app.py

import gradio as gr
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import re

# Download required NLTK resources
try:
    nltk.data.find('sentiment/vader_lexicon.zip')
except LookupError:
    nltk.download('vader_lexicon')

try:
    nltk.data.find('tokenizers/punkt/english.pickle')
except LookupError:
    nltk.download('punkt')

def calculate_scores(work_completion, delay_reports, incident_logs):
    """
    Calculates vendor performance scores based on log entries.
    This is a simplified, rule-based demo version.
    """

    quality_score = 100.0  # Initialize as float
    timeliness_score = 100.0
    safety_score = 100.0
    communication_score = 100.0

    # --- Quality Score ---
    quality_keywords_positive = ["good", "excellent", "high quality", "efficient", "precise"]
    quality_keywords_negative = ["poor", "bad", "low quality", "defect", "error"]
    for keyword in quality_keywords_positive:
        quality_score += work_completion.lower().count(keyword) * 5
    for keyword in quality_keywords_negative:
        quality_score -= work_completion.lower().count(keyword) * 10
    quality_score = max(0.0, min(quality_score, 100.0))

    # --- Timeliness Score ---
    timeliness_keywords_positive = ["on time", "punctual", "early", "ahead of schedule"]
    timeliness_keywords_negative = ["late", "delayed", "behind schedule"]
    for keyword in timeliness_keywords_positive:
        timeliness_score += work_completion.lower().count(keyword) * 5 + delay_reports.lower().count(keyword) * 5
    for keyword in timeliness_keywords_negative:
        timeliness_score -= delay_reports.lower().count(keyword) * 10
    timeliness_score = max(0.0, min(timeliness_score, 100.0))

    # --- Safety Score ---
    safety_keywords_positive = ["safe", "safety protocol", "no accidents", "precaution"]
    safety_keywords_negative = ["unsafe", "accident", "injury", "hazard"]
    for keyword in safety_keywords_positive:
        safety_score += work_completion.lower().count(keyword) * 5 + incident_logs.lower().count(keyword) * 5
    for keyword in safety_keywords_negative:
        safety_score -= incident_logs.lower().count(keyword) * 15
    safety_score = max(0.0, min(safety_score, 100.0))

    # --- Communication Score ---
    communication_keywords_positive = ["clear communication", "responsive", "proactive", "helpful"]
    communication_keywords_negative = ["unresponsive", "late reply", "miscommunication", "unclear"]
    for keyword in communication_keywords_positive:
        communication_score += work_completion.lower().count(keyword) * 5 + delay_reports.lower().count(keyword) * 5 + incident_logs.lower().count(keyword) * 5
    for keyword in communication_keywords_negative:
        communication_score -= delay_reports.lower().count(keyword) * 10
    communication_score = max(0.0, min(communication_score, 100.0))

    # Basic Sentiment Analysis (Optional - Requires NLTK Download)
    analyzer = SentimentIntensityAnalyzer()
    vs = analyzer.polarity_scores(work_completion + " " + delay_reports + " " + incident_logs)
    sentiment_score = vs['compound']
    communication_score += sentiment_score * 10  # Adjust weight as needed
    communication_score = max(0.0, min(communication_score, 100.0))

    # Calculate Final Score (as per Salesforce formula)
    final_score = (0.4 * quality_score + 0.3 * timeliness_score + 0.15 * safety_score + 0.15 * communication_score)
    final_score = max(0.0, min(final_score, 100.0))

    return {
        "Quality Score": round(quality_score, 2),
        "Timeliness Score": round(timeliness_score, 2),
        "Safety Score": round(safety_score, 2),
        "Communication Score": round(communication_score, 2),
        "Final Score": round(final_score, 2)
    }

if __name__ == "__main__":
    iface = gr.Interface(
        fn=calculate_scores,
        inputs=[
            gr.Textbox(lines=5, label="Work Completion Details"),
            gr.Textbox(lines=5, label="Delay Reports"),
            gr.Textbox(lines=5, label="Incident Logs")
        ],
        outputs=gr.Label(label="Performance Scores"),
        title="Vendor Performance Scoring",
        description="Enter vendor logs to calculate performance scores."
    )
    iface.launch()