import os
import google.generativeai as genai
import gradio as gr
from google.generativeai.types import HarmBlockThreshold, HarmCategory

# Configure Google API Key and model
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
genai.configure(api_key=GOOGLE_API_KEY)
MODEL_ID = "gemini-1.5-pro-002"
model = genai.GenerativeModel(MODEL_ID)

example_model = genai.GenerativeModel(
    MODEL_ID,
    system_instruction=[
        "You are an advocate against gender-based violence.",
        "Analyze the content for signs of gender discrimination and provide actionable advice."
    ],
)

# Set model parameters
generation_config = genai.GenerationConfig(
    temperature=0.9,
    top_p=1.0,
    top_k=32,
    candidate_count=1,
    max_output_tokens=8192,
)

# Safety and instruction settings
safety_settings = {
    HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
    HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
}

# Function to analyze text with error handling
def analyze_text(text):
    prompt = f"Analyze this text for any instances of gender-based discrimination and provide tips: {text}"
    contents = [prompt]
    response = example_model.generate_content(
        contents,
        generation_config=generation_config,
        safety_settings=safety_settings,
    )
    return response.text if response else "No response generated."

# Custom CSS for branding and animation
css = """
body { font-family: 'Arial', sans-serif; background-color: #f0f0f0; }
h1 { color: #333; animation: fadeIn 2s; }
@keyframes fadeIn {
    from { opacity: 0; }
    to { opacity: 1; }
}
#logo { font-size: 48px; color: #5f4b8b; animation: slideIn 2s; }
@keyframes slideIn {
    0% { margin-left: -200px; }
    100% { margin-left: 0px; }
}
.button { background-color: #4CAF50; color: white; padding: 14px 20px; margin: 8px 0; border: none; cursor: pointer; width: 100%; }
.button:hover { opacity: 0.8; }
.textbox { width: 100%; padding: 12px 20px; margin: 8px 0; display: inline-block; border: 1px solid #ccc; box-sizing: border-box; }
"""


# Example scenarios for gender discrimination analysis
example_scenarios = [
    "During a team meeting, whenever a female colleague tried to express her opinion, she was often interrupted or talked over by male colleagues.",
    "The feedback given to female employees often focuses more on their demeanor and less on their actual accomplishments.",
    "Male employees are more frequently considered for promotions and challenging projects, even when female employees have similar or superior qualifications.",
    "During a hiring panel, female candidates were often asked about their personal life, family plans, and how they would balance home and work.",
    "There are significant wage discrepancies between male and female employees who hold the same position and possess comparable experience.",
    "Some male colleagues often make inappropriate jokes or comments about female employees' appearances and attire."
]

# Gradio interface setup
with gr.Blocks(css=css) as app:
    gr.Markdown("<div id='logo'>J<span>ustEva</span></div>")
    gr.Markdown("<h1 style='text-align: center; color: #5f4b8b;'>Gender Bias Analysis Platform</h1>")
    gr.Markdown("<p style='text-align: center; font-size: 16px; color: #4a4a4a;'>Powered by AI to advocate against gender-based violence</p>")
    with gr.Tab("Text Analysis"):
        text_input = gr.Textbox(label="Enter Text or Select an Example", placeholder="Type here or select an example...", lines=4)
        analyze_text_btn = gr.Button("Analyze Text")
        text_output = gr.Textbox(label="Analysis Output", lines=6)
        
        examples = gr.Examples(
            examples=example_scenarios,
            inputs=text_input,
            outputs=text_output
        )
        
        analyze_text_btn.click(
            fn=analyze_text,
            inputs=text_input,
            outputs=text_output
        )

app.launch()