Try to fix fairseq-signals installation with PyTorch 2.0.1 and better debugging

test_real_ecg.py

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+import requests
+import numpy as np
+import pandas as pd
+import json
+
+# Your deployed Hugging Face Space URL
+SPACE_URL = "https://mystic-cbk-ecg-fm-api.hf.space"
+
+def load_ecg_data(csv_file):
+    """Load ECG data from CSV file"""
+    print(f"📁 Loading ECG data from: {csv_file}")
+    
+    # Read the CSV file
+    df = pd.read_csv(csv_file)
+    
+    print(f"📊 ECG Data Shape: {df.shape}")
+    print(f"📊 Leads: {list(df.columns)}")
+    print(f"📊 Samples per lead: {len(df)}")
+    
+    # Convert to the format expected by the API
+    # Each lead should be a list of float values
+    ecg_data = []
+    for lead in df.columns:
+        ecg_data.append(df[lead].astype(float).tolist())
+    
+    print(f"✅ ECG data loaded successfully!")
+    print(f"📊 Data format: {len(ecg_data)} leads × {len(ecg_data[0])} samples")
+    
+    return ecg_data
+
+def test_api_with_real_ecg():
+    """Test the deployed ECG-FM API with real ECG data"""
+    
+    print("🧪 Testing ECG-FM API with Real ECG Data")
+    print("=" * 60)
+    print(f"🌐 API URL: {SPACE_URL}")
+    print()
+    
+    # Load the real ECG data
+    try:
+        ecg_data = load_ecg_data("ecg_d2c4fdc0-01ee-4210-a027-54a527329cd8.csv")
+    except Exception as e:
+        print(f"❌ Error loading ECG data: {e}")
+        return
+    
+    print()
+    
+    # Test 1: Health check
+    print("1️⃣ Testing health endpoint...")
+    try:
+        r = requests.get(f"{SPACE_URL}/healthz", timeout=30)
+        print(f"   Status: {r.status_code}")
+        if r.status_code == 200:
+            response = r.json()
+            print(f"   Response: {response}")
+            print(f"   Mode: {response.get('mode', 'unknown')}")
+        else:
+            print(f"   Error: {r.text}")
+    except Exception as e:
+        print(f"   ❌ Error: {e}")
+    
+    print()
+    
+    # Test 2: Root endpoint
+    print("2️⃣ Testing root endpoint...")
+    try:
+        r = requests.get(f"{SPACE_URL}/", timeout=30)
+        print(f"   Status: {r.status_code}")
+        if r.status_code == 200:
+            response = r.json()
+            print(f"   Response: {response}")
+        else:
+            print(f"   Error: {r.text}")
+    except Exception as e:
+        print(f"   ❌ Error: {e}")
+    
+    print()
+    
+    # Test 3: Predict endpoint with REAL ECG data
+    print("3️⃣ Testing predict endpoint with REAL ECG data...")
+    
+    # Prepare the payload with real ECG data
+    payload = {
+        "signal": ecg_data,
+        "fs": 500  # 500 Hz sampling rate
+    }
+    
+    try:
+        print("   📤 Sending REAL ECG data...")
+        print(f"   📊 Input: 12 leads × {len(ecg_data[0])} samples")
+        print(f"   📊 Sampling rate: 500 Hz")
+        print(f"   📊 Duration: {len(ecg_data[0])/500:.1f} seconds")
+        print("   ⏳ Waiting for inference...")
+        
+        r = requests.post(f"{SPACE_URL}/predict", json=payload, timeout=180)  # 3 min timeout
+        print(f"   Status: {r.status_code}")
+        
+        if r.status_code == 200:
+            result = r.json()
+            print(f"   ✅ Success!")
+            print(f"   📊 Mode: {result.get('mode', 'unknown')}")
+            print(f"   📊 Output shape: {result.get('output_shape', 'unknown')}")
+            print(f"   📊 Input shape: {result.get('input_shape', 'unknown')}")
+            print(f"   📊 Output length: {len(result.get('output', []))}")
+            
+            if result.get('mode') == 'fallback':
+                print(f"   💡 Note: {result.get('note', 'Running in fallback mode')}")
+            else:
+                print(f"   🎯 Full ECG-FM model inference completed!")
+                
+        else:
+            print(f"   ❌ Error: {r.text}")
+            
+    except requests.exceptions.Timeout:
+        print("   ⏰ Timeout - Inference taking longer than expected")
+    except Exception as e:
+        print(f"   ❌ Error: {e}")
+    
+    print()
+    print("=" * 60)
+    print("🏁 Real ECG Testing Complete!")
+    print()
+    print("📊 Results Summary:")
+    print("   - If you see 200 status codes: API is working with real data!")
+    print("   - If you see fallback mode: API works but model not fully loaded")
+    print("   - If you see full mode: Complete ECG-FM inference working!")
+    print()
+    print("🔗 Your API is live at:")
+    print(f"   {SPACE_URL}")
+    print()
+    print("📱 Test manually:")
+    print(f"   Health: {SPACE_URL}/healthz")
+    print(f"   Root: {SPACE_URL}/")
+
+if __name__ == "__main__":
+    test_api_with_real_ecg()