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| import streamlit as st | |
| from transformers import pipeline | |
| import google.generativeai as genai | |
| import json | |
| import random | |
| # Load language configurations from JSON | |
| with open('languages_config.json', 'r', encoding='utf-8') as f: | |
| LANGUAGES = json.load(f)['LANGUAGES'] | |
| # Load the JSON data for emotion templates | |
| with open('emotion_templates.json', 'r') as f: | |
| data = json.load(f) | |
| # Configure Gemini (replace with your API key) | |
| genai.configure(api_key="AIzaSyCYRYNwCU1f9cgJYn8pd86Xcf6hiSMwJr0") | |
| model = genai.GenerativeModel('gemini-2.0-flash') | |
| def generate_text(prompt, context=""): | |
| """ | |
| Generates text using the Gemini model. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| return response.text | |
| except Exception as e: | |
| print(f"Error generating text: {e}") | |
| return "I am sorry, I encountered an error while generating the text." | |
| def create_prompt(emotion, topic=None): | |
| """ | |
| Chooses a random prompt from the template list. | |
| """ | |
| templates = data["emotion_templates"][emotion] | |
| prompt = random.choice(templates) | |
| if topic: | |
| # Replace various placeholders in the prompt | |
| placeholders = ["[topic/person]", "[topic]", "[person]", "[object]", "[outcome]"] | |
| for placeholder in placeholders: | |
| prompt = prompt.replace(placeholder, topic) | |
| subfix_prompt = "Make the generated text in the same language as the topic.\n" | |
| subfix_prompt += "Make the generated text short.\n" | |
| prefix_prompt = "## topic\n" + topic | |
| prompt = subfix_prompt + prompt + prefix_prompt | |
| return prompt | |
| # 1. Emotion Detection Model (Using Hugging Face's transformer) | |
| emotion_classifier = pipeline("text-classification", model="AnasAlokla/multilingual_go_emotions") | |
| # 2. Conversational Agent Logic | |
| def get_ai_response(user_input, emotion_predictions): | |
| """Generates AI response based on user input and detected emotions.""" | |
| dominant_emotion = None | |
| max_score = 0 | |
| responses = None | |
| for prediction in emotion_predictions: | |
| if prediction['score'] > max_score: | |
| max_score = prediction['score'] | |
| dominant_emotion = prediction['label'] | |
| prompt_text = create_prompt(dominant_emotion, user_input) | |
| responses = generate_text(prompt_text) | |
| # Handle cases where no specific emotion is clear | |
| if dominant_emotion is None: | |
| return "Error for response" | |
| else: | |
| return responses | |
| # 3. Streamlit Frontend | |
| def main(): | |
| # Language Selection | |
| selected_language = st.sidebar.selectbox( | |
| "Select Interface Language", | |
| list(LANGUAGES.keys()), | |
| index=0 # Default to English | |
| ) | |
| # Display Image | |
| st.image('chatBot_image.jpg', channels='RGB') | |
| # Set page title and header based on selected language | |
| st.title(LANGUAGES[selected_language]['title']) | |
| # Input Text Box | |
| user_input = st.text_input( | |
| LANGUAGES[selected_language]['input_placeholder'], | |
| "" | |
| ) | |
| if user_input: | |
| # Emotion Detection | |
| emotion_predictions = emotion_classifier(user_input) | |
| # Display Emotions | |
| st.subheader(LANGUAGES[selected_language]['emotions_header']) | |
| for prediction in emotion_predictions: | |
| st.write(f"- {prediction['label']}: {prediction['score']:.2f}") | |
| # Get AI Response | |
| ai_response = get_ai_response(user_input, emotion_predictions) | |
| # Display AI Response | |
| st.subheader(LANGUAGES[selected_language]['response_header']) | |
| st.write(ai_response) | |
| # Run the main function | |
| if __name__ == "__main__": | |
| main() |