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app.py

@@ -1,73 +1,51 @@
-# import gradio as gr
-# from transformers import pipeline
-
-# # Load your model from Hugging Face
-# classifier = pipeline("text-classification", model="coldnasser/depression-anxiety-mindscape")
-
-# # Define the prediction function
-# def predict(text):
-#     return classifier(text)
-
-# # Create the Gradio interface
-# gr.Interface(
-#     fn=predict,
-#     inputs="text",
-#     outputs="label",
-#     title="Mindscape AI Therapist"
-# ).launch()
-
-# import gradio as gr
-# from transformers import pipeline
-
-# classifier = pipeline("text-classification", model="coldnasser/depression-anxiety-mindscape")
-
-# def predict(text):
-#     try:
-#         return classifier(text)
-#     except Exception as e:
-#         return f"Error: {str(e)}"
-
-# gr.Interface(fn=predict, inputs="text", outputs="text", title="Mindscape").launch()
 import gradio as gr
 from transformers import pipeline
-import re
-# def clean_text(text):
-#     # Remove mentions (@username)
-#     text = re.sub(r'@\w+', '', text)
-    
-#     # Remove URLs
-#     text = re.sub(r'http\S+|www\S+', '', text)
-    
-    
-#     # Remove special characters, numbers, and extra spaces
-#     text = re.sub(r'[^a-zA-Z\s]', '', text)  # Keep only letters and spaces
-#     text = re.sub(r'\s+', ' ', text).strip()  # Remove extra spaces
+from collections import defaultdict
 
-#     return text
-
-
-# Define the mapping from label to human-readable status
+# Label mapping
 label_mapping = {
     "LABEL_0": "Normal",
     "LABEL_1": "Depression",
     "LABEL_2": "Anxiety"
 }
 
+# Load classifier
 classifier = pipeline("text-classification", model="coldnasser/depression-anxiety-mindscape")
 
-def predict(text):
+def predict(texts):
     try:
-        # text=clean_text(text=text)
-        result = classifier(text)[0]  # Get the first result
-        label = result['label']
-        score = result['score']
+        if isinstance(texts, str):
+            texts = [texts]
+        
+        results = classifier(texts)
 
-        # Map the label to a human-readable value
-        human_label = label_mapping.get(label, label)
+        # Initialize score aggregator
+        score_sums = defaultdict(float)
+        count = len(texts)
 
-        return {"label": human_label, "score": score}
-    except Exception as e:
-        return f"Error: {str(e)}"
+        for res in results:
+            label = res['label']
+            score = res['score']
+            score_sums[label] += score
 
-gr.Interface(fn=predict, inputs="text", outputs="json", title="Mindscape").launch()
+        # 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.inputs.Textbox(lines=10, placeholder="Enter one or more texts (one per line)", label="Input Texts"),
+    outputs="json",
+    title="Mindscape AI Therapist (Multi-text Support)"
+).launch()