new model

app.py

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+import gradio as gr
+from transformers import pipeline
+from collections import defaultdict
+
+# Label mapping
+label_mapping = {
+    "LABEL_0": "Normal",
+    "LABEL_1": "Depression",
+    "LABEL_2": "Anxiety"
+}
+
+# Load classifier
+classifier = pipeline("text-classification", model="username/mindscape-v2")
+
+def predict(texts):
+    try:
+        if isinstance(texts, str):
+            texts = [texts]
+
+        results = classifier(texts)
+
+        # Initialize score aggregator
+        score_sums = defaultdict(float)
+        count = len(texts)
+
+        for res in results:
+            label = res['label']
+            score = res['score']
+            score_sums[label] += score
+
+        # Calculate average scores
+        avg_scores = {label_mapping.get(label, label): score_sums[label] / count for label in score_sums}
+
+        # Get final predicted label (highest average)
+        final_label = max(avg_scores.items(), key=lambda x: x[1])[0]
+
+        return {
+            "Predicted Status": final_label,
+            "Average Scores": avg_scores
+        }
+
+    except Exception as e:
+        return {"Error": str(e)}
+
+# Gradio interface
+gr.Interface(
+     fn=predict,
+     inputs=gr.Textbox(
+         lines=10,
+         placeholder="Enter one or more texts (one per line)",
+         label="Input Texts"
+     ),
+     outputs=gr.JSON(
+         label="Predicted Status & Scores"
+     ),
+     title="Mindscape AI Therapist (Multi-text Support)"
+).launch()

requirements.txt

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+transformers
+gradio
+torch