Update app.py

app.py

@@ -2,17 +2,16 @@ import gradio as gr
 import torch
 import numpy as np
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from transformers import RobertaForSequenceClassification, RobertaTokenizer
 
-# Load both sentiment and abuse models from the same Hugging Face repo
-repo_id = "SamanthaStorm/Tether"
+# 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
-sentiment_model = AutoModelForSequenceClassification.from_pretrained(repo_id, subfolder="sentiment")
-sentiment_tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="sentiment")
-
-# Load abuse detection model
-abuse_model = AutoModelForSequenceClassification.from_pretrained(repo_id, subfolder="abuse")
-abuse_tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="abuse")
+# 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)
 
 LABELS = [
     "gaslighting", "mockery", "dismissiveness", "control", "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
@@ -82,9 +81,9 @@ 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 = abuse_tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
-        outputs = abuse_model(**inputs)
+        outputs = 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]))
@@ -104,14 +103,14 @@ def analyze_messages(input_text, risk_flags):
     abuse_level = calculate_abuse_level(scores, THRESHOLDS)
     abuse_description = interpret_abuse_level(abuse_level)
 
-    resources = (
-        "Immediate assistance recommended. Please seek professional help or contact emergency services."
-        if danger_flag_count >= 2 else
-        "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
-    )
+    if danger_flag_count >= 2:
+        resources = "Immediate assistance recommended. Please seek professional help or contact emergency services."
+    else:
+        resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
 
     scored_patterns = [(label, score) for label, score in zip(PATTERN_LABELS, scores[:15])]
     top_patterns = sorted(scored_patterns, key=lambda x: x[1], reverse=True)[:2]
+
     top_pattern_explanations = "\n".join([
         f"\u2022 {label.replace('_', ' ').title()}: {EXPLANATIONS.get(label, 'No explanation available.')}"
         for label, _ in top_patterns