feat: min words 80, stability fixes, docs link, test-case PDFs refs

app.py

@@ -158,12 +158,12 @@ async def analyze_text(request: AnalysisRequest):
         if not request.text or len(request.text.strip()) == 0:
             raise HTTPException(status_code=400, detail="Text cannot be empty")
         
-        # Check text length for meaningful analysis (200-7000 words)
+        # Check text length for meaningful analysis (80-7000 words)
         word_count = len(request.text.split())
-        if word_count < 200:
+        if word_count < 80:
             raise HTTPException(
                 status_code=400,
-                detail="Text is too short for analysis. Please provide at least 200 words for accurate AI detection and sentiment analysis."
+                detail="Text is too short for analysis. Please provide at least 80 words for accurate AI detection and sentiment analysis."
             )
         
         if word_count > 7000:
@@ -200,12 +200,12 @@ async def detect_ai(request: AnalysisRequest):
         if not request.text or len(request.text.strip()) == 0:
             raise HTTPException(status_code=400, detail="Text cannot be empty")
         
-        # Check text length (200-7000 words)
+        # Check text length (80-7000 words)
         word_count = len(request.text.split())
-        if word_count < 200:
+        if word_count < 80:
             raise HTTPException(
                 status_code=400,
-                detail="Text is too short. Please provide at least 200 words."
+                detail="Text is too short. Please provide at least 80 words."
             )
         elif word_count > 7000:
             raise HTTPException(

model_handler.py

@@ -428,14 +428,14 @@ class AIDetectionModelHandler:
         # Split text into sentences
         sentences = sent_tokenize(text)
         
-        # Validate input text length (200-7000 words)
+        # Validate input text length (80-7000 words)
         total_words = len(text.split())
-        if total_words < 200:
+        if total_words < 80:
             return {
                 'prediction': 'Human' if overall_label == 0 else 'AI',
                 'confidence': abs(overall_prob - 0.5) * 2,
                 'is_mixed': False,
-                'reason': f'Text too short for analysis ({total_words} words, minimum 200 words required)',
+                'reason': f'Text too short for analysis ({total_words} words, minimum 80 words required)',
                 'overall_probability': overall_prob,
                 'modified_probability': overall_prob,
                 'chunk_analysis': []
@@ -694,10 +694,10 @@ class AIDetectionModelHandler:
         Returns:
             Complete analysis results with model-based sentiment features
         """
-        # Validate input text length (200-7000 words)
+        # Validate input text length (80-7000 words)
         total_words = len(text.split())
-        if total_words < 200:
-            raise ValueError(f"Text too short for analysis ({total_words} words, minimum 200 words required)")
+        if total_words < 80:
+            raise ValueError(f"Text too short for analysis ({total_words} words, minimum 80 words required)")
         elif total_words > 7000:
             raise ValueError(f"Text too long for analysis ({total_words} words, maximum 7000 words allowed)")