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+FROM python:3.11-slim
+
+RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+COPY models ./models
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from pathlib import Path
+from typing import Dict, List, Optional
+
+import json
+import joblib
+import numpy as np
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from sentence_transformers import SentenceTransformer
+
+
+# Paths
+
+
+BASE_DIR = Path(__file__).resolve().parent
+
+MODEL_PATH = BASE_DIR / "models" / "best_logistic_embedding_model.joblib"
+METADATA_PATH = BASE_DIR / "models" / "best_model_metadata.json"
+
+
+
+# FastAPI app
+
+
+app = FastAPI(
+    title="Grievance Department Classifier API",
+    description="Classifies citizen complaints into government departments using MiniLM embeddings + Logistic Regression.",
+    version="1.0.0",
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # later replace with your frontend URL
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+
+# Request / Response Schemas
+
+
+class DepartmentPredictionRequest(BaseModel):
+    complaint_text: str = Field(
+        ...,
+        min_length=3,
+        description="Citizen complaint text",
+        example="Garbage is dumped in an empty plot and bad smell is coming."
+    )
+    location: Optional[str] = Field(
+        default="unknown",
+        description="Optional location, ward, zone, city, or area",
+        example="Ward 12"
+    )
+
+
+class ClassProbability(BaseModel):
+    department: str
+    probability: float
+
+
+class DepartmentPredictionResponse(BaseModel):
+    complaint_text: str
+    predicted_department: str
+    confidence: float
+    probabilities: List[ClassProbability]
+    model: str
+    method: str
+
+
+class BatchDepartmentPredictionRequest(BaseModel):
+    complaints: List[DepartmentPredictionRequest]
+
+
+class BatchDepartmentPredictionResponse(BaseModel):
+    predictions: List[DepartmentPredictionResponse]
+
+
+
+# Model service
+
+
+class DepartmentClassifierService:
+    def __init__(self, model_path: Path, metadata_path: Path):
+        if not model_path.exists():
+            raise FileNotFoundError(f"Model file not found: {model_path}")
+
+        if not metadata_path.exists():
+            raise FileNotFoundError(f"Metadata file not found: {metadata_path}")
+
+        with open(metadata_path, "r", encoding="utf-8") as file:
+            self.metadata = json.load(file)
+
+        self.embedding_model_name = self.metadata.get(
+            "embedding_model_name",
+            "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+        )
+
+        self.classifier = joblib.load(model_path)
+
+        self.embedding_model = SentenceTransformer(self.embedding_model_name)
+
+    def predict(self, complaint_text: str) -> Dict:
+        text = str(complaint_text).strip()
+
+        embedding = self.embedding_model.encode(
+            [text],
+            convert_to_numpy=True,
+            normalize_embeddings=True
+        )
+
+        predicted_department = self.classifier.predict(embedding)[0]
+
+        if hasattr(self.classifier, "predict_proba"):
+            probabilities = self.classifier.predict_proba(embedding)[0]
+            classes = self.classifier.classes_
+
+            probability_items = [
+                {
+                    "department": str(cls),
+                    "probability": float(prob)
+                }
+                for cls, prob in zip(classes, probabilities)
+            ]
+
+            probability_items = sorted(
+                probability_items,
+                key=lambda item: item["probability"],
+                reverse=True
+            )
+
+            confidence = float(max(probabilities))
+
+        else:
+            probability_items = [
+                {
+                    "department": str(predicted_department),
+                    "probability": 1.0
+                }
+            ]
+            confidence = 1.0
+
+        return {
+            "predicted_department": str(predicted_department),
+            "confidence": confidence,
+            "probabilities": probability_items,
+        }
+
+
+classifier_service = DepartmentClassifierService(
+    model_path=MODEL_PATH,
+    metadata_path=METADATA_PATH
+)
+
+
+
+# Routes
+
+
+@app.get("/")
+def home():
+    return {
+        "message": "Grievance Department Classifier API is running",
+        "embedding_model": classifier_service.embedding_model_name,
+        "classifier": classifier_service.metadata.get("classifier", "unknown"),
+    }
+
+
+@app.get("/health")
+def health():
+    return {
+        "status": "ok",
+        "model_loaded": True,
+        "embedding_model": classifier_service.embedding_model_name,
+        "classifier": classifier_service.metadata.get("classifier", "unknown"),
+    }
+
+
+@app.get("/model-info")
+def model_info():
+    return classifier_service.metadata
+
+
+@app.post("/predict-department", response_model=DepartmentPredictionResponse)
+def predict_department(request: DepartmentPredictionRequest):
+    result = classifier_service.predict(request.complaint_text)
+
+    return {
+        "complaint_text": request.complaint_text,
+        "predicted_department": result["predicted_department"],
+        "confidence": result["confidence"],
+        "probabilities": result["probabilities"],
+        "model": classifier_service.embedding_model_name,
+        "method": "MiniLM embeddings + Logistic Regression",
+    }
+
+
+@app.post("/batch-predict-department", response_model=BatchDepartmentPredictionResponse)
+def batch_predict_department(request: BatchDepartmentPredictionRequest):
+    predictions = []
+
+    for item in request.complaints:
+        result = classifier_service.predict(item.complaint_text)
+
+        predictions.append({
+            "complaint_text": item.complaint_text,
+            "predicted_department": result["predicted_department"],
+            "confidence": result["confidence"],
+            "probabilities": result["probabilities"],
+            "model": classifier_service.embedding_model_name,
+            "method": "MiniLM embeddings + Logistic Regression",
+        })
+
+    return {
+        "predictions": predictions
+    }

readme.md

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+---
+title: Grievance Department Classifier API
+emoji: 🏛️
+colorFrom: blue
+colorTo: green
+sdk: docker
+app_port: 7860
+---
+
+# Grievance Department Classifier API
+
+FastAPI backend for classifying citizen complaints into departments.

requirements.txt

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+fastapi
+uvicorn[standard]
+pydantic
+python-multipart
+joblib
+numpy
+scikit-learn
+sentence-transformers
+torch
+transformers