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

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

backend/api/facebook_writer/models/engagement_models.py

@@ -0,0 +1,70 @@
+"""Pydantic models for Facebook Engagement Analysis functionality."""
+
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field
+from enum import Enum
+
+
+class ContentType(str, Enum):
+    """Content type options for analysis."""
+    POST = "Post"
+    STORY = "Story"
+    REEL = "Reel"
+    CAROUSEL = "Carousel"
+    VIDEO = "Video"
+    IMAGE = "Image"
+    LINK = "Link"
+
+
+class AnalysisType(str, Enum):
+    """Analysis type options."""
+    CONTENT_ANALYSIS = "Content analysis"
+    PERFORMANCE_PREDICTION = "Performance prediction"
+    OPTIMIZATION_SUGGESTIONS = "Optimization suggestions"
+    COMPETITOR_COMPARISON = "Competitor comparison"
+    TREND_ANALYSIS = "Trend analysis"
+
+
+class FacebookEngagementRequest(BaseModel):
+    """Request model for Facebook engagement analysis."""
+    content: str = Field(..., description="Content to analyze")
+    content_type: ContentType = Field(..., description="Type of content being analyzed")
+    analysis_type: AnalysisType = Field(..., description="Type of analysis to perform")
+    business_type: str = Field(..., description="Type of business")
+    target_audience: str = Field(..., description="Target audience description")
+    post_timing: Optional[str] = Field(None, description="When the content was/will be posted")
+    hashtags: Optional[List[str]] = Field(None, description="Hashtags used with the content")
+    competitor_content: Optional[str] = Field(None, description="Competitor content for comparison")
+    historical_performance: Optional[Dict[str, Any]] = Field(None, description="Historical performance data")
+
+
+class EngagementMetrics(BaseModel):
+    """Engagement metrics and predictions."""
+    predicted_reach: str = Field(..., description="Predicted reach")
+    predicted_engagement_rate: str = Field(..., description="Predicted engagement rate")
+    predicted_likes: str = Field(..., description="Predicted likes")
+    predicted_comments: str = Field(..., description="Predicted comments")
+    predicted_shares: str = Field(..., description="Predicted shares")
+    virality_score: str = Field(..., description="Virality potential score")
+
+
+class OptimizationSuggestions(BaseModel):
+    """Content optimization suggestions."""
+    content_improvements: List[str] = Field(..., description="Content improvement suggestions")
+    timing_suggestions: List[str] = Field(..., description="Posting time optimization")
+    hashtag_improvements: List[str] = Field(..., description="Hashtag optimization suggestions")
+    visual_suggestions: List[str] = Field(..., description="Visual element suggestions")
+    engagement_tactics: List[str] = Field(..., description="Engagement boosting tactics")
+
+
+class FacebookEngagementResponse(BaseModel):
+    """Response model for Facebook engagement analysis."""
+    success: bool = Field(..., description="Whether the analysis was successful")
+    content_score: Optional[float] = Field(None, description="Overall content quality score (0-100)")
+    engagement_metrics: Optional[EngagementMetrics] = Field(None, description="Predicted engagement metrics")
+    optimization_suggestions: Optional[OptimizationSuggestions] = Field(None, description="Optimization recommendations")
+    sentiment_analysis: Optional[Dict[str, Any]] = Field(None, description="Content sentiment analysis")
+    trend_alignment: Optional[Dict[str, Any]] = Field(None, description="Alignment with current trends")
+    competitor_insights: Optional[Dict[str, Any]] = Field(None, description="Competitor comparison insights")
+    error: Optional[str] = Field(None, description="Error message if analysis failed")
+    metadata: Optional[Dict[str, Any]] = Field(None, description="Additional metadata about the analysis")