import gradio as gr
import replicate
import os
import random
import openai
import numpy as np
from PIL import Image
import requests
import io
import base64
import zipfile

# Set API tokens
os.environ["REPLICATE_API_TOKEN"] = "r8_Brv0MtpmAiqrXrMrziyUXoSHuFV5hqs1Lw4Mo"
# Initialize the Replicate client
rep_client = replicate.Client()

# Set your OpenAI API key
OPENAI_API_KEY = "sk-proj-5iy4bwrqAW8GpguiEawaT3BlbkFJ8p88lLSjOCeDbxWsAOlr"
openai.api_key = OPENAI_API_KEY

predefined_prompts = [
    "Missing bolts on railway track",
    "Cracks on railway track",
    "Overgrown vegetation near railway track",
    "Broken railings on railway bridge",
    "Debris on railway track",
    "Damaged railway platform"
]

def handle_feedback(feedback, sentiment):
    if sentiment == "like":
        return "Thank you for your positive feedback!"
    else:
        return "Sorry to hear that. We are trying to improve based on your feedback."

def generate_variations(base_prompt, number_of_variations):
    locations = ["on the left side", "on the right side", "at the top", "at the bottom", "in the center"]
    sizes = ["small", "medium", "large", "tiny", "huge"]
    weather_conditions = ["under cold conditions", "during hot weather", "in dry weather", "in humid conditions", "under varying temperatures"]

    variations = []
    for _ in range(number_of_variations):
        location = random.choice(locations)
        size = random.choice(sizes)
        weather = random.choice(weather_conditions)
        
        full_prompt = f"{base_prompt}, with a {size} defect {location}, observed {weather}."
        variations.append(full_prompt)
    return variations

def generate_images(prompts):
    images = []
    for prompt in prompts:
        try:
            prediction = rep_client.predictions.create(
                version="ac732df83cea7fff18b8472768c88ad041fa750ff7682a21affe81863cbe77e4",
                input={"prompt": prompt, "scheduler": "K_EULER"}
            )
            prediction.wait()
            if prediction.status == "succeeded" and prediction.output:
                images.append(prediction.output[0])
            else:
                images.append("Failed to generate image.")
        except Exception as e:
            images.append(f"Error: {str(e)}")
    return images

# UI creation
with gr.Blocks() as app:
    with gr.Tabs("Prompt Input"):
        with gr.Tab("Generate Images"):
            prompt_input = gr.Dropdown(choices=predefined_prompts, label="Select a defect prompt")
            number_input = gr.Number(label="Number of images", value=1, minimum=1, maximum=10)
            generate_button = gr.Button("Generate")
            gallery = gr.Gallery(label="Generated Images")
            
            generate_button.click(
                fn=lambda prompt, num: generate_images(generate_variations(prompt, num)),
                inputs=[prompt_input, number_input],
                outputs=gallery
            )

        with gr.Tab("Custom Defect"):
            custom_prompt_input = gr.Textbox(label="Custom Defect")
            number_input_custom = gr.Number(label="Number of images to generate", value=1, minimum=1, maximum=10)
            submit_button_custom = gr.Button("Generate")
            image_outputs_custom = gr.Gallery()

            submit_button_custom.click(
                fn=lambda prompt, num: generate_images(generate_variations(prompt, num)),
                inputs=[custom_prompt_input, number_input_custom],
                outputs=image_outputs_custom
            )
            
    feedback_input = gr.Textbox(label="Enter your feedback", placeholder="Write your feedback here...")
    like_button = gr.Button(value="👍 Like")
    dislike_button = gr.Button(value="👎 Dislike")
    feedback_result = gr.Textbox(label="System Response", interactive=False)
    refresh_button = gr.Button("Refresh Page")
    

    like_button.click(lambda x: handle_feedback(x, "like"), inputs=feedback_input, outputs=feedback_result)
    dislike_button.click(lambda x: handle_feedback(x, "dislike"), inputs=feedback_input, outputs=feedback_result)
    refresh_button.click(lambda: gr.update(reload_browser=True))

if __name__ == "__main__":
    app.launch()