Update app.py

app.py

@@ -1,88 +1,94 @@
 import gradio as gr
-    import nltk
-    from nltk.sentiment.vader import SentimentIntensityAnalyzer
-    import re
+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+import re
 
-    # Download required NLTK resources
+# Download required NLTK resources
+try:
+    nltk.data.find('vader_lexicon')
+except nltk.downloader.DownloadError:
     nltk.download('vader_lexicon')
+try:
+    nltk.data.find('punkt')
+except nltk.downloader.DownloadError:
     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.
-        """
+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  # Start with a base score
-        timeliness_score = 100
-        safety_score = 100
-        communication_score = 100
+    quality_score = 100  # Start with a base score
+    timeliness_score = 100
+    safety_score = 100
+    communication_score = 100
 
-        # --- 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
-    
-        # --- 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
-    
-        # --- 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
-    
-        # --- 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
+    # --- 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
 
-        # 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
+    # --- 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
 
-        # Ensure scores are within 0-100 range
-        quality_score = max(0, min(quality_score, 100))
-        timeliness_score = max(0, min(timeliness_score, 100))
-        safety_score = max(0, min(safety_score, 100))
-        communication_score = max(0, min(communication_score, 100))
+    # --- 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
 
-        # 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)
+    # --- 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
 
-        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)
-        }
+    # 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
 
-    if __name__ == "__main__":
-        iface = gr.Interface(
-            fn=calculate_scores,
-            inputs=[
-                gr.inputs.Textbox(lines=5, label="Work Completion Details"),
-                gr.inputs.Textbox(lines=5, label="Delay Reports"),
-                gr.inputs.Textbox(lines=5, label="Incident Logs")
-            ],
-            outputs=gr.outputs.Label(label="Performance Scores"),
-            title="Vendor Performance Scoring",
-            description="Enter vendor logs to calculate performance scores."
-        )
-        iface.launch()
+    # Ensure scores are within 0-100 range
+    quality_score = max(0, min(quality_score, 100))
+    timeliness_score = max(0, min(timeliness_score, 100))
+    safety_score = max(0, min(safety_score, 100))
+    communication_score = max(0, min(communication_score, 100))
+
+    # 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)
+
+    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.inputs.Textbox(lines=5, label="Work Completion Details"),
+            gr.inputs.Textbox(lines=5, label="Delay Reports"),
+            gr.inputs.Textbox(lines=5, label="Incident Logs")
+        ],
+        outputs=gr.outputs.Label(label="Performance Scores"),
+        title="Vendor Performance Scoring",
+        description="Enter vendor logs to calculate performance scores."
+    )
+    iface.launch()