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model.py

@@ -1,52 +1,60 @@
-# model.py
 from sentence_transformers import SentenceTransformer, util
-from sql_templates import sql_templates, sql_keyword_aliases, fuzzy_aliases
+from sql_templates import sql_templates, sql_keyword_aliases, fuzzy_aliases, conflicting_phrases, greeting_templates
+from data_loader import load_rules
 import torch
 
-# Load training rules (string-to-SQL map)
-from data_loader import load_rules  # you can split this for cleanliness
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# 📘 Load rules
 rules = load_rules()
 
-# Load embedding model (lightweight, fast)
+# 🔍 Load semantic model
 model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2")
-
-# Pre-compute embeddings of training prompts
 train_prompts = list(rules.keys())
 train_embeddings = model.encode(train_prompts, convert_to_tensor=True)
 
+# 🤖 Load local LLM model
+llm_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(llm_name)
+llm_model = AutoModelForCausalLM.from_pretrained(llm_name, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
+llm_pipeline = pipeline("text-generation", model=llm_model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
+
 def oracle_sql_suggester(prompt):
     prompt_clean = prompt.strip().lower()
 
-    # Try direct rule match
+    # ✅ Exact rule
     if prompt_clean in rules:
         return rules[prompt_clean]
 
-    # Check template keywords (first!)
+    # ✅ Greeting handling
+    for greet_key, greet_reply in greeting_templates.items():
+        if greet_key in prompt_clean:
+            return greet_reply
+        
+    # ✅ Conflicting phrase
+    for terms, response in conflicting_phrases.items():
+        if all(term in prompt_clean for term in terms):
+            return response
+
+    # ✅ Keyword alias
     for word in prompt_clean.split():
-     if word in sql_keyword_aliases:
-        mapped_key = sql_keyword_aliases[word]
-        return sql_templates[mapped_key]
-    
+        if word in sql_keyword_aliases:
+            mapped_key = sql_keyword_aliases[word]
+            return sql_templates.get(mapped_key)
+
+    # ✅ Template match
     for key, template in sql_templates.items():
-     if key.replace("_", " ") in prompt_clean or key in prompt_clean:
-        return template
-# Try keyword aliases (word-level match)
-    for word in prompt_clean.split():
-     if word in sql_keyword_aliases:
-       return sql_templates[sql_keyword_aliases[word]]  
-   
-# Try fuzzy alias matches
+        if key in prompt_clean or key.replace("_", " ") in prompt_clean:
+            return template
+
+    # ✅ Fuzzy match
     for fuzzy_phrase, mapped_key in fuzzy_aliases.items():
-     if fuzzy_phrase in prompt_clean:
-        return sql_templates[mapped_key]
+        if fuzzy_phrase in prompt_clean:
+            return sql_templates.get(mapped_key)
 
-  
-    
-    
-    # Semantic matching
+    # ✅ Semantic match
     user_embedding = model.encode(prompt_clean, convert_to_tensor=True)
     cosine_scores = util.cos_sim(user_embedding, train_embeddings)
-
     top_match_index = torch.argmax(cosine_scores).item()
     top_score = cosine_scores[0][top_match_index].item()
 
@@ -54,6 +62,12 @@ def oracle_sql_suggester(prompt):
         matched_prompt = train_prompts[top_match_index]
         return rules[matched_prompt]
 
-    # Fallback
-    return "🤖 Sorry, I couldn’t understand that. Please try rephrasing your request."    
-    
+    # ✅ Local LLM fallback
+    try:
+        prompt_text = f"Generate an Oracle SQL query or guidance for the following request:\n{prompt}\n\nSQL:"
+        output = llm_pipeline(prompt_text, max_new_tokens=256, do_sample=True, temperature=0.5)[0]["generated_text"]
+        return "🤖 (Local LLM): " + output.split("SQL:")[-1].strip()
+    except Exception as e:
+        print("⚠️ Local LLM error:", e)
+        return "🤖 Sorry, I couldn’t process that locally. Please try a simpler prompt."
+

sql_templates.py

@@ -39,3 +39,21 @@ fuzzy_aliases = {
     "change row": "update",
     "erase record": "delete"
 }
+
+conflicting_phrases = {
+    ("modify", "new"): "⚠️ You cannot modify a new record. Consider using INSERT instead.",
+    ("update", "new"): "⚠️ You cannot update new data. Did you mean to INSERT?",
+    ("delete", "new"): "⚠️ You cannot delete something that doesn't exist yet.",
+}
+
+# 🤖 Greeting phrases and responses
+greeting_templates = {
+    "hello": "👋 Hello! How can I assist you with SQL or PL/SQL today?",
+    "hi": "👋 Hi there! Need help with Oracle SQL or PL/SQL?",
+    "hey": "👋 Hey! Let me know your SQL or PLSQL question.",
+    "good morning": "🌅 Good morning! What Oracle task can I help with?",
+    "good afternoon": "🌞 Good afternoon! Ready to write some SQL?",
+    "good evening": "🌆 Good evening! Need SQL/PLSQL guidance?",
+    "how are you": "🤖 I'm a bot but I'm always ready to help! What do you need?",
+    "what can you do": "💡 I can help you generate Oracle SQL/PLSQL from descriptions, explain syntax, suggest queries, and more.",
+}