Update app.py

app.py

@@ -4,14 +4,15 @@ import numpy as np
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
 
-# Load fine-tuned sentiment model from Hugging Face
-sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment")
-sentiment_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-sentiment")
+# Load fine-tuned sentiment model (DistilBERT)
+sentiment_model_name = "SamanthaStorm/tether-sentiment"
+sentiment_model = AutoModelForSequenceClassification.from_pretrained(sentiment_model_name)
+sentiment_tokenizer = AutoTokenizer.from_pretrained(sentiment_model_name)
 
-# Load abuse pattern model
-model_name = "SamanthaStorm/Tether"
-model = RobertaForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
-tokenizer = RobertaTokenizer.from_pretrained(model_name, trust_remote_code=True)
+# Load abuse pattern model (RoBERTa)
+abuse_model_name = "SamanthaStorm/abuse-pattern-detector-v2"
+abuse_model = RobertaForSequenceClassification.from_pretrained(abuse_model_name)
+abuse_tokenizer = RobertaTokenizer.from_pretrained(abuse_model_name)
 
 LABELS = [
     "gaslighting", "mockery", "dismissiveness", "control", "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
@@ -81,9 +82,9 @@ def analyze_messages(input_text, risk_flags):
 
     adjusted_thresholds = {k: v * 0.8 for k, v in THRESHOLDS.items()} if sentiment_label == "NEGATIVE" else THRESHOLDS.copy()
 
-    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+    inputs = abuse_tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
-        outputs = model(**inputs)
+        outputs = abuse_model(**inputs)
     scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
 
     pattern_count = sum(score > adjusted_thresholds[label] for label, score in zip(PATTERN_LABELS, scores[:15]))
@@ -120,7 +121,6 @@ def analyze_messages(input_text, risk_flags):
         f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n"
         f"Most Likely Patterns:\n{top_pattern_explanations}\n\n"
         f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n"
-        "The Danger Assessment is a validated tool that helps identify serious risk in intimate partner violence.\n\n"
         f"Resources: {resources}\n\n"
         f"Sentiment: {sentiment_label} (Confidence: {sentiment_score*100:.2f}%)"
     )