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| !pip install transformers streamlit | |
| !pip install torch # for PyTorch | |
| import streamlit as st | |
| from transformers import pipeline | |
| # 1. Emotion Detection Model (Using Hugging Face's transformer) | |
| # Choose a suitable model - 'emotion-classification' is the task, you can specify a model from Hugging Face Model Hub. | |
| emotion_classifier = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions") # Or choose another model | |
| # 2. Conversational Agent Logic | |
| def get_ai_response(user_input, emotion_predictions): | |
| """Generates AI response based on user input and detected emotions.""" | |
| # Basic response generation based on detected emotions | |
| responses = { | |
| "anger": "I understand you're feeling angry. Let's take a deep breath and try to resolve this.", | |
| "sadness": "I'm sorry to hear you're feeling sad. Is there anything I can do to help?", | |
| "joy": "That's wonderful! I'm so happy for you!", | |
| "surprise": "Wow, that's surprising! Tell me more.", | |
| "fear": "I understand you're afraid. How can I help?", | |
| "neutral": "Understood.", # or a more neutral response | |
| "default": "I am not able to understand the emotion, please try again" | |
| } | |
| dominant_emotion = None | |
| max_score = 0 | |
| for prediction in emotion_predictions: | |
| if prediction['score'] > max_score: | |
| max_score = prediction['score'] | |
| dominant_emotion = prediction['label'] | |
| # Handle cases where no specific emotion is clear | |
| if dominant_emotion is None: | |
| return responses["default"] # or use default message if no emotion is detected. | |
| elif dominant_emotion in responses: | |
| return responses[dominant_emotion] | |
| else: | |
| return "I'm detecting some emotion, but I'm not sure how to respond." #Handle unexpected emotion labels. | |
| # 3. Streamlit Frontend | |
| st.title("Emotionally Aware Chatbot") | |
| # Input Text Box | |
| user_input = st.text_input("Enter your message:", "") | |
| if user_input: | |
| # Emotion Detection | |
| emotion_predictions = emotion_classifier(user_input) | |
| # Display Emotions | |
| st.subheader("Detected Emotions:") | |
| for prediction in emotion_predictions: | |
| st.write(f"- {prediction['label']}: {prediction['score']:.2f}") # Show emotion score. | |
| # Get AI Response | |
| ai_response = get_ai_response(user_input, emotion_predictions) | |
| # Display AI Response | |
| st.subheader("AI Response:") | |
| st.write(ai_response) | |