app1.py

import gradio as gr
import replicate
import os
from huggingface_hub import InferenceClient
import random
import openai

# 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
# Initialize the Replicate client
rep_client = replicate.Client()

# Predefined prompts for the dropdown
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 ask_rail_defect_question(question, model_name='ft:gpt-3.5-turbo-0125:personal::99NsSAeQ'):
    openai.api_key = OPENAI_API_KEY
    response = openai.ChatCompletion.create(
        model=model_name,
        messages=[
            {
                "role": "system",
                "content": "The assistant is knowledgeable about rail defects and can answer questions related to them.",
            },
            {
                "role": "user",
                "content": question,
            }
        ],
    )
    return response.choices[0].message['content']

# Function to generate variations enhanced by the GPT model
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)
        
        # Enhance the base prompt with the GPT model
        enhanced_prompt = ask_rail_defect_question(base_prompt)
        
        full_prompt = f"{enhanced_prompt}, with a {size} defect {location}, observed {weather}."
        variations.append(full_prompt)
    return variations

# Function to generate images from prompts
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

def process_railway_defects(prompt, number_of_images):
    variations = generate_variations(prompt, number_of_images)
    images = generate_images(variations)
    return images
    


# UI creation
with gr.Blocks() as app:
    with gr.Tabs("Prompt Input"):
        with gr.Tab("Current Defects"):
            with gr.Row():
                prompt_input = gr.Dropdown(choices=predefined_prompts, label="Select a prompt")
                number_input_dropdown = gr.Number(label="Number of images to generate", value=1, minimum=1, maximum=10)
                submit_button_dropdown = gr.Button("Generate")
            image_outputs_dropdown = gr.Gallery()

            def on_submit_click_dropdown(prompt, number_of_images):
                images = process_railway_defects(prompt, number_of_images)
                return images



            submit_button_dropdown.click(
                fn=on_submit_click_dropdown,
                inputs=[prompt_input, number_input_dropdown],
                outputs=image_outputs_dropdown
            )

        with gr.Tab("Custom Defect"):
            with gr.Row():
                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()

            def on_submit_click_custom(custom_prompt, number_of_images):
              images = process_railway_defects(custom_prompt, number_of_images)
              return images

            submit_button_custom.click(
                fn=on_submit_click_custom,
                inputs=[custom_prompt_input, number_input_custom],
                outputs=image_outputs_custom
            )
        
if __name__ == "__main__":
    app.launch()