Upload model.py

model.py

@@ -0,0 +1,49 @@
+from typing import Literal, TypedDict
+
+SentimentClass = Literal["negative", "neutral", "positive"]
+
+class FeedbackAnalysis(TypedDict):
+    sentiment: SentimentClass
+    needs_followup: bool
+    suggested_priority: str
+
+def analyze_feedback(rating: int, followup_flag: str) -> FeedbackAnalysis:
+    """Map numeric rating and follow-up flag to sentiment and priority.
+
+    - rating: 1–5
+    - followup_flag: "yes" or "no"
+    """
+    if rating <= 2:
+        sentiment: SentimentClass = "negative"
+    elif rating == 3:
+        sentiment = "neutral"
+    else:
+        sentiment = "positive"
+
+    flag = followup_flag.strip().lower() == "yes"
+
+    if not flag:
+        priority = "low" if sentiment == "positive" else "normal"
+    else:
+        if sentiment == "negative":
+            priority = "high"
+        elif sentiment == "neutral":
+            priority = "normal"
+        else:
+            priority = "medium"
+
+    return {
+        "sentiment": sentiment,
+        "needs_followup": flag,
+        "suggested_priority": priority,
+    }
+
+if __name__ == "__main__":
+    tests = [
+        (5, "no"),
+        (4, "yes"),
+        (3, "yes"),
+        (2, "yes"),
+    ]
+    for r, f in tests:
+        print(r, f, "->", analyze_feedback(r, f))