#Praise Jesus!
import gradio as gr
import google.generativeai as genai
from transformers import pipeline
import docx2txt
import os


genai.configure(api_key="AIzaSyDBGF5y7WqDB0SO7-UO6yjshiEZN3Hpt3g")


# Whisper model for audio transcription
whisper_model = pipeline("automatic-speech-recognition", model="openai/whisper-large")

# Function to generate response using Google Gemini model
def get_gemini_response(input_text):
    # Ensure input is detailed enough
    if len(input_text.split()) < 10:
        return "Please provide a more detailed user story to help generate relevant needs and wants."

    # Define the prompt
    input_prompt = f"""
    Based on the user story "{input_text}", extract any unarticulated needs and wants.
    Only provide essential needs and wants directly relevant to the given story. Do not speculate or over-extrapolate.
    """
    
    # Generate the response
    response = genai.GenerativeModel('gemini-1.5-flash').generate_content(input_prompt)
    return response.text

# Main processing function for Gradio interface
def process_input(user_story=None, user_audio=None, user_file=None):
    # Process audio input if provided
    if user_audio:
        transcription = whisper_model(user_audio)["text"]
        user_story = transcription

    # Process file input if provided and if text is empty
    if user_file and not user_story:
        user_story = docx2txt.process(user_file)

    # Ensure there's text to process
    if not user_story:
        return "Please provide a user story, an audio file, or upload a Word file."

    # Generate response with Google Gemini
    gemini_response = get_gemini_response(user_story)

    return f"Gemini Output:\n{gemini_response}"

# Custom CSS styling for a colorful interface
custom_css = """
    .gradio-container {
        background-color: #f2f6ff;
        color: #333333;
        font-family: Arial, sans-serif;
    }
    .gr-button {
        background-color: #1e90ff;
        color: white;
        font-weight: bold;
        border-radius: 5px;
        padding: 8px 12px;
    }
    .gr-input, .gr-output {
        border-color: #1e90ff;
    }
    .gradio-block h1, .gradio-block h2 {
        color: #003366;
    }
"""

# Gradio interface with improved look and feel
interface = gr.Interface(
    fn=process_input,
    inputs=[
        gr.Textbox(label="User Story (Text Input)", placeholder="Enter your user story here...", lines=5),
        gr.Audio(type="filepath", label="User Story (Audio Input)"),
        gr.File(label="Upload Word File (.docx)")
    ],
    outputs="text",
    title="🌟 Multimodal Needs & Wants Extractor 🌟",
    description="**Author:** Victor Daniel\n\nEnter a detailed user story or upload an audio/Word file to extract the unarticulated needs and wants.",
    examples=[
        ["The user often speaks about wanting to improve their health but is hesitant to join a gym."]
    ],
    css=custom_css,
    theme="huggingface"
)

# Launch the Gradio app with custom CSS for styling
interface.launch()