Update app.py

app.py

@@ -71,13 +71,21 @@ def adapt_resume(resume_data, keywords, job_description, model, max_retries=3):
     
     for attempt in range(max_retries):
         try:
-            prompt = f"""You are a CV coach skilled in resume customization and JSON formatting. Consider what the ideal candidate for the target role would be like. You have creative freedom to tailor the provided base Original CV JSON to align with the company's needs using relevant keywords based on their importance level (high: 3x, medium: 2x, low: 1x). Use the language established in the Keywords. IMPORTANT: Consider the company's research and job details to enhance the CV's relevance.
-            Embed relevant keywords throughout specific sections: 'summary', 'experience', 'volunteer', 'interests', 'awards', 'projects' and 'skills' without altering any factual information.
-            Output the modified CV as JSON, strictly maintaining the original structure, keys, and logical flow of information. Use the language specified in the Keywords and avoid adding any fabricated details.
-            Retain specific keys such as 'id' and 'url' as in the original CV, as well as any key found within the 'metadata' section of the schema.
-            Schema: {json.dumps(original_schema)}
+            prompt = f"""As a CV expert, optimize the provided resume JSON for the target role. 
+            Enhance sections (summary, experience, volunteer, interests, awards, projects, skills) by incorporating provided keywords:
+            - High priority (3x weight)
+            - Medium priority (2x weight)
+            - Low priority (1x weight)
+
+            Rules:
+            - Keep all original facts and information
+            - Maintain exact JSON structure and all existing keys
+            - Use natural language from the keywords list
+            - Do not add fictional content
+
+            Base Schema: {json.dumps(original_schema)}
             Keywords: {json.dumps(keywords)}
-            Job: {job_description}"""
+            Job Description: {job_description}"""
             
             response = model.generate_content(prompt)
             tailored_resume = json.loads(response.text)
@@ -123,16 +131,76 @@ def calculate_resume_match(resume_data, keywords):
     
     return normalized_score, matches
 
+def create_match_visualization(original_score, tailored_score, keywords, original_matches, tailored_matches):
+    """Create visualization showing resume match comparison"""
+    
+    # Overall score comparison
+    st.markdown("### 📊 Resume Match Analysis")
+    
+    # Score metrics side by side
+    col1, col2 = st.columns(2)
+    with col1:
+        st.metric(
+            "Original Resume Match Score", 
+            f"{original_score:.1f}%"
+        )
+    with col2:
+        st.metric(
+            "Tailored Resume Match Score", 
+            f"{tailored_score:.1f}%",
+            delta=f"+{tailored_score - original_score:.1f}%"
+        )
+
+    # Keyword analysis by priority
+    st.markdown("### 🎯 Keyword Matches")
+    tabs = st.tabs(["High Priority 🔴", "Medium Priority 🟡", "Low Priority 🟢"])
+    
+    for idx, priority in enumerate(['high', 'medium', 'low']):
+        with tabs[idx]:
+            col1, col2 = st.columns(2)
+            
+            orig_matches = set(original_matches[priority])
+            new_matches = set(tailored_matches[priority])
+            added = new_matches - orig_matches
+            
+            # Original matches
+            with col1:
+                st.markdown("#### Original Matching Keywords")
+                if orig_matches:
+                    for keyword in orig_matches:
+                        st.markdown(f"✓ `{keyword}`")
+                else:
+                    st.info("No matches found")
+            
+            # New matches
+            with col2:
+                st.markdown("#### Added the following Keywords")
+                if added:
+                    for keyword in added:
+                        st.markdown(f"➕ `{keyword}`")
+                else:
+                    st.info("No new matches")
+
 # Page config
 st.set_page_config(page_title="Resume Tailor", page_icon="📄", layout="wide")
 
 # Header
-st.title("🎯 AI Resume Tailor")
+st.title("📄 Curriculum Customization Tool")
 st.markdown("### Transform your resume for your dream job")
 # Sidebar with API key
 with st.sidebar:
-    st.markdown("### Google API Key")
+    st.markdown("### About")
     st.markdown("This tool works with Google's Gemini model, which you can use for free. For more information, visit [Google AI Studio](https://ai.google.dev/aistudio).")
+    
+    # Disclaimer
+    st.warning("""
+    ⚠️ **Disclaimer**
+    
+    This tool is for educational purposes only. 
+    AI-based tools can produce unexpected or inaccurate results. 
+    By using this tool, you accept full responsibility for verifying and using its output.
+    """)
+    
     api_key = st.secrets["google_api_key"]
     if not api_key:
         st.error("API key not found in secrets. Please add your API key to the secrets.")
@@ -148,7 +216,7 @@ with col2:
         st.session_state['original_resume'] = json.load(resume_str)
 
 # Process button
-if st.button("🚀 Tailor Resume", type="primary", use_container_width=True):
+if st.button("🎯 Tailor Resume", type="primary", use_container_width=True):
     if job_url and api_key and resume_file:
         try:
             with st.status("🔄 Processing...") as status:
@@ -173,7 +241,7 @@ if st.button("🚀 Tailor Resume", type="primary", use_container_width=True):
 
             # Results section
             st.markdown("---")
-            st.markdown("### 📊 Results")
+            st.markdown("## 📊 Results")
             
             # Calculate and display scores
             original_score, original_matches = calculate_resume_match(
@@ -185,24 +253,13 @@ if st.button("🚀 Tailor Resume", type="primary", use_container_width=True):
                 st.session_state['keywords']
             )
 
-            score_col1, score_col2 = st.columns(2)
-            with score_col1:
-                st.metric("Original Match", f"{original_score:.1f}%")
-            with score_col2:
-                st.metric("Tailored Match", f"{tailored_score:.1f}%", 
-                         delta=f"+{tailored_score - original_score:.1f}%")
-
-            # Keyword matches
-            with st.expander("🎯 View Keyword Matches"):
-                for priority in ['high', 'medium', 'low']:
-                    st.subheader(f"{priority.title()} Priority")
-                    orig_matches = set(original_matches[priority])
-                    new_matches = set(tailored_matches[priority])
-                    added = new_matches - orig_matches
-                    
-                    st.write("✓ Original:", ", ".join(orig_matches) if orig_matches else "None")
-                    if added:
-                        st.write("✚ Added:", f"<span style='background-color: #d4edda;'>{', '.join(added)}</span>", unsafe_allow_html=True)
+            create_match_visualization(
+                original_score,
+                tailored_score,
+                st.session_state['keywords'],
+                original_matches,
+                tailored_matches
+            )
 
             # Download section
             st.markdown("### 📥 Download")
@@ -214,7 +271,7 @@ if st.button("🚀 Tailor Resume", type="primary", use_container_width=True):
                 use_container_width=True
             ):
                 webbrowser.open_new_tab("https://rxresu.me/")
-                st.info("📝 Opening Resume Builder in new tab...")
+                st.info("📝 Resume Builder opened in new tab")
 
         except Exception as e:
             st.error(f"An error occurred: {str(e)}")