Create app.py

app.py

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+ import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+import tempfile
+
+# Load your updated model and tokenizer from Hugging Face
+model_name = "SamanthaStorm/abuse-pattern-detector-v2"
+model = AutoModelForSequenceClassification.from_pretrained(model_name, force_download=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name, force_download=True)
+
+# Our model outputs 17 labels:
+# - First 14 are abuse pattern categories
+# - Last 3 are Danger Assessment cues
+TOTAL_LABELS = 17
+
+def analyze_messages(text):
+    input_text = text.strip()
+    if not input_text:
+        return "Please enter a message for analysis.", None
+    
+    # Tokenize input text
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Assume model logits shape is [17] (for a single example)
+    logits = outputs.logits.squeeze()  # shape: [17]
+    scores = torch.sigmoid(logits).numpy()
+    
+    # For the first 14 labels (abuse patterns), count how many exceed threshold 0.5
+    abuse_pattern_scores = scores[:14]
+    concerning_pattern_count = int(np.sum(abuse_pattern_scores > 0.5))
+    
+    # For the last 3 labels (Danger Assessment cues), count how many exceed threshold 0.5
+    danger_scores = scores[14:17]
+    danger_flag_count = int(np.sum(danger_scores > 0.5))
+    
+    # Map danger flag count to Danger Assessment Score
+    if danger_flag_count >= 2:
+        danger_assessment = "High"
+    elif danger_flag_count == 1:
+        danger_assessment = "Moderate"
+    else:
+        danger_assessment = "Low"
+    
+    # Customize resource links based on Danger Assessment Score (with additional niche support)
+    if danger_assessment == "High":
+        resources = (
+            "**Immediate Help:** If you are in immediate danger, please call 911.\n\n"
+            "**Crisis Support:** National DV Hotline – Safety Planning: [thehotline.org/plan-for-safety](https://www.thehotline.org/plan-for-safety/)\n"
+            "**Legal Assistance:** WomensLaw – Legal Help for Survivors: [womenslaw.org](https://www.womenslaw.org/)\n"
+            "**Specialized Support:** For LGBTQ+, immigrants, and neurodivergent survivors, please consult local specialized services or visit RAINN: [rainn.org](https://www.rainn.org/)"
+        )
+    elif danger_assessment == "Moderate":
+        resources = (
+            "**Safety Planning:** The Hotline – What Is Emotional Abuse?: [thehotline.org/resources](https://www.thehotline.org/resources/what-is-emotional-abuse/)\n"
+            "**Relationship Health:** One Love Foundation – Digital Relationship Health: [joinonelove.org](https://www.joinonelove.org/)\n"
+            "**Support Chat:** National Domestic Violence Hotline Chat: [thehotline.org](https://www.thehotline.org/)\n"
+            "**Specialized Groups:** Look for support groups tailored for LGBTQ+, immigrant, and neurodivergent communities."
+        )
+    else:  # Low risk
+        resources = (
+            "**Educational Resources:** Love Is Respect – Healthy Relationships: [loveisrespect.org](https://www.loveisrespect.org/)\n"
+            "**Therapy Finder:** Psychology Today – Find a Therapist: [psychologytoday.com](https://www.psychologytoday.com/us/therapists)\n"
+            "**Relationship Tools:** Relate – Relationship Health Tools: [relate.org.uk](https://www.relate.org.uk/)\n"
+            "**Community Support:** Consider community-based and online support groups, especially those focused on LGBTQ+, immigrant, and neurodivergent survivors."
+        )
+    
+    # Prepare the output result with both scores
+    result_md = (
+        f"**Abuse Pattern Count:** {concerning_pattern_count}\n\n"
+        f"**Danger Assessment Score:** {danger_assessment}\n\n"
+        f"**Support Resources:**\n{resources}"
+    )
+    
+    # Save the result to a temporary text file for download
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode="w") as f:
+        f.write(result_md)
+        report_path = f.name
+    
+    return result_md, report_path
+
+# Build the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Abuse Pattern Detector - Risk Analysis")
+    gr.Markdown("Enter one or more messages (separated by newlines) for analysis.")
+    
+    text_input = gr.Textbox(label="Input Messages", lines=10, placeholder="Type your message(s) here...")
+    result_output = gr.Markdown(label="Analysis Result")
+    download_output = gr.File(label="Download Report (.txt)")
+    
+    text_input.submit(analyze_messages, inputs=text_input, outputs=[result_output, download_output])
+    analyze_btn = gr.Button("Analyze")
+    analyze_btn.click(analyze_messages, inputs=text_input, outputs=[result_output, download_output])
+    
+if __name__ == "__main__":
+    demo.launch()
+