 1-2 specific strengths relevant to Google
+4. Mention 1 specific area where they might need improvement to better fit Google's requirements
 
 Candidate Profile:
 {candidate_summary}
 
-Company Requirements:
-{company_info}
+Google's Requirements:
+{GOOGLE_DESCRIPTION}
 <|im_end|>
 
 <|im_start|>assistant
@@ -361,8 +364,8 @@ Company Requirements:
             normalized_score = 0.5  # Default if no group was extracted
     else:
         # If no explicit score, try to infer from sentiments
-        positive_words = ['excellent', 'perfect', 'outstanding', 'ideal', 'great']
-        negative_words = ['poor', 'inadequate', 'insufficient', 'lacks', 'mismatch']
+        positive_words = ['excellent', 'perfect', 'outstanding', 'ideal', 'great', 'strong']
+        negative_words = ['poor', 'inadequate', 'insufficient', 'lacks', 'mismatch', 'weak']
         
         positive_count = sum(assistant_response.lower().count(word) for word in positive_words)
         negative_count = sum(assistant_response.lower().count(word) for word in negative_words)
@@ -385,28 +388,25 @@ Company Requirements:
 #####################################
 # Main Streamlit Interface - with Progress Reporting
 #####################################
-st.title("Resume Analyzer and Company Suitability Checker")
+st.title("Google Resume Match Analyzer")
 st.markdown(
     """
-Upload your resume file in **.docx**, **.doc**, or **.txt** format. The app performs the following tasks:
-1. Extracts text from the resume.
-2. Uses AI to generate a structured candidate summary with name, age, expected job industry, previous work experience, and skills.
-3. Uses TinyLlama AI to evaluate the candidate's suitability for the company and provide detailed feedback.
+Upload your resume file in **.docx**, **.doc**, or **.txt** format to see how well you match with Google's hiring requirements. The app performs the following tasks:
+1. Extracts text from your resume.
+2. Uses AI to generate a structured candidate summary.
+3. Evaluates your fit for Google, providing a match score and specific feedback.
 """
 )
 
+# Display Google's requirements
+with st.expander("Google's Requirements", expanded=False):
+    st.write(GOOGLE_DESCRIPTION)
+
 # File uploader
 uploaded_file = st.file_uploader("Upload your resume (.docx, .doc, or .txt)", type=["docx", "doc", "txt"])
 
-# Company description text area
-company_prompt = st.text_area(
-    "Enter the company description or job requirements:",
-    height=150,
-    help="Enter a detailed description of the company culture, role requirements, and desired skills.",
-)
-
 # Process button with optimized flow
-if uploaded_file is not None and company_prompt and st.button("Analyze Resume"):
+if uploaded_file is not None and st.button("Analyze My Google Fit"):
     # Create a placeholder for the progress bar
     progress_bar = st.progress(0)
     status_text = st.empty()
@@ -425,36 +425,57 @@ if uploaded_file is not None and company_prompt and st.button("Analyze Resume"):
         progress_bar.progress(75)
         
         # Display summary
-        st.subheader("Candidate Summary")
+        st.subheader("Your Resume Summary")
         st.markdown(summary)
         st.info(f"Summary generated in {summarization_time:.2f} seconds")
         
-        # Step 3: Evaluate with TinyLlama
-        status_text.text("Step 3/3: Evaluating candidate with TinyLlama...")
-        suitability_score, evaluation, evaluation_time = evaluate_with_tiny_llama(
-            summary, company_prompt, _evaluator=models['evaluator']
+        # Step 3: Evaluate Google fit
+        status_text.text("Step 3/3: Evaluating your fit for Google...")
+        fit_score, evaluation, evaluation_time = evaluate_google_fit(
+            summary, _evaluator=models['evaluator']
         )
         progress_bar.progress(100)
         
         # Clear status messages
         status_text.empty()
         
-        # Display suitability results
-        st.subheader("Suitability Assessment")
+        # Display Google fit results
+        st.subheader("Google Fit Assessment")
         
-        # Display score with appropriate color
-        score_percent = int(suitability_score * 100)
-        if suitability_score >= 0.85:
-            st.success(f"**Matching Score:** {score_percent}%")
-        elif suitability_score >= 0.70:
-            st.success(f"**Matching Score:** {score_percent}%")
-        elif suitability_score >= 0.50:
-            st.warning(f"**Matching Score:** {score_percent}%")
+        # Display score with appropriate color and emoji
+        score_percent = int(fit_score * 100)
+        if fit_score >= 0.85:
+            st.success(f"**Google Match Score:** {score_percent}% 🌟")
+        elif fit_score >= 0.70:
+            st.success(f"**Google Match Score:** {score_percent}% ✅")
+        elif fit_score >= 0.50:
+            st.warning(f"**Google Match Score:** {score_percent}% ⚠️")
         else:
-            st.error(f"**Matching Score:** {score_percent}%")
+            st.error(f"**Google Match Score:** {score_percent}% 🔍")
         
         # Display the full evaluation
-        st.markdown("### Detailed Evaluation")
+        st.markdown("### Feedback from Google AI Recruiter")
         st.markdown(evaluation)
             
-        st.info(f"Evaluation completed in {evaluation_time:.2f} seconds using TinyLlama")
+        st.info(f"Evaluation completed in {evaluation_time:.2f} seconds")
+        
+        # Add potential next steps based on the score
+        st.subheader("Recommended Next Steps")
+        if fit_score >= 0.80:
+            st.markdown("""
+            - Consider applying for positions at Google that match your experience
+            - Prepare for technical interviews by practicing algorithms and system design
+            - Review Google's interview process and STAR method for behavioral questions
+            """)
+        elif fit_score >= 0.60:
+            st.markdown("""
+            - Focus on strengthening the improvement areas mentioned in the evaluation
+            - Work on projects that demonstrate your skills in Google's key technology areas
+            - Consider taking additional courses in areas where Google has shown interest
+            """)
+        else:
+            st.markdown("""
+            - Build experience in areas matching Google's requirements
+            - Develop projects showcasing problem-solving abilities and technical skills
+            - Consider gaining more experience before applying, or target specific Google roles that better match your profile
+            """)