Add Facebook Writer API with models, routers, and migration summary

Co-authored-by: ajay.calsoft <ajay.calsoft@gmail.com>

backend/api/facebook_writer/services/base_service.py

@@ -0,0 +1,171 @@
+"""Base service for Facebook Writer functionality."""
+
+import os
+import sys
+from pathlib import Path
+from typing import Dict, Any, Optional
+from loguru import logger
+
+# Add the backend path to sys.path to import services
+backend_path = Path(__file__).parent.parent.parent.parent
+sys.path.append(str(backend_path))
+
+from services.llm_providers.gemini_provider import gemini_text_response, gemini_structured_json_response
+
+
+class FacebookWriterBaseService:
+    """Base service class for Facebook Writer functionality."""
+    
+    def __init__(self):
+        """Initialize the base service."""
+        self.logger = logger
+        
+    def _generate_text(self, prompt: str, temperature: float = 0.7, max_tokens: int = 2048) -> str:
+        """
+        Generate text using Gemini provider.
+        
+        Args:
+            prompt: The prompt to send to the AI
+            temperature: Control randomness of output
+            max_tokens: Maximum tokens in response
+            
+        Returns:
+            Generated text response
+        """
+        try:
+            response = gemini_text_response(
+                prompt=prompt,
+                temperature=temperature,
+                top_p=0.9,
+                n=40,
+                max_tokens=max_tokens,
+                system_prompt=None
+            )
+            return response
+        except Exception as e:
+            self.logger.error(f"Error generating text: {e}")
+            raise
+    
+    def _generate_structured_response(
+        self, 
+        prompt: str, 
+        schema: Dict[str, Any], 
+        temperature: float = 0.3, 
+        max_tokens: int = 8192
+    ) -> Dict[str, Any]:
+        """
+        Generate structured JSON response using Gemini provider.
+        
+        Args:
+            prompt: The prompt to send to the AI
+            schema: JSON schema for structured output
+            temperature: Control randomness (lower for structured output)
+            max_tokens: Maximum tokens in response
+            
+        Returns:
+            Structured JSON response
+        """
+        try:
+            response = gemini_structured_json_response(
+                prompt=prompt,
+                schema=schema,
+                temperature=temperature,
+                top_p=0.9,
+                top_k=40,
+                max_tokens=max_tokens,
+                system_prompt=None
+            )
+            return response
+        except Exception as e:
+            self.logger.error(f"Error generating structured response: {e}")
+            raise
+    
+    def _build_base_prompt(self, business_type: str, target_audience: str, purpose: str) -> str:
+        """
+        Build a base prompt for Facebook content generation.
+        
+        Args:
+            business_type: Type of business
+            target_audience: Target audience description
+            purpose: Purpose or goal of the content
+            
+        Returns:
+            Base prompt string
+        """
+        return f"""
+        You are an expert Facebook content creator specializing in creating engaging, high-performing social media content.
+        
+        Business Context:
+        - Business Type: {business_type}
+        - Target Audience: {target_audience}
+        - Content Purpose: {purpose}
+        
+        Create content that:
+        1. Resonates with the target audience
+        2. Aligns with Facebook's best practices
+        3. Encourages engagement and interaction
+        4. Maintains a professional yet approachable tone
+        5. Includes relevant calls-to-action when appropriate
+        """
+    
+    def _create_analytics_prediction(self) -> Dict[str, str]:
+        """
+        Create default analytics predictions.
+        
+        Returns:
+            Dictionary with analytics predictions
+        """
+        return {
+            "expected_reach": "2.5K - 5K",
+            "expected_engagement": "5-8%",
+            "best_time_to_post": "2 PM - 4 PM"
+        }
+    
+    def _create_optimization_suggestions(self, content_type: str = "post") -> list:
+        """
+        Create default optimization suggestions.
+        
+        Args:
+            content_type: Type of content being optimized
+            
+        Returns:
+            List of optimization suggestions
+        """
+        base_suggestions = [
+            "Consider adding a question to increase comments",
+            "Use more emojis to increase visibility",
+            "Keep paragraphs shorter for better readability"
+        ]
+        
+        if content_type == "post":
+            base_suggestions.append("Add a poll to increase engagement")
+        elif content_type == "story":
+            base_suggestions.append("Include interactive stickers")
+        elif content_type == "reel":
+            base_suggestions.append("Use trending music for better reach")
+        
+        return base_suggestions
+    
+    def _handle_error(self, error: Exception, operation: str) -> Dict[str, Any]:
+        """
+        Handle errors and return standardized error response.
+        
+        Args:
+            error: The exception that occurred
+            operation: Description of the operation that failed
+            
+        Returns:
+            Standardized error response
+        """
+        error_message = f"Error in {operation}: {str(error)}"
+        self.logger.error(error_message)
+        
+        return {
+            "success": False,
+            "error": error_message,
+            "content": None,
+            "metadata": {
+                "operation": operation,
+                "error_type": type(error).__name__
+            }
+        }