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| import gradio as gr | |
| from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer | |
| from sentence_transformers import SentenceTransformer | |
| from gtts import gTTS | |
| import os | |
| # Load models | |
| def load_model(): | |
| try: | |
| # Sentiment Analysis Pipeline | |
| sentiment_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english") | |
| sentiment_tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english") | |
| sentiment_analyzer = pipeline("sentiment-analysis", model=sentiment_model, tokenizer=sentiment_tokenizer) | |
| # Emotion Detection | |
| emotion_model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base") | |
| emotion_tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base") | |
| emotion_analyzer = pipeline("sentiment-analysis", model=emotion_model, tokenizer=emotion_tokenizer) | |
| # Sentence Transformers for Book Insights | |
| book_model = SentenceTransformer('all-MiniLM-L6-v2') | |
| return sentiment_analyzer, emotion_analyzer, book_model | |
| except Exception as e: | |
| print(f"Error loading models: {str(e)}") | |
| return None, None, None | |
| # Text-to-Speech Function | |
| def text_to_speech(text): | |
| try: | |
| tts = gTTS(text, lang='en') | |
| audio_file = "response.mp3" | |
| tts.save(audio_file) | |
| return audio_file | |
| except Exception as e: | |
| print(f"Error in text-to-speech conversion: {str(e)}") | |
| return None | |
| # Response Generation Logic with empathetic responses | |
| def virtual_psychologist_assistant(user_input, sentiment_analyzer, emotion_analyzer, book_model): | |
| try: | |
| # Sentiment Analysis | |
| sentiment = sentiment_analyzer(user_input)[0]['label'] | |
| emotion = emotion_analyzer(user_input)[0]['label'] | |
| # Predefined responses for emotions | |
| response_templates = { | |
| "POSITIVE": "I’m glad to hear you're feeling well. Could you share more about what's bringing you joy?", | |
| "NEGATIVE": "I'm here for you. It sounds like things might be tough right now. Want to tell me more?", | |
| "NEUTRAL": "I'm here to listen. Feel free to share whatever's on your mind." | |
| } | |
| # Choose response based on sentiment | |
| response = response_templates.get(sentiment, "Thank you for sharing. Can you tell me more?") | |
| # Book Insights for added context | |
| book_quotes = { | |
| "Man’s Search for Meaning": [ | |
| "The meaning of life is to give life meaning.", | |
| "Those who have a why to live, can bear almost any how." | |
| ] | |
| } | |
| quote = book_quotes["Man’s Search for Meaning"][0] if sentiment == "POSITIVE" else book_quotes["Man’s Search for Meaning"][1] | |
| # Combine final response | |
| final_response = f"Emotion Detected: {emotion}\nSentiment: {sentiment}\n\nPsychologist’s Insight: {quote} - Viktor E. Frankl\n\nResponse: {response}" | |
| # Convert to Audio | |
| audio_file = text_to_speech(final_response) | |
| return final_response, audio_file | |
| except Exception as e: | |
| print(f"Error during response generation: {str(e)}") | |
| return f"An error occurred: {str(e)}", None | |
| # Gradio Interface | |
| def gradio_interface(user_input): | |
| sentiment_analyzer, emotion_analyzer, book_model = load_model() | |
| if sentiment_analyzer is None or emotion_analyzer is None or book_model is None: | |
| return "Error loading models. Please try again later.", None | |
| response, audio_file = virtual_psychologist_assistant(user_input, sentiment_analyzer, emotion_analyzer, book_model) | |
| return response, audio_file | |
| # Create Gradio interface | |
| iface = gr.Interface(fn=gradio_interface, | |
| inputs="text", | |
| outputs=["text", "audio"], | |
| title="Virtual Psychologist Assistant", | |
| description="A virtual psychologist that analyzes emotions and provides meaningful insights.", | |
| live=False) # Set live=False for submit button | |
| iface.launch() | |