AI Researcher and Video Studio implementation complete

backend/models/research_intent_models.py

@@ -230,6 +230,14 @@ class ResearchIntent(BaseModel):
         le=1.0,
         description="Confidence in the intent inference"
     )
+    confidence_reason: Optional[str] = Field(
+        None,
+        description="Reason for the confidence level"
+    )
+    great_example: Optional[str] = Field(
+        None,
+        description="Example of what a great input would look like (if confidence is low)"
+    )
     needs_clarification: bool = Field(
         False,
         description="True if AI is uncertain and needs user clarification"
@@ -281,6 +289,8 @@ class IntentInferenceResponse(BaseModel):
         default_factory=list,
         description="Quick options for user to confirm/modify intent"
     )
+    confidence_reason: Optional[str] = Field(None, description="Reason for confidence level")
+    great_example: Optional[str] = Field(None, description="Example of great input (if confidence is low)")
 
 
 # ============================================================================

backend/models/research_trends_models.py

@@ -0,0 +1,51 @@
+"""
+Google Trends Data Models
+
+Pydantic models for Google Trends API responses.
+"""
+
+from typing import List, Dict, Any, Optional
+from pydantic import BaseModel, Field
+from datetime import datetime
+
+
+class GoogleTrendsData(BaseModel):
+    """Structured Google Trends data."""
+    interest_over_time: List[Dict[str, Any]] = Field(default_factory=list, description="Time series interest data")
+    interest_by_region: List[Dict[str, Any]] = Field(default_factory=list, description="Geographic interest data")
+    related_topics: Dict[str, List[Dict[str, Any]]] = Field(
+        default_factory=dict,
+        description="Related topics: {top: [...], rising: [...]}"
+    )
+    related_queries: Dict[str, List[Dict[str, Any]]] = Field(
+        default_factory=dict,
+        description="Related queries: {top: [...], rising: [...]}"
+    )
+    trending_searches: Optional[List[str]] = Field(None, description="Current trending searches")
+    timeframe: str = Field(..., description="Timeframe used (e.g., 'today 12-m')")
+    geo: str = Field(..., description="Geographic region (e.g., 'US', 'GB')")
+    keywords: List[str] = Field(..., description="Keywords analyzed")
+    timestamp: datetime = Field(default_factory=datetime.utcnow, description="When data was fetched")
+
+
+class TrendsConfig(BaseModel):
+    """Google Trends configuration with AI-driven justifications."""
+    enabled: bool = Field(True, description="Whether trends analysis is enabled")
+    keywords: List[str] = Field(..., description="AI-optimized keywords for trends analysis")
+    keywords_justification: str = Field(..., description="Why these keywords were chosen")
+    timeframe: str = Field(default="today 12-m", description="Timeframe: 'today 1-y', 'today 12-m', 'all', etc.")
+    timeframe_justification: str = Field(..., description="Why this timeframe was chosen")
+    geo: str = Field(default="US", description="Country code (e.g., 'US', 'GB', 'IN')")
+    geo_justification: str = Field(..., description="Why this geographic region was chosen")
+    expected_insights: List[str] = Field(
+        default_factory=list,
+        description="What insights trends will uncover for content generation"
+    )
+
+
+class TrendsAnalysisResponse(BaseModel):
+    """Response from trends analysis endpoint."""
+    success: bool
+    data: Optional[GoogleTrendsData] = None
+    error_message: Optional[str] = None
+    cached: bool = Field(False, description="Whether data was served from cache")