app.py

import os
from PIL import Image
import google.generativeai as genai
import gradio as gr
from gtts import gTTS
from pydub import AudioSegment
import tempfile

# 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-latest"
model = genai.GenerativeModel(MODEL_ID)

# System prompts
analysis_system_prompt = "You are an expert in gender studies. Analyze the following content for any signs of gender-based discrimination and suggest actionable advice."
podcast_prompt = """You are Eva, a solo podcast host focusing on gender equality topics.
- Discuss real-life scenarios involving gender-based discrimination, provide insights, and offer solutions in a conversational, storytelling style.
- Based on the analyzed text, create an engaging solo podcast as if reading stories from different victims who send you their story.
- Introduce yourself as Eva.
- Keep the conversation within 30000 characters, with a lot of emotion.
- Use short sentences suitable for speech synthesis.
- Maintain an empathetic tone.
- Include filler words like 'äh' for a natural flow.
- Avoid background music or extra words.
"""

# Model generation configuration
generation_config = genai.GenerationConfig(
    temperature=0.9,
    top_p=1.0,
    top_k=32,
    candidate_count=1,
    max_output_tokens=8192,
)

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

# Analyze text
def analyze_text(text):
    prompt = f"{analysis_system_prompt}\nContent:\n{text}"
    response = model.generate_content(
        [prompt],
        generation_config=generation_config,
        safety_settings=safety_settings,
    )
    return response.text if response else "No response generated."

# Analyze image
def analyze_image(image: Image.Image) -> str:
    prompt = f"{analysis_system_prompt}\nAnalyze this image for any instances of gender-based discrimination."
    resized_image = preprocess_image(image)
    response = model.generate_content(
        [prompt, resized_image],
        generation_config=generation_config,
        safety_settings=safety_settings,
    )
    return response.text if response else "No response generated."

# Preprocess image by resizing
def preprocess_image(image: Image.Image) -> str:
    image = image.resize((512, int(image.height * 512 / image.width)))
    return "a detailed analysis of the visual content, focusing on gender-based discrimination aspects"

# Generate podcast script
def generate_podcast_script(content):
    prompt = f"{podcast_prompt}\nAnalyzed content:\n{content}"
    response = model.generate_content([prompt], generation_config=generation_config)
    script = response.text if response else "Eva has no commentary at this time."
    return script

# Convert script to audio using gTTS
def text_to_speech(script):
    lines = [line.strip() for line in script.split(".") if line.strip()]  # Split by sentences for manageable TTS segments
    audio_files = []

    for line in lines:
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3')
        try:
            tts = gTTS(text=line, lang='en', tld='com')  # Using 'com' for American accent
            tts.save(temp_file.name)
            sound = AudioSegment.from_mp3(temp_file.name)
            sound += AudioSegment.silent(duration=500)  # Add a 0.5-second pause after each sentence
            sound.export(temp_file.name, format="mp3")
            audio_files.append(temp_file.name)
        except Exception as e:
            print(f"Error generating audio for line '{line}': {e}")

    combined_audio = AudioSegment.empty()
    for file in audio_files:
        sound = AudioSegment.from_mp3(file)
        combined_audio += sound
        os.remove(file)  # Clean up temporary files

    output_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
    combined_audio.export(output_file.name, format="mp3")
    return output_file.name

# Generate and play podcast
def generate_and_play_podcast(content, content_type='text'):
    script = generate_podcast_script(content)
    return text_to_speech(script)

# Gradio interface setup
with gr.Blocks(css=css_style) as app:
    gr.HTML("""
        <div id="logo">
            <span class="letter j">J</span>
            <span class="letter u">u</span>
            <span class="letter s">s</span>
            <span class="letter t">t</span>
            <span class="letter e">E</span>
            <span class="letter v">v</span>
            <span class="letter a">a</span>
        </div>
    """)
    gr.Markdown("<h1 style='text-align: center; color:#f0f0f0;'>Promotes Gender Equality in Every Conversation</h1>")

    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)
        text_output = gr.Textbox(label="Analysis Output", lines=6)
        analyze_text_btn = gr.Button("Analyze Text")
        listen_podcast_btn = gr.Button("Listen to Eva")
        
        analyze_text_btn.click(analyze_text, inputs=text_input, outputs=text_output)
        listen_podcast_btn.click(generate_and_play_podcast, inputs=text_output, outputs=gr.Audio())

    with gr.Tab("Image Analysis"):
        image_input = gr.Image(label="Upload Image (e.g., screenshot, photos, etc.)", type="pil")
        image_output = gr.Textbox(label="Analysis Output", lines=6)
        analyze_image_btn = gr.Button("Analyze Image")
        listen_podcast_image_btn = gr.Button("Listen to Eva")

        analyze_image_btn.click(analyze_image, inputs=image_input, outputs=image_output)
        listen_podcast_image_btn.click(generate_and_play_podcast, inputs=image_output, outputs=gr.Audio())

app.launch()