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| import os | |
| from groq import Groq | |
| from dotenv import load_dotenv | |
| import streamlit as st | |
| # Load environment variables | |
| load_dotenv() | |
| # Get API key - handle both local development and Hugging Face Spaces | |
| try: | |
| # Try to get from Streamlit secrets (Hugging Face Spaces) | |
| GROQ_API_KEY = st.secrets["GROQ_API_KEY"] | |
| except: | |
| # Fallback to local .env file | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") | |
| if not GROQ_API_KEY: | |
| raise Exception("GROQ API Key not found. Please check your environment configuration.") | |
| # Initialize Groq client | |
| client = Groq(api_key=GROQ_API_KEY) | |
| # Different story starts for each genre | |
| GENRE_STARTS = { | |
| "Crime": "It's 11:00 pm on a winter night, you are standing next to a phone booth, you are witnessing a murder scene, and then the murderers see you standing behind the booth and watching the scene.", | |
| "Comedy": "You accidentally walk into a top-secret government meeting, but instead of being arrested, they mistake you for their new intern. Now, you must pretend to know what you're doing.", | |
| "Dark": "The last thing you remember is falling asleep in your bed, but now you wake up in an abandoned hospital with flickering lights and a note in your hand that says 'RUN'.", | |
| "Educational": "A renowned scientist just discovered a way to time travel, and you're the first volunteer to test the machine. However, something goes wrong, and you end up in the year 3024." | |
| } | |
| def generate_story(genre, story_history): | |
| try: | |
| start_story = GENRE_STARTS.get(genre, "A new adventure begins...") | |
| prompt = f""" | |
| Write a story continuation and provide 4 options that map to specific personality traits: | |
| Story start: {start_story} | |
| Format: | |
| [STORY] | |
| Write the story continuation here (max 150 words) | |
| [OPTIONS] | |
| 1. An option showing openness (creative, exploratory approach) | |
| 2. An option showing conscientiousness (methodical, careful approach) | |
| 3. An option showing extraversion and agreeableness (social, cooperative approach) | |
| 4. An option showing neuroticism (emotional, cautious approach) | |
| Important: Present the options naturally without mentioning these traits explicitly. | |
| Each option should be a natural story choice while subtly reflecting its trait. | |
| """ | |
| response = client.chat.completions.create( | |
| messages=[{"role": "user", "content": prompt}], | |
| model="meta-llama/llama-4-scout-17b-16e-instruct", | |
| temperature=0.7, | |
| max_tokens=800 | |
| ) | |
| content = response.choices[0].message.content | |
| # Split content into story and options | |
| parts = content.split("[OPTIONS]") | |
| if len(parts) == 2: | |
| story = parts[0].replace("[STORY]", "").strip() | |
| options_text = parts[1].strip() | |
| options = [line.strip() for line in options_text.split("\n") if line.strip().startswith(("1.", "2.", "3.", "4."))] | |
| else: | |
| story = content | |
| options = ["Continue the story", "Ask for help", "Take a different path", "Wait and observe"] | |
| story_history.append(story) | |
| return story, options | |
| except Exception as e: | |
| print(f"Error generating story: {e}") | |
| return "An error occurred while generating the story. Please try again.", ["Retry", "Choose different genre", "Start over", "Get help"] | |
| def continue_story(story_history, selected_option): | |
| try: | |
| full_story = "\n".join(story_history) | |
| prompt = f""" | |
| Previous story: {full_story} | |
| Chosen action: {selected_option} | |
| Continue the story based on the chosen action. | |
| Provide 4 new options that map to: | |
| - Option 1: Openness (innovative, creative solutions) | |
| - Option 2: Conscientiousness (careful planning, responsibility) | |
| - Option 3: Extraversion/Agreeableness (social interaction, cooperation) | |
| - Option 4: Neuroticism (emotional awareness, caution) | |
| Format: | |
| [STORY] | |
| Write the continuation here (max 150 words) | |
| [OPTIONS] | |
| Present 4 natural story options that subtly reflect these traits without explicitly mentioning them. | |
| Make each option feel like a natural choice in the story. | |
| """ | |
| response = client.chat.completions.create( | |
| model="meta-llama/llama-4-scout-17b-16e-instruct", | |
| messages=[{"role": "user", "content": prompt}], | |
| temperature=0.7, | |
| max_tokens=800 | |
| ) | |
| content = response.choices[0].message.content | |
| # Split content into story and options | |
| parts = content.split("[OPTIONS]") | |
| if len(parts) == 2: | |
| story = parts[0].replace("[STORY]", "").strip() | |
| options_text = parts[1].strip() | |
| options = [line.strip() for line in options_text.split("\n") if line.strip().startswith(("1.", "2.", "3.", "4."))] | |
| else: | |
| story = content | |
| options = ["Continue forward", "Take a different approach", "Reconsider the situation", "Try something new"] | |
| story_history.append(story) | |
| return story, options | |
| except Exception as e: | |
| print(f"Error continuing story: {e}") | |
| return "An error occurred while continuing the story. Please try again.", ["Retry", "Go back", "Choose different option", "Start over"] | |
| # def predict_personality(choices_text): | |
| # prompt = f""" | |
| # Analyze the following choices made by the user and determine their personality type: | |
| # Choices: | |
| # {choices_text} | |
| # The choices are labeled as follows: | |
| # 1. Emotional | |
| # 2. Rational | |
| # 3. Diplomatic | |
| # 4. Angry | |
| # Based on the frequency and pattern of choices, predict the user's personality type. | |
| # Give a short summary of their personality traits. | |
| # ### Give me only output | |
| # """ | |
| # response = client.chat.completions.create( | |
| # model="llama3-70b-8192", | |
| # messages=[{"role": "user", "content": prompt}], | |
| # temperature=0.3, | |
| # max_tokens=300 | |
| # ) | |
| # return response.choices[0].message.content | |