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| # 1. Import necessary libraries | |
| import gradio as gr | |
| from transformers import pipeline | |
| # 2. Load the NEW, more specialized AI model | |
| emotion_classifier = pipeline( | |
| "text-classification", | |
| model="mental/mental-roberta-base", # <-- UPGRADED MODEL | |
| top_k=None | |
| ) | |
| # 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 a better user experience. | |
| """ | |
| # Get the raw predictions from the model | |
| predictions = emotion_classifier(text_input)[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) | |
| interpretation_text = "" | |
| resource_links = "" | |
| 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 the scores, the interpretation, and the resources | |
| return key_indicators, interpretation_text, resource_links | |
| # 4. Create the Gradio web interface with new components | |
| app_interface = gr.Interface( | |
| fn=analyze_text, | |
| inputs=gr.Textbox( | |
| lines=8, | |
| label="Social Media Post", | |
| placeholder="Type or paste a social media post here..." | |
| ), | |
| # --- 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=""" | |
| 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."], | |
| ["Finished my project and I'm so proud of how it turned out! The hard work really paid off."] | |
| ] | |
| ) | |
| # 5. Launch the app | |
| app_interface.launch() |