app.py

import os
import requests
import random
import threading
import time
import schedule
import tweepy
import gradio as gr

# Secrets from HF Space environment
HF_TOKEN = os.environ['HF_TOKEN']
CONSUMER_KEY = os.environ['CONSUMER_KEY']
CONSUMER_SECRET = os.environ['CONSUMER_SECRET']
ACCESS_TOKEN = os.environ['ACCESS_TOKEN']
ACCESS_SECRET = os.environ['ACCESS_SECRET']

# LLM API setup
API_URL = "https://router.huggingface.co/v1/chat/completions"
headers = {
    "Authorization": f"Bearer {HF_TOKEN}",
}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

 # Topics for posts
topics = [
    "GeoAI as a field and it's application",
    "application of Geology with Tech for exploration",
    "application of AI in geological exploration",
    "Entrepreneurship"
]
# Function to generate a high-quality post using LLM
def generate_post(topic=None):
    if topic is None or topic == "Random":
        topic = random.choice(topics)
    prompt = f"Generate a high-quality, educational, and informative X (Twitter) post under 280 characters showcasing expertise in {topic}. Make it engaging, insightful, and professional. Include relevant hashtags and hashtags shouldn't be more than 2 or not even at all in some post also avoid using - in the post so it looks natural."
    response = query({
        "messages": [{"role": "user", "content": prompt}],
        "model": "deepseek-ai/DeepSeek-V3.2:novita",
        "max_tokens": 200,
        "temperature": 0.8
    })
    post_content = response["choices"][0]["message"]["content"].strip()
    if len(post_content) > 280:
        post_content = post_content[:277] + "..."
    return post_content

# Tweepy v2 Client setup
client = tweepy.Client(
    consumer_key=CONSUMER_KEY,
    consumer_secret=CONSUMER_SECRET,
    access_token=ACCESS_TOKEN,
    access_token_secret=ACCESS_SECRET
)

# Global queue and scheduler state
post_queue = []
scheduler_running = False
scheduler_thread = None

# Function to post to X
def post_to_x(content):
    try:
        response = client.create_tweet(text=content)
        tweet_id = response.data['id']
        return f"Posted! https://x.com/user/status/{tweet_id}"
    except Exception as e:
        return f"Error: {str(e)}"

# Scheduler job — posts one item from queue
def scheduled_post_job():
    global post_queue
    if post_queue:
        content = post_queue.pop(0)  # FIFO: first in, first out
        result = post_to_x(content)
        print(f"[Scheduler] {result}\nContent: {content}")
        return result, "\n\n".join(post_queue) if post_queue else "Queue is empty."
    else:
        print("[Scheduler] Queue is empty.")
        return "No post scheduled — queue is empty.", "Queue is empty."

# Background scheduler loop
def run_scheduler():
    global scheduler_running
    schedule.every(2).hours.do(scheduled_post_job)
    while scheduler_running:
        schedule.run_pending()
        time.sleep(30)

# Start scheduler
def start_scheduler():
    global scheduler_running, scheduler_thread
    if not scheduler_running:
        scheduler_running = True
        scheduler_thread = threading.Thread(target=run_scheduler, daemon=True)
        scheduler_thread.start()
        return "Scheduler started! Posts every 2 hours from the queue."
    return "Scheduler is already running."

# Stop scheduler
def stop_scheduler():
    global scheduler_running
    scheduler_running = False
    return "Scheduler stopped."

# Clear queue function
def clear_queue():
    global post_queue
    post_queue.clear()
    return "Queue cleared.", "Queue is empty."

# Gradio functions
def generate_new_post(topic):
    content = generate_post(topic)
    return content, gr.update(visible=True)

def add_to_queue(content):
    global post_queue
    stripped = content.strip()
    if stripped and stripped not in post_queue:
        post_queue.append(stripped)
    return (
        "",  # Clear the current post box
        gr.update(visible=False),  # Hide Add button
        "\n\n".join(post_queue) if post_queue else "Queue is empty."
    )

# Gradio Interface
with gr.Blocks(title="X Post Generator & Scheduler") as demo:
    gr.Markdown("# AI/Tech/Startups X Post Generator & Queue Scheduler")
    gr.Markdown("Generate → Review → Add to queue → Start scheduler for automated 2-hour posting.")

    with gr.Row():
        topic_input = gr.Dropdown(choices=["Random"] + topics, value="Random", label="Topic")
        generate_btn = gr.Button("Generate New Post")

    current_post = gr.Textbox(label="Generated Post (Review before adding)", lines=6, interactive=False)
    add_btn = gr.Button("➕ Add to Queue", visible=False)

    queue_display = gr.Textbox(
        label="Post Queue (will be posted in order, every 2 hours)",
        value="Queue is empty.",
        lines=12,
        interactive=False
    )

    with gr.Row():
        start_btn = gr.Button("Start Scheduler")
        stop_btn = gr.Button("Stop Scheduler")
        clear_queue_btn = gr.Button("Clear Queue")

    status_box = gr.Textbox(label="Status", value="Ready", interactive=False)

    # Event bindings
    generate_btn.click(
        generate_new_post,
        inputs=topic_input,
        outputs=[current_post, add_btn]
    )

    add_btn.click(
        add_to_queue,
        inputs=current_post,
        outputs=[current_post, add_btn, queue_display]
    )

    start_btn.click(
        start_scheduler,
        outputs=status_box
    )

    stop_btn.click(
        stop_scheduler,
        outputs=status_box
    )

    clear_queue_btn.click(
        clear_queue,
        outputs=[status_box, queue_display]
    )

demo.launch()