import os 
import json
import gradio as gr
from transformers import Tool
from huggingface_hub import upload_folder
from huggingface_hub import create_repo
#from gradio import forms
import time

#########
import streamlit as st

st.title("Drop files onto the chat input field")


def handle_file_drop(files):
    # Do something with the dropped files (e.g., process them, store them, etc.)
    print(f"Dropped files: {files}")
    
# Create a chat input field with file upload capabilities
chat_input = st.chat_input(
    placeholder="Type your message or drop a file...",
    key="chat_input",
   # accept_drops=False,
    on_submit=handle_file_drop
)


#if __name__ == "__main__":
#    st.launch(function_name="app")
#########
###############################
#os.environ["DISPLAY"] = ":99.0"

import streamlit as st
import pyautogui

import os

st.title("Chat Interface with Screenshot Capability")

# Create a chat input field
chat_input = st.chat_input(
    placeholder="Type your message or click the button to take a screenshot...",
    key="chat_input"
)

# Create a button to trigger the screenshot capture
screenshot_button = st.button("Take Screenshot")

# Define a function to capture the screenshot
def capture_screenshot():
    # Capture the entire screen
    img = pyautogui.screenshot("EntireScreen")

    # Upload the screenshot to the chat input field
    chat_input.insert_image(img)

# Bind the function to the button
screenshot_button.do(capture_screenshot)

# Display the chat input field and button
st.write(chat_input)
st.write(screenshot_button)


####################

def generate_files(title="Text Generation Tool", tool_description="This is a tool that chats with a user. "
                   "It takes an input named `prompt` which contains a system_role, user_message, context and history. It returns a text message."):
    
    # Generate readme content
    readme_content = '''
---
title: {}
emoji: 🌖
colorFrom: blue
colorTo: blue
sdk: gradio
sdk_version: 4.3.0
app_file: app.py
pinned: false
tags:
  - tool
---
    '''.format(title)

    tool_name = title.replace(" ", "_").lower()
    tool_class = title.replace(" ", "")
    tool_repo_id = title.replace(" ", "-")

	
    # Generate tool config JSON content
    tool_config = {
        "description": tool_description,
        "name": tool_name,
        "tool_class": "{}Tool".format(tool_class)
    }
    tool_config_json = json.dumps(tool_config, indent=4)

    # Generate app.py content
    app_py_content = '''
from transformers.tools.base 
from transformers import Tool
import launch_gradio_demo
from {} import {}
launch_gradio_demo({}Tool)
    '''.format( tool_name, tool_class, tool_class)

    # Generate requirements.txt content
    requirements_content = '''
transformers>=4.29.0
# diffusers
accelerate
torch
    '''

    # Generate text_generator.py content
    text_generator_py_content = '''
import os
from transformers import pipeline
class {}(Tool):
name = "{}"
description = (
    "{}"
)
inputs = ["text"]
outputs = ["text"]
def __call__(self, prompt: str):
    token = os.environ['hf']
    text_generator = pipeline(model="microsoft/Orca-2-13b", token=token)
    generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)
    print(generated_text)
    return generated_text
'''.format(tool_class, tool_name, tool_description)

    # Create a new folder for the tool
    os.makedirs(tool_class, exist_ok=True)

    # Write content to files
    with open(f"{tool_class}/README.md", "w") as readme_file:
        readme_file.write(readme_content)

    with open(f"{tool_class}/tool_config.json", "w") as tool_config_file:
        tool_config_file.write(tool_config_json)

    with open(f"{tool_class}/app.py", "w") as app_py_file:
        app_py_file.write(app_py_content)

    with open(f"{tool_class}/requirements.txt", "w") as requirements_file:
        requirements_file.write(requirements_content)

    with open(f"{tool_class}/app.py", "w") as text_generator_py_file:
        text_generator_py_file.write(text_generator_py_content)

    create_repo(repo_id=tool_repo_id, repo_type="space", space_sdk = "gradio")
        #repo_type="space"

    
    # Sleep for 5 seconds
    time.sleep(5)
    
    print("Slept for 5 seconds!")
    # Upload the folder to the Hugging Face Hub
    upload_folder(
        folder_path=tool_class,
        repo_id=f"Chris4K/{tool_repo_id}",
        repo_type="space"
    )

    # Return the generated files for download
    return f"Chris4K/{tool_class}"


# Define the inputs for the Gradio interface
io = gr.Interface(generate_files,
                    inputs=[
                            gr.Textbox(
                                label="Titel",
                                info="Initial text",
                                lines=1,
                                value="Cool Tool3",
                            ),
                            gr.Textbox(
                                label="Text 2",
                                info="Text to compare",
                                lines=3,
                                value="The fast brown fox jumps over lazy dogs.",
                            ),
                        ],
                    outputs=["text"])

# Launch the Gradio interface
io.launch()