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| import streamlit as st | |
| import requests | |
| import os | |
| # Access the Hugging Face token from the environment variable | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| if not HF_TOKEN: | |
| st.error("Hugging Face token not found. Please check your Secrets configuration.") | |
| st.stop() | |
| # Hugging Face Inference API endpoints | |
| SENTIMENT_API_URL = "https://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english" | |
| TEXT_GENERATION_API_URL = "https://api-inference.huggingface.co/models/gpt2" | |
| HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| # Tea recommendations based on mood | |
| tea_recommendations = { | |
| "POSITIVE": { | |
| "description": "Perfect for celebrating your joy!", | |
| "teas": ["Fruit Infusion Tea", "Hibiscus Tea", "Earl Grey Tea"], | |
| "health_benefits": "Boosts mood, rich in vitamins, and supports heart health.", | |
| "recipe": "Steep for 6 minutes in hot water (95°C). Add sugar or fruit slices for extra flavor." | |
| }, | |
| "NEGATIVE": { | |
| "description": "Calm your mind and reduce stress.", | |
| "teas": ["Peppermint Tea", "Lemon Balm Tea", "Rooibos Tea"], | |
| "health_benefits": "Relieves anxiety, soothes digestion, and reduces tension.", | |
| "recipe": "Steep for 4 minutes in boiling water. Add lemon for a refreshing twist." | |
| } | |
| } | |
| # Function to detect mood using Hugging Face Inference API | |
| def detect_mood(user_input): | |
| try: | |
| payload = {"inputs": user_input} | |
| response = requests.post(SENTIMENT_API_URL, headers=HEADERS, json=payload) | |
| if response.status_code == 200: | |
| result = response.json() | |
| if isinstance(result, list) and len(result) > 0: | |
| return result[0][0]["label"] # Returns "POSITIVE" or "NEGATIVE" | |
| return "POSITIVE" # Default to positive if API fails | |
| except Exception as e: | |
| st.error(f"Error detecting mood: {e}") | |
| return "POSITIVE" # Fallback to positive mood | |
| # Function to generate personalized tea recommendations using Hugging Face Inference API | |
| def personalized_recommendation(user_input): | |
| try: | |
| prompt = f"Based on the following preferences: '{user_input}', suggest a type of tea and explain why it's a good fit." | |
| payload = {"inputs": prompt, "max_length": 100, "num_return_sequences": 1} | |
| response = requests.post(TEXT_GENERATION_API_URL, headers=HEADERS, json=payload) | |
| if response.status_code == 200: | |
| result = response.json() | |
| if isinstance(result, list) and len(result) > 0: | |
| return result[0]["generated_text"] | |
| return "I recommend trying Chamomile Tea for its calming properties." # Default recommendation if API fails | |
| except Exception as e: | |
| st.error(f"Error generating recommendation: {e}") | |
| return "I recommend trying Chamomile Tea for its calming properties." # Fallback recommendation | |
| # Streamlit app | |
| def main(): | |
| st.title("🍵 Tea Time Logic") | |
| st.write("Welcome to your AI-powered tea recommendation app! Describe how you're feeling, and we'll suggest the perfect tea for you.") | |
| # User input for mood detection | |
| user_input = st.text_input("Describe how you're feeling today:") | |
| if user_input: | |
| mood = detect_mood(user_input) | |
| st.write(f"**Detected Mood:** {mood}") | |
| st.subheader(f"Tea Recommendations for Feeling {mood}:") | |
| st.write(tea_recommendations[mood]["description"]) | |
| st.write("**Recommended Teas:**") | |
| for tea in tea_recommendations[mood]["teas"]: | |
| st.write(f"- {tea}") | |
| st.write("**Health Benefits:**") | |
| st.write(tea_recommendations[mood]["health_benefits"]) | |
| st.write("**Brewing Recipe:**") | |
| st.write(tea_recommendations[mood]["recipe"]) | |
| # Personalized recommendations | |
| st.subheader("Personalized Tea Recommendations") | |
| preference_input = st.text_input("Describe your preferences (e.g., 'I like sweet and floral teas'):") | |
| if preference_input: | |
| recommendation = personalized_recommendation(preference_input) | |
| st.write("**Your Personalized Recommendation:**") | |
| st.write(recommendation) | |
| # Footer | |
| st.write("---") | |
| st.write("Enjoy your tea and have a wonderful day! 😊") | |
| if __name__ == "__main__": | |
| main() |