Updated model to flan t5 large for better accuracy

gradio_app.py

@@ -105,7 +105,7 @@ class FlanT5Analyzer:
                 progress(0.1, "🍩 Loading tokenizer...")
             
             self.tokenizer = AutoTokenizer.from_pretrained(
-                "google/flan-t5-base",
+                "google/flan-t5-large",
                 use_fast=True
             )
             
@@ -113,7 +113,7 @@ class FlanT5Analyzer:
                 progress(0.3, "🍰 Loading model...")
             
             self.model = AutoModelForSeq2SeqLM.from_pretrained(
-                "google/flan-t5-base",
+                "google/flan-t5-large",
                 torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
                 device_map="auto"
             )
@@ -504,7 +504,7 @@ async def analyze_content_flant5(
         result = {
             "detected_triggers": triggers,
             "confidence": "High - Content detected" if triggers != ["None"] else "High - No concerning content detected",
-            "model": "google/flan-t5-base",
+            "model": "google/flan-t5-large",
             "analysis_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         }
 
@@ -516,7 +516,7 @@ async def analyze_content_flant5(
         return {
             "detected_triggers": ["Error occurred during analysis"],
             "confidence": "Error",
-            "model": "google/flan-t5-base",
+            "model": "google/flan-t5-large",
             "analysis_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
             "error": str(e)
         }