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# app.py
# Complaint Prioritization System wrapped in a Gradio app (and API)
from typing import Dict, Any
import gradio as gr
# ================================
# Configuration (default values)
# ================================
DEFAULT_PRIORITY_SCORE = {
"water": 0.9,
"electricity": 0.9,
"gas": 0.6,
"road": 0.5,
"garbage": 0.2
}
DEPARTMENT_WEIGHT = {
"water": 1.2,
"electricity": 1.2,
"gas": 1.0,
"road": 0.9,
"garbage": 0.8
}
# Helper to convert 0–1 score into labels
def get_priority_label(score: float) -> str:
if score >= 0.75:
return "Critical"
elif score >= 0.55:
return "High"
elif score >= 0.35:
return "Medium"
else:
return "Low"
# Calculate weighted score (0–1)
def calculate_weighted_score(upvotes: int, complaints: int, alpha: float=0.6, beta: float=0.4) -> float:
upvote_score = min(upvotes / 50.0, 1.0) # assume 50 upvotes = max
complaint_score = min(complaints / 20.0, 1.0) # assume 20 complaints = max
weighted = (alpha * complaint_score) + (beta * upvote_score)
return weighted
# Core handler
def handle_complaint(text: str, category: str, complaints: int, upvotes: int) -> Dict[str, Any]:
if category is None:
category = ""
category_key = category.strip().lower()
default_score = DEFAULT_PRIORITY_SCORE.get(category_key, 0.3)
weighted_score = calculate_weighted_score(upvotes=upvotes, complaints=complaints)
department_bias = DEPARTMENT_WEIGHT.get(category_key, 1.0)
raw_final = 0.5 * default_score + 0.5 * weighted_score
final_score = min(raw_final * department_bias, 1.0) # cap at 1.0
result = {
"text": text,
"category": category_key if category_key else "unknown",
"default_score": round(default_score, 2),
"weighted_score": round(weighted_score, 2),
"department_bias": department_bias,
"final_score": round(final_score, 2),
"final_label": get_priority_label(final_score)
}
return result
# ================================
# Gradio UI & API
# ================================
with gr.Blocks() as demo:
gr.Markdown("# Complaint Prioritization API")
gr.Markdown("Provide complaint details. You can also call `/api/predict` for API access.")
with gr.Row():
txt = gr.Textbox(lines=3, label="Complaint text", value="Pothole on main road causing damage.")
cat = gr.Dropdown(
choices=["water", "electricity", "gas", "road", "garbage"],
value="road",
label="Category"
)
with gr.Row():
complaints_in = gr.Number(value=1, label="Number of distinct complaints (integer)")
upvotes_in = gr.Number(value=0, label="Number of upvotes (integer)")
run_btn = gr.Button("Get Priority")
output = gr.JSON(label="Result")
run_btn.click(fn=handle_complaint,
inputs=[txt, cat, complaints_in, upvotes_in],
outputs=output)
# Example load
demo.load(
lambda: handle_complaint(
"Water pipeline leakage near market area.",
"water",
15,
8
),
outputs=output
)
if __name__ == "__main__":
demo.launch()
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