Create model_setup.py

model_setup.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""
+Helper script to prepare models for deployment
+"""
+import os
+import zipfile
+import shutil
+from pathlib import Path
+
+def setup_bert_model():
+    """Extract and setup the fine-tuned BERT model"""
+    
+    zip_path = "fine_tuned_bert_sentiment.zip"
+    extract_path = "./fine_tuned_bert_sentiment"
+    
+    if not os.path.exists(zip_path):
+        print(f"❌ {zip_path} not found. Please upload your fine-tuned BERT model.")
+        return False
+    
+    print(f"📦 Extracting {zip_path}...")
+    
+    # Create extraction directory
+    os.makedirs(extract_path, exist_ok=True)
+    
+    # Extract zip file
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_path)
+    
+    # Verify required files exist
+    required_files = [
+        "config.json",
+        "pytorch_model.bin",
+        "tokenizer_config.json",
+        "vocab.txt"
+    ]
+    
+    missing_files = []
+    for file in required_files:
+        if not os.path.exists(os.path.join(extract_path, file)):
+            missing_files.append(file)
+    
+    if missing_files:
+        print(f"⚠️ Missing required files: {missing_files}")
+        return False
+    
+    print("✅ BERT model setup complete!")
+    return True
+
+def download_fallback_models():
+    """Download fallback models if needed"""
+    from transformers import AutoTokenizer, AutoModel
+    
+    print("📥 Downloading fallback models...")
+    
+    # Download SigLIP model
+    try:
+        AutoTokenizer.from_pretrained("google/siglip-large-patch16-384")
+        AutoModel.from_pretrained("google/siglip-large-patch16-384")
+        print("✅ SigLIP-Large downloaded")
+    except Exception as e:
+        print(f"⚠️ SigLIP-Large download failed: {e}")
+        print("📥 Downloading SigLIP-Base as fallback...")
+        AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")
+        AutoModel.from_pretrained("google/siglip-base-patch16-224")
+    
+    # Download sentiment model
+    AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+    AutoModel.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+    print("✅ Sentiment model downloaded")
+
+if __name__ == "__main__":
+    print("🚀 Setting up Enhanced Ensemble Model...")
+    
+    # Setup BERT model
+    bert_success = setup_bert_model()
+    
+    # Download other models
+    download_fallback_models()
+    
+    if bert_success:
+        print("🎉 All models ready for deployment!")
+    else:
+        print("⚠️ Deployment ready with fallback models. Upload your BERT model for best performance.")