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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 ask_rail_defect_question(question, model_name='ft:gpt-3.5-turbo-0125:personal::99NsSAeQ'):
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']
def ask_feedback(question, model_name='gpt-3.5-turbo'):
response = openai.ChatCompletion.create(
model=model_name,
messages=[
{
"role": "system",
"content": "The assistant is knowledgeable about user feedback and can analysis feedback and answer user-firndly way that they are updating the system message and LLM continuosly.",
},
{
"role": "user",
"content": question,
}
],
)
return response.choices[0].message['content']
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...")
send_feedback_button = gr.Button("Send Feedback 🚀")
like_button = gr.Button(value="👍 Like")
dislike_button = gr.Button(value="👎 Dislike")
feedback_result = gr.Textbox(label="System Response")
refresh_button = gr.Button("Refresh Page")
def handle_feedback(feedback, sentiment):
analysis = ask_feedback(feedback)
if sentiment == "👍 Like":
return f"Thank you for your positive feedback! We are updating our system based on your feedback."
elif sentiment == "👎 Dislike":
return f"We're sorry for not providing a satisfactory response. We are updating our system based on your feedback."
else:
return f"Your feedback: '{feedback}' was analyzed as: '{analysis}'. We are updating our system based on your feedback."
send_feedback_button.click(lambda x: handle_feedback(x, ""), inputs=feedback_input, outputs=feedback_result)
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()
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