Update app.py

app.py

@@ -1,22 +1,13 @@
 # app.py
 """
-Complaint Prioritization API (Gradio-based)
-Drop this file into a Hugging Face Space (SDK: Gradio) or run locally.
+Complaint Prioritization API (no category input).
+Inputs: text, complaints, upvotes
+Outputs: score + label
 """
 
-from typing import List, Dict, Any
+from typing import Dict, Any
 import gradio as gr
 
-# ================================
-# Default priority values mapped 0–1
-DEFAULT_PRIORITY_SCORE = {
-    "water": 0.9,
-    "electricity": 0.9,
-    "gas": 0.6,
-    "road": 0.5,
-    "garbage": 0.2
-}
-
 # Helper to convert 0–1 score into labels
 def get_priority_label(score: float) -> str:
     if score >= 0.75:
@@ -37,37 +28,33 @@ def calculate_weighted_score(upvotes: int, complaints: int, alpha: float = 0.6,
     return weighted
 
 # Handle a single complaint
-def handle_complaint(text: str, category: str, complaints: int, upvotes: int) -> Dict[str, Any]:
-    default_score = DEFAULT_PRIORITY_SCORE.get(category.lower(), 0.3)
+def handle_complaint(text: str, complaints: int, upvotes: int) -> Dict[str, Any]:
     weighted_score = calculate_weighted_score(upvotes, complaints)
-    final_score = 0.5 * default_score + 0.5 * weighted_score
-
     return {
         "text": text,
-        "category": category,
-        "default_score": round(default_score, 2),
-        "weighted_score": round(weighted_score, 2),
-        "final_score": round(final_score, 2),
-        "final_label": get_priority_label(final_score)
+        "complaints": complaints,
+        "upvotes": upvotes,
+        "final_score": round(weighted_score, 2),
+        "final_label": get_priority_label(weighted_score)
     }
 
-# API entrypoint for a single complaint (for UI)
-def predict_single(text: str, category: str, complaints: int, upvotes: int):
+# API entrypoint for single complaint (for UI)
+def predict_single(text: str, complaints: int, upvotes: int):
     try:
         complaints = int(complaints)
         upvotes = int(upvotes)
     except Exception:
         return {"error": "complaints and upvotes must be integers"}
-    return handle_complaint(text, category, complaints, upvotes)
+    return handle_complaint(text, complaints, upvotes)
 
 # API entrypoint for batch complaints (JSON text input)
 def predict_batch(json_string: str):
     """
-    Accepts a JSON array string (list of dicts with keys: text, category, complaints, upvotes)
+    Accepts a JSON array string (list of dicts with keys: text, complaints, upvotes)
     Example:
     [
-      {"text":"Pothole","category":"road","complaints":2,"upvotes":5},
-      {"text":"Leak","category":"water","complaints":15,"upvotes":8}
+      {"text":"Pothole on main road","complaints":2,"upvotes":5},
+      {"text":"Water leakage","complaints":15,"upvotes":8}
     ]
     """
     import json
@@ -81,32 +68,31 @@ def predict_batch(json_string: str):
     results = []
     for it in items:
         t = it.get("text", "")
-        cat = it.get("category", "")
         complaints = int(it.get("complaints", 0))
         upvotes = int(it.get("upvotes", 0))
-        results.append(handle_complaint(t, cat, complaints, upvotes))
+        results.append(handle_complaint(t, complaints, upvotes))
     return {"results": results}
 
-# Small UI using Gradio; Spaces will host this and expose a programmatic endpoint automatically
+# Small UI using Gradio
 with gr.Blocks() as demo:
-    gr.Markdown("## Complaint Prioritization API\nProvide a single complaint or a JSON list for batch processing.")
+    gr.Markdown("## Complaint Prioritization API (No Category Input)")
     with gr.Tab("Single complaint"):
         txt = gr.Textbox(label="Complaint text", value="Huge pothole on main road, damaging cars daily.")
-        cat = gr.Dropdown(label="Category", choices=["water","electricity","gas","road","garbage","other"], value="road")
         comp = gr.Number(label="Number of complaints", value=2, precision=0)
         upv = gr.Number(label="Number of upvotes", value=5, precision=0)
         out = gr.JSON(label="Result")
         btn = gr.Button("Predict")
-        btn.click(fn=predict_single, inputs=[txt, cat, comp, upv], outputs=out)
+        btn.click(fn=predict_single, inputs=[txt, comp, upv], outputs=out)
 
     with gr.Tab("Batch (JSON array)"):
-        batch_in = gr.Textbox(label="JSON array of complaints", lines=10, value='[{"text":"Leak near market","category":"water","complaints":15,"upvotes":8}]')
+        batch_in = gr.Textbox(
+            label="JSON array of complaints", 
+            lines=10, 
+            value='[{"text":"Leak near market","complaints":15,"upvotes":8}]'
+        )
         batch_out = gr.JSON(label="Batch results")
         batch_btn = gr.Button("Predict batch")
         batch_btn.click(fn=predict_batch, inputs=batch_in, outputs=batch_out)
 
-    gr.Markdown("### Programmatic usage\nUse the Spaces' `gradio` API endpoint `/api/predict/` (or use the `predict_batch` JSON route). See README for examples.")
-
-# When running locally in debug mode, this will start a Gradio server.
 if __name__ == "__main__":
     demo.launch()