okok

app.py

@@ -11,76 +11,54 @@ import pandas as pd
 from flask import Flask, jsonify, request
 from flask_cors import CORS
 from sklearn.preprocessing import LabelEncoder
-from datasets import load_dataset as hf_load_dataset
 
-warnings.filterwarnings("ignore")
+warnings.filterwarnings('ignore')
 
 app = Flask(__name__)
 CORS(app, resources={r"/api/*": {"origins": "*"}})
 
+
 # ============================================================================
-# Paths
-# ============================================================================
+# FIX: Use raw strings or forward slashes for Windows paths
+# ================================================
+# Base directory = where app.py lives
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-MODEL_DIR = os.path.join(BASE_DIR, "model")
-DATASET_PATH = os.path.join(MODEL_DIR, "dataset_cleaned.csv")
+
+# model/ is INSIDE the same folder as app.py
+MODEL_DIR = os.path.join(BASE_DIR, 'model')
+
+DATASET_PATH = os.path.join(MODEL_DIR, 'dataset_cleaned.csv')
 
 print(f"\n📂 BASE_DIR: {BASE_DIR}")
-print(f"📂 MODEL_DIR: {MODEL_DIR}")
 print(f"📊 DATASET_PATH: {DATASET_PATH}")
-print(f"✓ Model dir exists: {os.path.exists(MODEL_DIR)}")
-print(f"✓ Dataset exists (local): {os.path.exists(DATASET_PATH)}\n")
+print(f"📂 MODEL_DIR: {MODEL_DIR}")
+print(f"✓ Dataset exists: {os.path.exists(DATASET_PATH)}")
+print(f"✓ Model dir exists: {os.path.exists(MODEL_DIR)}\n")
 
-# ============================================================================
-# Globals
-# ============================================================================
 df = None
 model1 = None
 model2 = None
 le = None
 
-# ============================================================================
-# Dataset loader (HF first, local fallback)
-# ============================================================================
-def load_crime_dataset():
+def load_dataset():
+    """Load the crime dataset"""
     global df
-
-    # ---- Attempt 1: Hugging Face dataset (LFS-safe) ----
-    try:
-        print("🚀 Attempting to load dataset via Hugging Face Datasets...")
-        ds = hf_load_dataset(
-            "Unmeshraj/opensight",
-            data_files="model/dataset_cleaned.csv",
-            split="train"
-        )
-
-        df = ds.to_pandas()
-        print(f"✅ HF dataset loaded: {len(df)} rows")
-        print(f"📊 Columns: {list(df.columns)}")
-        return True
-
-    except Exception as hf_error:
-        print("⚠️ HF dataset load failed, falling back to local CSV")
-        print(hf_error)
-
-    # ---- Attempt 2: Local CSV fallback ----
     try:
         if not os.path.exists(DATASET_PATH):
-            print(f"❌ Local dataset not found at: {DATASET_PATH}")
+            print(f"❌ Dataset not found at: {DATASET_PATH}")
             df = pd.DataFrame()
             return False
-
+        
         df = pd.read_csv(DATASET_PATH)
-        print(f"✅ Local dataset loaded: {len(df)} rows")
-        print(f"📊 Columns: {list(df.columns)}")
+        print(f"✅ Dataset loaded: {len(df)} records")
+        print(f"   Columns: {list(df.columns)}")
         return True
-
-    except Exception as local_error:
-        print("❌ Local dataset load failed")
+    except Exception as e:
+        print(f"❌ Error loading dataset: {e}")
         traceback.print_exc()
         df = pd.DataFrame()
         return False
-    
+
 def load_models():
     """Load trained models with fallback options"""
     global model1, model2, le
@@ -499,5 +477,5 @@ if __name__ == '__main__':
     print("\n" + "="*60)
     print("✅ Starting OpenSight API Server")
     print("="*60 + "\n")
-    app.run(host="0.0.0.0",port=int(os.environ.get("PORT", 7860)),debug=False)
+    app.run(host="0.0.0.0",port=int(os.environ.get("PORT", 5000)),debug=False)
 

model/dataset_cleaned.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:efbc3e8d31e67cc706d95f596e568ac2aa9ee1a0f8f623be43d0308ad3566630
-size 41960106
+oid sha256:98fa23c026bddae6fd52b1c5f2ce89865f0b54e096b71ea7e7eb81f80f84c516
+size 41959917