ok

app.py

@@ -1104,13 +1104,15 @@ YASH_PROFILE = {
             }
         },
         {
-            "name": "Inhance - AI-Powered LinkedIn Profile Optimizer",
-            "description": "Hosted application providing personalized LinkedIn profile feedback and optimization",
-            "technologies": ["Python", "Streamlit", "GROQ Cloud", "Mixtral LLM"],
+            "name": "Inhance & Interactive Portfolio",
+            "description": "Built the product flow (evaluation → recommendation → resume generation) and integrated LLM agents for conversational help.",
+            "technologies": ["Streamlit", "GROQ Cloud", "Mixtral LLM", "Multi-Agent System", "LinkedIn API", "ATS Analysis", "LaTeX", "PDF Processing"],
             "key_features": [
-                "Multi-agent system for profile evaluation",
-                "Targeted role optimization",
-                "Chatbot guidance for improvements"
+                "Result: tangible recruiter-facing demos, ATS scoring, and exportable resumes",
+                "Add an interactive widget on portfolio: 'Score my LinkedIn' demo with live ATS output",
+                "Provide before/after LinkedIn profile examples and downloadable LaTeX resume templates",
+                "Multi-agent AI system for profile evaluation with optimization suggestions",
+                "Multi-format support (PDF, DOCX, TXT) with professional LaTeX formatting"
             ],
             "impact": "Helps professionals optimize their LinkedIn presence for better job opportunities",
             "rich_content": {
@@ -1142,13 +1144,15 @@ YASH_PROFILE = {
             }
         },
         {
-            "name": "Financial Compliance Automation Tool",
-            "description": "AI product management - analyzed RBI compliance requirements and identified automation opportunity worth ₹10% annual savings for banks.",
-            "technologies": ["Regulatory Tech", "Market Analysis", "Python", "Streamlit", "GROQ Cloud", "Gemma 9B", "Vector Databases", "FAISS"],
+            "name": "FinLLM-RAG: RBI Compliance Automation",
+            "description": "Combined regulatory research with RAG model engineering; validated ₹10% cost-savings case and improved model accuracy to ~80%.",
+            "technologies": ["Regulatory Tech", "Market Analysis", "Python", "Mixtral LLM", "GROQ Cloud", "FAISS", "Custom Model Training"],
             "key_features": [
-                "I analyzed RBI compliance requirements, identifying automation opportunity worth ₹10% annual savings for banks",
-                "I achieved 80% accuracy in compliance predictions through iterative AI product refinement",
-                "I implemented dynamic vector search with FAISS for real-time regulatory updates and query handling"
+                "Packaged outputs into investor-ready demos and compliance dashboards",
+                "Publish a short compliance playbook showing sample rule → model output → human review loop",
+                "Include accuracy/confusion matrix screenshot and cost-savings calculation that led to the ₹10% claim",
+                "Custom RAG system for regulatory document processing with Mixtral LLM integration",
+                "Iterative AI product refinement methodology for compliance automation"
             ],
             "impact": "Revolutionary AI compliance tool with 90% verification time reduction and 80% regulatory accuracy",
             "rich_content": {
@@ -1180,14 +1184,15 @@ YASH_PROFILE = {
             }
         },
         {
-            "name": "Cryptocurrency Fetching Agent",
-            "description": "Real-time cryptocurrency information system with multilingual support",
-            "technologies": ["Python", "Streamlit", "Web Scraping", "Google Translation", "Together AI", "Llama 3.2 8B"],
+            "name": "Additional Tools: Churn Predictor & Utilities",
+            "description": "Produced robust analytics (churn 94% accuracy) and multimodal utilities that showcase end-to-end AI product thinking — from data collection to deployment and UX.",
+            "technologies": ["Python", "Random Forest", "Decision trees", "Power BI", "Numpy", "Pandas", "Seaborn", "Matplotlib"],
             "key_features": [
-                "Real-time cryptocurrency prices",
-                "Multilingual query handling",
-                "Dynamic error management",
-                "Multi-currency support"
+                "Attach Power BI embed or screenshots highlighting feature importance and actionable insights",
+                "Provide code link + README showing evaluation pipeline and data preprocessing steps",
+                "94% accuracy in customer churn prediction with Random Forest and Decision Tree algorithms",
+                "Interactive Power BI dashboard for comprehensive business insights",
+                "Visual analytics pipeline with Seaborn and Matplotlib for data storytelling"
             ],
             "impact": "Provides seamless cryptocurrency information access across language barriers"
         },
@@ -1284,6 +1289,7 @@ YASH_PROFILE = {
             "organization": "Vacha NGO",
             "role": "English & Computer Instructor",
             "duration": "July 2024",
+            "description": "I designed innovative AI-enhanced learning modules for underprivileged students with improved digital literacy engagement.",
             "activities": [
                 "Coached English and computer skills to students",
                 "Crafted innovative learning aids to improve engagement"