Update app.py

app.py

@@ -2,60 +2,85 @@
 import gradio as gr
 from transformers import pipeline
 
-# 2. Load the pre-trained AI model
-# We are using a model fine-tuned to recognize multiple emotions.
-# This is more nuanced than a simple positive/negative classifier.
+# 2. Load the NEW, more specialized AI model
 emotion_classifier = pipeline(
     "text-classification",
-    model="SamLowe/roberta-base-go_emotions",
-    top_k=None # This ensures we see the scores for all emotions
+    model="mental/mental-roberta-base", # <-- UPGRADED MODEL
+    top_k=None
 )
 
-# 3. Define the function that will process the input and return the output
-def predict_emotions(text_input):
+# 3. Define the function to process the input and return outputs
+def analyze_text(text_input):
     """
     This function takes text, passes it to the AI model,
-    and then formats the results for display.
+    and then formats the results for a better user experience.
     """
     # Get the raw predictions from the model
-    predictions = emotion_classifier(text_input)
+    predictions = emotion_classifier(text_input)[0]
 
-    # The model returns a list of lists. We only need the first element.
-    emotions = predictions[0]
+    # Create a dictionary to hold the scores for key indicators
+    key_indicators = {
+        'sadness': 0, 'anger': 0, 'fear': 0, 'joy': 0
+    }
+    for emotion in predictions:
+        if emotion['label'] in key_indicators:
+            key_indicators[emotion['label']] = round(emotion['score'], 3)
+    
+    # --- NEW: Interpretation Logic ---
+    # Find the dominant emotion among our key indicators
+    if not key_indicators:
+        dominant_emotion = "neutral"
+    else:
+        dominant_emotion = max(key_indicators, key=key_indicators.get)
 
-    # We will focus on key indicators for stress and depression
-    key_indicators = ['sadness', 'fear', 'anger', 'disappointment', 'nervousness']
+    interpretation_text = ""
+    resource_links = ""
 
-    # Create a dictionary to hold the scores for our key indicators
-    formatted_results = {}
-    for emotion in emotions:
-        if emotion['label'] in key_indicators:
-            formatted_results[emotion['label']] = round(emotion['score'], 3)
+    if key_indicators.get(dominant_emotion, 0) > 0.4: # Set a threshold
+        if dominant_emotion in ['sadness', 'fear', 'anger']:
+            interpretation_text = (
+                f"The analysis shows a strong presence of **{dominant_emotion}**. "
+                "These feelings can be challenging. Remember, it's okay to seek support."
+            )
+            # --- NEW: Provide helpful resources ---
+            resource_links = """
+            **Please consider reaching out to a professional:**
+            - **Vandrevala Foundation:** [vandrev ফাউন্ডেশন](https://www.vandrevalafoundation.com/) (India)
+            - **NIMHANS Centre for Well-Being:** [NIMHANS](http://www.nimhans.ac.in/well-being-centre/) (Bengaluru)
+            - **Find a Helpline:** [findahelpline.com](https://findahelpline.com/) (Global)
+            """
+        elif dominant_emotion == 'joy':
+            interpretation_text = (
+                f"The text shows strong indicators of **joy**. It's wonderful to see such positive expression."
+            )
 
-    return formatted_results
+    # Return the scores, the interpretation, and the resources
+    return key_indicators, interpretation_text, resource_links
 
-# 4. Create the Gradio web interface
+# 4. Create the Gradio web interface with new components
 app_interface = gr.Interface(
-    fn=predict_emotions,
+    fn=analyze_text,
     inputs=gr.Textbox(
         lines=8,
         label="Social Media Post",
-        placeholder="Type or paste a social media post here to analyze its emotional content..."
-    ),
-    outputs=gr.Label(
-        num_top_classes=5,
-        label="Key Emotional Indicators"
+        placeholder="Type or paste a social media post here..."
     ),
-    title="Student Wellness Analyzer 🧠",
+    # --- NEW: Multiple output components for a richer display ---
+    outputs=[
+        gr.Label(label="Key Emotional Indicators"),
+        gr.Markdown(label="Interpretation"),
+        gr.Markdown(label="Resources")
+    ],
+    title="Enhanced Student Wellness Analyzer 🧠✨",
     description="""
-    **Disclaimer:** This is an AI demo and **not a medical diagnostic tool**.
-    This model analyzes text for emotional indicators often associated with stress and depression.
+    This upgraded tool uses a specialized AI to analyze text for emotional indicators.
+    **Disclaimer:** This is **not a medical diagnostic tool**. It is an AI demonstration.
     If you are struggling, please seek help from a qualified professional.
     """,
     examples=[
         ["I'm so behind on all my assignments and the exams are next week. I don't know how I'm going to manage all this pressure."],
         ["Feeling completely isolated and lonely on campus. It seems like everyone else has their friend group figured out."],
-        ["I failed a midterm I studied really hard for. I just feel like a total disappointment and can't get motivated anymore."]
+        ["Finished my project and I'm so proud of how it turned out! The hard work really paid off."]
     ]
 )