Update README.md

README.md

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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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+Here is a comprehensive GitHub README structure tailored to the Molex Project Engineer role. This is designed to show a hiring manager that you understand the intersection of Data Engineering, AI, and Business Value.
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+Project: Sales-Ops Intelligence Hub (DCH Prototype)
+Automating Lead Scoring & CRM Integration for Global Sales Operations
+1. Project Overview
+This Proof of Concept (PoC) addresses the common business challenge of "Data Silos" and manual, repetitive lead triaging. Inspired by the Digital Customer Hub (DCH) model, this solution automates the ingestion of unstructured customer data (emails/logs) and uses Natural Language Processing (NLP) to generate high-priority sales opportunities.
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+Direct Alignment with Molex JD:
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+Automation: Eliminates manual data entry and lead categorization.
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+Lead Scoring: Implements an AI-driven priority matrix.
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+Data Integration: Generates structured JSON outputs ready for SAP and Salesforce ingestion.
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+2. Technical Architecture
+The system is built using a modern data engineering stack:
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+Intelligence Engine: Hugging Face BART-Large-MNLI (Zero-Shot Classification).
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+UI/Interface: Gradio (for stakeholder demonstration and feedback).
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+Data Processing: Python (Pandas/JSON).
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+Environment: Google Colab.
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+3. Implementation Details
+A. Intent Classification & Lead Scoring
+The model identifies four specific business intents without requiring a pre-labeled dataset:
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+Urgent RFQ (High Priority)
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+Sales Opportunity (High Priority)
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+Technical Support (Medium Priority)
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+General Inquiry (Low Priority)
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+B. Data Pipeline Logic
+The system performs a Clean-Score-Structure workflow:
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+Ingestion: Receives raw text from Sales/Customer Service logs.
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+AI Analysis: Calculates a confidence score for the detected intent.
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+Prioritization: A logic-based script assigns a "System Priority" based on the AI's confidence and the intent type.
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+Output: Produces a standardized JSON object to maintain Data Integrity across global systems.
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+4. Business Impact (Projected)
+Efficiency: Estimated 90% reduction in time spent by sales teams on manual lead qualification.
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+Accuracy: Improved lead routing through a synchronized "Intelligence Engine."
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+Scalability: Modular Python code allows for rapid deployment as an API or cloud-based microservice (AWS/Azure).
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+5. How to Run
+Open the Google Colab Notebook.
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+Install dependencies: pip install transformers torch gradio pandas.
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+Run the final cell to launch the Gradio interactive dashboard.
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+Input a sample customer email (e.g., "I need a technical quote for the new connector series ASAP") to see real-time lead scoring.