Update pattern_analyzer.py

pattern_analyzer.py

@@ -9,13 +9,12 @@ from pattern_logic import PatternLogic
 class PatternAnalyzer:
     def __init__(self):
         self.model = AutoModelForCausalLM.from_pretrained(
-            "tmmdev/codellama-pattern-analysis",
-            load_in_8bit=True,  # Enable 8-bit quantization
-            device_map="auto",   # Optimize device usage
-            torch_dtype="auto"   # Automatic precision selection
+            "http://localhost:5000/codellama-chart-model",
+            load_in_8bit=True,
+            device_map="auto",
+            torch_dtype="auto"
         )
-        self.tokenizer = AutoTokenizer.from_pretrained("tmmdev/codellama-pattern-analysis")
-
+        self.tokenizer = AutoTokenizer.from_pretrained("http://localhost:5000/codellama-chart-model")
         self.basic_patterns = {
             'channel': {'min_points': 4, 'confidence_threshold': 0.7},
             'triangle': {'min_points': 3, 'confidence_threshold': 0.75},
@@ -27,7 +26,7 @@ class PatternAnalyzer:
         self.pattern_logic = PatternLogic()
 
     def analyze_data(self, ohlcv_data):
-        data_prompt = f"""TASK: Identify high-confidence technical patterns only. 
+        data_prompt = f"""TASK: Identify high-confidence technical patterns only.
         Minimum confidence threshold: 0.8
         Required pattern criteria:
         1. Channel: Must have at least 3 touching points
@@ -57,7 +56,6 @@ class PatternAnalyzer:
             
             for pattern in analysis_data.get('patterns', []):
                 pattern_type = pattern.get('type')
-                
                 if pattern_type in self.basic_patterns:
                     threshold = self.basic_patterns[pattern_type]['confidence_threshold']
                     if pattern.get('confidence', 0) >= threshold:
@@ -70,8 +68,6 @@ class PatternAnalyzer:
                                 'timestamp': pd.Timestamp.now().isoformat()
                             }
                         })
-            
             return patterns
-            
         except json.JSONDecodeError:
             return []