Create app.py

app.py

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+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 = ['AI', 'Tech', 'Business Startups', 'Entrepreneurship']
+
+# Function to generate a high-quality post using LLM
+def generate_post(topic=None):
+    if topic is None:
+        topic = random.choice(topics)
+    prompt = f"Generate a high-quality, educational, and informative X (Twitter) post under 270 characters showcasing your expertise in {topic}. Make it engaging, insightful, and professional."
+    response = query({
+        "messages": [
+            {
+                "role": "user",
+                "content": prompt
+            }
+        ],
+        "model": "deepseek-ai/DeepSeek-V3.2:novita",
+        "max_tokens": 200,
+        "temperature": 0.7
+    })
+    post_content = response["choices"][0]["message"]["content"].strip()
+    # Ensure under 270 chars
+    if len(post_content) > 280:
+        post_content = post_content[:277] + "..."
+    return post_content
+
+# Tweepy setup for X API
+auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
+auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)
+api = tweepy.API(auth)
+
+# Function to post to X
+def post_to_x(content):
+    try:
+        api.update_status(status=content)
+        return f"Posted successfully: {content}"
+    except Exception as e:
+        return f"Error posting: {str(e)}"
+
+# Auto-post function
+def auto_post():
+    content = generate_post()
+    result = post_to_x(content)
+    print(result)  # Log to console
+
+# Background scheduler thread
+def run_scheduler():
+    schedule.every(2).hours.do(auto_post)
+    while True:
+        schedule.run_pending()
+        time.sleep(60)  # Check every minute
+
+# Start scheduler in background
+threading.Thread(target=run_scheduler, daemon=True).start()
+
+# Gradio interface for manual generation and posting
+def manual_generate(topic):
+    content = generate_post(topic)
+    return content
+
+def manual_post(content):
+    return post_to_x(content)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# AI/Tech/Startups/Entrepreneurship X Post Generator")
+    gr.Markdown("Use this interface to manually generate and post high-quality content. Auto-posting runs every 2 hours in the background.")
+    
+    with gr.Row():
+        topic_input = gr.Dropdown(choices=topics + ["Random"], label="Select Topic", value="Random")
+        generate_btn = gr.Button("Generate Post")
+    
+    output_text = gr.Textbox(label="Generated Post (under 270 chars)", lines=5)
+    
+    post_btn = gr.Button("Post to X")
+    post_result = gr.Textbox(label="Post Result")
+    
+    generate_btn.click(manual_generate, inputs=topic_input, outputs=output_text)
+    post_btn.click(manual_post, inputs=output_text, outputs=post_result)
+
+demo.launch()