Update app.py

app.py

@@ -1,105 +1,86 @@
 import gradio as gr
 import torch
-from transformers import RobertaForSequenceClassification, RobertaTokenizer
 import numpy as np
-from transformers import pipeline
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from transformers import RobertaForSequenceClassification, RobertaTokenizer
 
-# Load sentiment analysis model
-sentiment_analyzer = pipeline("sentiment-analysis")
+# Load fine-tuned sentiment model
+sentiment_model_path = "./sentiment_model"
+sentiment_model = AutoModelForSequenceClassification.from_pretrained(sentiment_model_path)
+sentiment_tokenizer = AutoTokenizer.from_pretrained(sentiment_model_path)
 
-# Load model and tokenizer
+# Load abuse pattern model
 model_name = "SamanthaStorm/abuse-pattern-detector-v2"
 model = RobertaForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
 tokenizer = RobertaTokenizer.from_pretrained(model_name, trust_remote_code=True)
 
-# Define labels (18 total)
 LABELS = [
-    "gaslighting", "mockery", "dismissiveness", "control",
-    "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
-    "contradictory_statements", "manipulation", "deflection", "insults",
-    "obscure_formal", "recovery_phase", "non_abusive", "suicidal_threat", "physical_threat",
-    "extreme_control"
+    "gaslighting", "mockery", "dismissiveness", "control", "guilt_tripping", "apology_baiting", "blame_shifting", "projection",
+    "contradictory_statements", "manipulation", "deflection", "insults", "obscure_formal", "recovery_phase", "non_abusive",
+    "suicidal_threat", "physical_threat", "extreme_control"
 ]
 
-# Custom thresholds for each label
 THRESHOLDS = {
-    "gaslighting": 0.25,
-    "mockery": 0.15,
-    "dismissiveness": 0.30,
-    "control": 0.43,
-    "guilt_tripping": 0.19,
-    "apology_baiting": 0.45,
-    "blame_shifting": 0.23,
-    "projection": 0.50,
-    "contradictory_statements": 0.25,
-    "manipulation": 0.25,
-    "deflection": 0.30,
-    "insults": 0.34,
-    "obscure_formal": 0.25,
-    "recovery_phase": 0.25,
-    "non_abusive": 2.0,
-    "suicidal_threat": 0.45,
-    "physical_threat": 0.10,
-    "extreme_control": 0.36
+    "gaslighting": 0.25, "mockery": 0.15, "dismissiveness": 0.30, "control": 0.43, "guilt_tripping": 0.19,
+    "apology_baiting": 0.45, "blame_shifting": 0.23, "projection": 0.50, "contradictory_statements": 0.25,
+    "manipulation": 0.25, "deflection": 0.30, "insults": 0.34, "obscure_formal": 0.25, "recovery_phase": 0.25,
+    "non_abusive": 2.0, "suicidal_threat": 0.45, "physical_threat": 0.10, "extreme_control": 0.36
 }
 
 PATTERN_LABELS = LABELS[:15]
 DANGER_LABELS = LABELS[15:18]
 
 EXPLANATIONS = {
-    "gaslighting": "Gaslighting involves making someone question their own reality or perceptions, often causing them to feel confused or insecure.",
-    "blame_shifting": "Blame-shifting is when one person redirects the responsibility for an issue onto someone else, avoiding accountability.",
-    "projection": "Projection involves accusing the victim of behaviors or characteristics that the abuser themselves exhibit.",
-    "dismissiveness": "Dismissiveness is the act of belittling or disregarding another person's thoughts, feelings, or experiences.",
-    "mockery": "Mockery involves ridiculing or making fun of someone in a hurtful way, often with the intent to humiliate them.",
-    "recovery_phase": "Recovery phase refers to dismissing or invalidating someone’s process of emotional healing, or ignoring their need for support.",
-    "insults": "Insults are derogatory remarks aimed at degrading or humiliating someone, often targeting their personal traits or character.",
-    "apology_baiting": "Apology-baiting is when the abuser manipulates the victim into apologizing for something the abuser caused or did wrong.",
-    "deflection": "Deflection is the act of avoiding responsibility or shifting focus away from one's own actions, often to avoid accountability.",
-    "control": "Control tactics are behaviors that restrict or limit someone's autonomy, often involving domination, manipulation, or coercion.",
-    "extreme_control": "Extreme control involves excessive manipulation or domination over someone’s actions, decisions, or behaviors.",
-    "physical_threat": "Physical threats involve any indication or direct mention of harm to someone’s physical well-being, often used to intimidate or control.",
-    "suicidal_threat": "Suicidal threats are statements made to manipulate or control someone by making them feel responsible for the abuser’s well-being.",
-    "guilt_tripping": "Guilt-tripping involves making someone feel guilty or responsible for things they didn’t do, often to manipulate their behavior.",
-    "emotional_manipulation": "Emotional manipulation is using guilt, fear, or emotional dependency to control another person’s thoughts, feelings, or actions.",
-    "manipulation": "Manipulation refers to using deceptive tactics to control or influence someone’s emotions, decisions, or behavior to serve the manipulator’s own interests.",
-    "non_abusive": "Non-abusive language is communication that is respectful, empathetic, and free of harmful behaviors or manipulation.",
-    "obscure_formal": "Obscure or overly formal language used manipulatively to create confusion, avoid responsibility, or assert superiority."
+    "gaslighting": "Gaslighting involves making someone question their own reality or perceptions...",
+    "blame_shifting": "Blame-shifting is when one person redirects the responsibility...",
+    "projection": "Projection involves accusing the victim of behaviors the abuser exhibits.",
+    "dismissiveness": "Dismissiveness is belittling or disregarding another person’s feelings.",
+    "mockery": "Mockery ridicules someone in a hurtful, humiliating way.",
+    "recovery_phase": "Recovery phase dismisses someone's emotional healing process.",
+    "insults": "Insults are derogatory remarks aimed at degrading someone.",
+    "apology_baiting": "Apology-baiting manipulates victims into apologizing for abuser's behavior.",
+    "deflection": "Deflection avoids accountability by redirecting blame.",
+    "control": "Control restricts autonomy through manipulation or coercion.",
+    "extreme_control": "Extreme control dominates decisions and behaviors entirely.",
+    "physical_threat": "Physical threats signal risk of bodily harm.",
+    "suicidal_threat": "Suicidal threats manipulate others using self-harm threats.",
+    "guilt_tripping": "Guilt-tripping uses guilt to manipulate someone’s actions.",
+    "manipulation": "Manipulation deceives to influence or control outcomes.",
+    "non_abusive": "Non-abusive language is respectful and free of coercion.",
+    "obscure_formal": "Obscure/formal language manipulates through confusion or superiority."
 }
 
+def custom_sentiment(text):
+    inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = sentiment_model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    label_idx = torch.argmax(probs).item()
+    label = sentiment_model.config.id2label[label_idx]
+    score = probs[0][label_idx].item()
+    return {"label": label, "score": score}
 
 def calculate_abuse_level(scores, thresholds):
     triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
-    if not triggered_scores:
-        return 0.0
-    return round(np.mean(triggered_scores) * 100, 2)
-
+    return round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
 
 def interpret_abuse_level(score):
-    if score > 80:
-        return "Extreme / High Risk"
-    elif score > 60:
-        return "Severe / Harmful Pattern Present"
-    elif score > 40:
-        return "Likely Abuse"
-    elif score > 20:
-        return "Mild Concern"
-    else:
-        return "Very Low / Likely Safe"
-
+    if score > 80: return "Extreme / High Risk"
+    elif score > 60: return "Severe / Harmful Pattern Present"
+    elif score > 40: return "Likely Abuse"
+    elif score > 20: return "Mild Concern"
+    return "Very Low / Likely Safe"
 
 def analyze_messages(input_text, risk_flags):
     input_text = input_text.strip()
     if not input_text:
         return "Please enter a message for analysis."
 
-    sentiment = sentiment_analyzer(input_text)[0]
+    sentiment = custom_sentiment(input_text)
     sentiment_label = sentiment['label']
     sentiment_score = sentiment['score']
 
-    adjusted_thresholds = THRESHOLDS.copy()
-    if sentiment_label == "NEGATIVE":
-        adjusted_thresholds = {key: val * 0.8 for key, val in THRESHOLDS.items()}
+    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)
     with torch.no_grad():
@@ -110,8 +91,7 @@ def analyze_messages(input_text, risk_flags):
     danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18]))
 
     contextual_flags = risk_flags if risk_flags else []
-    contextual_risk_score = len(contextual_flags)
-    if contextual_risk_score >= 2:
+    if len(contextual_flags) >= 2:
         danger_flag_count += 1
 
     critical_flags = ["They've threatened harm", "They monitor/follow me", "I feel unsafe when alone with them"]
@@ -141,10 +121,8 @@ 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. "
-        "It flags communication patterns associated with increased risk of severe harm. "
-        "For more info, consider reaching out to support groups or professionals.\n\n"
-        f"Resources: {resources} \n\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}%)"
     )
 
@@ -155,22 +133,16 @@ def analyze_messages(input_text, risk_flags):
 
     return result
 
-
 iface = gr.Interface(
     fn=analyze_messages,
     inputs=[
         gr.Textbox(lines=10, placeholder="Enter message here..."),
         gr.CheckboxGroup(label="Do any of these apply to your situation?", choices=[
-            "They've threatened harm",
-            "They isolate me",
-            "I’ve changed my behavior out of fear",
-            "They monitor/follow me",
-            "I feel unsafe when alone with them"
+            "They've threatened harm", "They isolate me", "I’ve changed my behavior out of fear",
+            "They monitor/follow me", "I feel unsafe when alone with them"
         ])
     ],
-    outputs=[
-        gr.Textbox(label="Analysis Result"),
-    ],
+    outputs=[gr.Textbox(label="Analysis Result")],
     title="Abuse Pattern Detector"
 )