import os
import requests
import streamlit as st
from dotenv import load_dotenv

# Load environment variables from the .env file
load_dotenv()

# Get the Gemini API key from the .env file
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")

if GEMINI_API_KEY is None:
    st.error("API key not found! Please set the GEMINI_API_KEY in your .env file.")
    st.stop()

# Define the 3 questions for mood analysis
questions = [
    "How are you feeling today in one word?",
    "What's currently on your mind?",
    "Do you feel calm or overwhelmed right now?",
]

# Function to query the Gemini API
def query_gemini_api(user_answers):
    url = f'https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent?key={GEMINI_API_KEY}'
    headers = {'Content-Type': 'application/json'}
    
    # Prepare the payload with user answers
    input_text = " ".join(user_answers)  # Combining all answers into one input text
    
    payload = {
        "contents": [
            {
                "parts": [
                    {"text": input_text}
                ]
            }
        ]
    }

    try:
        # Send the request to the Gemini API
        response = requests.post(url, headers=headers, json=payload)
        
        # Log the response for debugging
        print(f"Status Code: {response.status_code}")  # Log the status code
        print(f"Response Text: {response.text}")  # Log the response text
        
        # Check if the API call is successful
        if response.status_code == 200:
            result = response.json()

            # Check if the response contains valid mood and recommendations
            mood = result.get("mood", None)
            recommendations = result.get("recommendations", None)

            if mood and recommendations:
                return mood, recommendations
            else:
                st.error("No mood or recommendations found in the response.")
                return None, None
        else:
            st.error(f"API Error {response.status_code}: {response.text}")
            return None, None
    except requests.exceptions.RequestException as e:
        st.error(f"An error occurred: {e}")
        return None, None

# Streamlit app for collecting answers
def main():
    st.title("Mood Analysis and Suggestions")

    st.write("Answer the following 3 questions to help us understand your mood:")

    # Collect responses from the user
    responses = []
    for i, question in enumerate(questions):
        response = st.text_input(f"{i+1}. {question}")
        if response:
            responses.append(response)

    # If all 3 responses are collected, send them to Gemini for analysis
    if len(responses) == len(questions):
        st.write("Processing your answers...")

        # Get mood and recommendations from Gemini API
        mood, recommendations = query_gemini_api(responses)

        if mood and recommendations:
            # Display the detected mood
            st.write(f"Detected Mood: {mood}")

            # Display the recommendations
            st.write("### Recommendations to Improve Your Mood:")
            for recommendation in recommendations:
                st.write(f"- {recommendation}")
        else:
            # If no valid mood or recommendations are found, show a message
            st.warning("Could not generate mood analysis. Please try again later.")
    else:
        st.write("Please answer all 3 questions to receive suggestions.")

if __name__ == "__main__":
    main()