Update app.py

app.py

@@ -1,62 +1,62 @@
-import gradio as gr
-from transformers import pipeline
-
-MODEL_NAME = "cardiffnlp/twitter-roberta-base-emotion-multilabel-latest"
-
-classifier = pipeline("text-classification", model=MODEL_NAME, top_k=None)
-
-DISCLAIMER = """
-This demo detects patterns in self-provided text that may indicate support-seeking or distress-related language.
-It is for educational demonstration only and is NOT a diagnostic, disciplinary, or clinical tool.
-If someone may be in immediate danger, contact emergency services or a qualified crisis resource.
-"""
-
-DISTRESS_KEYWORDS = {
-    "sadness", "fear", "anger", "disgust", "negative"
-}
-
-def analyze_text(text):
-    if not text or not text.strip():
-        return "Please enter some text.", {}
-
-    results = classifier(text[:512])[0]
-
-    label_scores = {item["label"]: float(item["score"]) for item in results}
-
-    distress_score = 0.0
-    for label, score in label_scores.items():
-        if label.lower() in DISTRESS_KEYWORDS:
-            distress_score += score
-
-    if distress_score >= 0.60:
-        assessment = "Potential support-seeking / distress-related language detected"
-    elif distress_score >= 0.30:
-        assessment = "Some distress-related language detected"
-    else:
-        assessment = "No strong distress-related language detected"
-
-    return assessment, label_scores
-
-demo = gr.Interface(
-    fn=analyze_text,
-    inputs=gr.Textbox(
-        lines=6,
-        label="Student text input",
-        placeholder="Enter a message, reflection, or post for analysis..."
-    ),
-    outputs=[
-        gr.Textbox(label="Assessment"),
-        gr.Label(label="Model scores")
-    ],
-    title="Student Support-Seeking Language Demo",
-    description=DISCLAIMER,
-    examples=[
-        ["I feel overwhelmed and I do not know who to talk to anymore."],
-        ["I am stressed about finals but I think I can manage."],
-        ["I had a good day and finished my homework."],
-    ],
-    flagging_mode="never"
-)
-
-if __name__ == "__main__":
+import gradio as gr
+from transformers import pipeline
+
+MODEL_NAME = "cardiffnlp/twitter-roberta-base-emotion-multilabel-latest"
+
+classifier = pipeline("text-classification", model=MODEL_NAME, top_k=None)
+
+DISCLAIMER = """
+This demo detects patterns in self-provided text that may indicate support-seeking or distress-related language.
+It is for educational demonstration only and is NOT a diagnostic, disciplinary, or clinical tool.
+If someone may be in immediate danger, contact emergency services or a qualified crisis resource.
+"""
+
+DISTRESS_KEYWORDS = {
+    "sadness", "fear", "anger", "disgust", "negative"
+}
+
+def analyze_text(text):
+    if not text or not text.strip():
+        return "Please enter some text.", {}
+
+    results = classifier(text[:512])[0]
+
+    label_scores = {item["label"]: float(item["score"]) for item in results}
+
+    distress_score = 0.0
+    for label, score in label_scores.items():
+        if label.lower() in DISTRESS_KEYWORDS:
+            distress_score += score
+
+    if distress_score >= 0.60:
+        assessment = "Potential support-seeking / distress-related language detected"
+    elif distress_score >= 0.30:
+        assessment = "Some distress-related language detected"
+    else:
+        assessment = "No strong distress-related language detected"
+
+    return assessment, label_scores
+
+demo = gr.Interface(
+    fn=analyze_text,
+    inputs=gr.Textbox(
+        lines=6,
+        label="Student text input",
+        placeholder="Enter a message, reflection, or post for analysis..."
+    ),
+    outputs=[
+        gr.Textbox(label="Assessment"),
+        gr.Label(label="Model scores")
+    ],
+    title="Student Support-Seeking Language Demo",
+    description=DISCLAIMER,
+    examples=[
+        ["I feel overwhelmed and I do not know who to talk to anymore."],
+        ["I am stressed about finals but I think I can manage."],
+        ["I had a good day and finished my homework."],
+    ],
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
     demo.launch()