Update app.py

app.py

@@ -349,8 +349,6 @@ with gr.Blocks(title="Blog Generator & Publisher") as app:
 
 if __name__ == "__main__":
     app.launch()'''
-
-
 import gradio as gr
 import os
 import time
@@ -358,69 +356,60 @@ import tempfile
 import requests
 from PIL import Image
 from io import BytesIO
-import markdown
 import re
-import json
-import random
 from datetime import datetime
+from dotenv import load_dotenv
 
-# Hugging Face Inference API endpoints
+# Load environment variables
+load_dotenv()
+
+# Hugging Face configuration
+HF_TOKEN = os.getenv("hftken")  # Make sure this matches your .env variable name
 TEXT_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
 IMAGE_API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
-HEADERS = {"Authorization": "HF_TOKEN"}  # Replace with your token
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}  # Correct header format
 
 def generate_blog_content(topic, tone="professional", length="medium"):
     current_date = datetime.now().strftime("%B %d, %Y")
     reading_time = {"short": "5-8", "medium": "8-12", "long": "15-20"}[length]
 
-    prompt = f"""Write a comprehensive blog post about {topic} with this structure:
-    # [Main Title]
-    ## [Subtitle]
-    **Published**: {current_date}
-    **Reading time**: {reading_time} minutes
-    
-    ### Table of Contents
-    - [Introduction](#introduction)
-    - [Section 1](#section-1)
-    - [Section 2](#section-2)
-    - [Conclusion](#conclusion)
-    
-    ## Introduction
-    [Engaging opening paragraph with statistics]
-    
-    ## Section 1: [Section Title]
-    [Detailed content with examples and data]
-    
-    ## Section 2: [Section Title]
-    [Technical details or case studies]
-    
-    ## Conclusion
-    [Summary and final thoughts]
-    
-    Use {tone} tone and include:
+    # Proper Mistral instruction format
+    prompt = f"""<s>[INST] Write a professional blog post about {topic} with this structure:
+    - Title and subtitle
+    - Introduction with statistics
+    - 2 main sections with subsections
+    - Conclusion
     - Markdown formatting
-    - Bullet points
-    - Realistic statistics
-    - 3 subsections per section"""
+    - Published date: {current_date}
+    - Reading time: {reading_time} minutes
+    Use {tone} tone [/INST]</s>"""
 
     payload = {
         "inputs": prompt,
         "parameters": {
-            "max_length": 2000,
+            "max_new_tokens": 1024,
             "temperature": 0.7,
-            "do_sample": True
+            "return_full_text": False  # Important to exclude original prompt
         }
     }
 
     try:
         response = requests.post(TEXT_API_URL, headers=HEADERS, json=payload)
+        
+        if response.status_code == 503:
+            # Handle model loading
+            estimate = response.json()['estimated_time']
+            time.sleep(estimate)
+            response = requests.post(TEXT_API_URL, headers=HEADERS, json=payload)
+            
         response.raise_for_status()
+        
         result = response.json()
-        if isinstance(result, list) and len(result) > 0:
-            return result[0]['generated_text'].split(prompt)[-1].strip()
-        return f"Error: {result.get('error', 'Unknown error')}"
+        return result[0]['generated_text']
+        
     except Exception as e:
-        return f"Error generating content: {str(e)}"
+        return f"Error: {str(e)} - {response.text if 'response' in locals() else ''}"
+
 
 def generate_featured_image(topic):
     prompt = f"Professional digital illustration for blog about {topic}, high quality, trending on artstation"