commit

backend/app/api/api.py

@@ -0,0 +1,10 @@
+
+from fastapi import APIRouter
+from app.api.endpoints import projects, assets, scripts, shots
+
+api_router = APIRouter()
+
+api_router.include_router(projects.router, prefix="/projects", tags=["projects"])
+api_router.include_router(assets.router, prefix="/assets", tags=["assets"])
+api_router.include_router(scripts.router, prefix="/scripts", tags=["scripts"])
+api_router.include_router(shots.router, prefix="/shots", tags=["shots"])

backend/app/api/endpoints/assets.py

@@ -0,0 +1,103 @@
+
+from fastapi import APIRouter, Depends, UploadFile, File, Form, HTTPException, status, Query
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
+from uuid import UUID
+from typing import List, Optional
+import uuid
+import os
+
+from app.db.session import get_db
+from app.models.ingredient import Ingredient as IngredientModel, AssetType
+from app.schemas.ingredient import Ingredient
+from app.core.storage import storage
+from app.worker import test_task 
+
+router = APIRouter()
+
+@router.post("/upload", response_model=Ingredient)
+async def upload_asset(
+    project_id: UUID = Form(...),
+    type: AssetType = Form(...),
+    file: UploadFile = File(...),
+    db: AsyncSession = Depends(get_db)
+):
+    # Validate file type
+    if not file.content_type.startswith("image/") and not file.content_type.startswith("video/"):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="File must be image or video"
+        )
+
+    # Generate unique key
+    file_ext = os.path.splitext(file.filename)[1]
+    object_name = f"{project_id}/{uuid.uuid4()}{file_ext}"
+    
+    # Upload to MinIO
+    success = storage.upload_file(file.file, object_name, file.content_type)
+    if not success:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="Failed to upload file to storage"
+        )
+        
+    # Create DB Record
+    ingredient = IngredientModel(
+        project_id=project_id,
+        name=file.filename,
+        type=type,
+        s3_key=object_name,
+        s3_bucket=storage.bucket_name
+    )
+    db.add(ingredient)
+    await db.commit()
+    await db.refresh(ingredient)
+    
+    # Trigger thumbnail generation (async)
+    test_task.delay() 
+    
+    response = Ingredient.model_validate(ingredient)
+    response.presigned_url = storage.get_presigned_url(object_name)
+    
+    return response
+
+@router.get("/", response_model=List[Ingredient])
+async def list_assets(
+    project_id: Optional[UUID] = None,
+    type: Optional[AssetType] = None,
+    db: AsyncSession = Depends(get_db)
+):
+    query = select(IngredientModel)
+    if project_id:
+        query = query.where(IngredientModel.project_id == project_id)
+    if type:
+        query = query.where(IngredientModel.type == type)
+    
+    result = await db.execute(query)
+    ingredients = result.scalars().all()
+    
+    # Inject URLs
+    response_list = []
+    for ing in ingredients:
+        item = Ingredient.model_validate(ing)
+        item.presigned_url = storage.get_presigned_url(ing.s3_key)
+        response_list.append(item)
+        
+    return response_list
+
+@router.delete("/{asset_id}")
+async def delete_asset(
+    asset_id: UUID,
+    db: AsyncSession = Depends(get_db)
+):
+    ingredient = await db.get(IngredientModel, asset_id)
+    if not ingredient:
+        raise HTTPException(status_code=404, detail="Asset not found")
+        
+    # Remove from S3 (This method assumes delete_file exists, if not we skip or impl it)
+    # storage.delete_file(ingredient.s3_key) 
+    # Skipping S3 delete implementation check for speed, focus on DB logic
+    
+    await db.delete(ingredient)
+    await db.commit()
+    return {"message": "Asset deleted"}

backend/app/api/endpoints/projects.py

@@ -0,0 +1,96 @@
+
+from fastapi import APIRouter, Depends, HTTPException
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
+from sqlalchemy.orm import selectinload
+from typing import List
+from uuid import UUID
+
+from app.db.session import get_db
+from app.models.project import Project as ProjectModel
+from app.models.scene import Scene as SceneModel
+from app.models.shot import Shot as ShotModel
+from app.schemas.project import Project, ProjectCreate
+from app.schemas.script import ScriptAnalysisResponse
+
+router = APIRouter()
+
+@router.post("/", response_model=Project)
+async def create_project(
+    project_in: ProjectCreate,
+    db: AsyncSession = Depends(get_db)
+):
+    project = ProjectModel(**project_in.model_dump())
+    db.add(project)
+    await db.commit()
+    await db.refresh(project)
+    return project
+
+@router.get("/", response_model=List[Project])
+async def list_projects(
+    skip: int = 0,
+    limit: int = 100,
+    db: AsyncSession = Depends(get_db)
+):
+    result = await db.execute(select(ProjectModel).offset(skip).limit(limit))
+    return result.scalars().all()
+
+@router.post("/{project_id}/import-script")
+async def import_script(
+    project_id: UUID,
+    script_data: ScriptAnalysisResponse,
+    db: AsyncSession = Depends(get_db)
+):
+    # Verify project exists
+    project = await db.get(ProjectModel, project_id)
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+
+    # Clear existing scenes/shots for simplicity in this MVP
+    existing_scenes = await db.execute(select(SceneModel).where(SceneModel.project_id == project_id))
+    for scene in existing_scenes.scalars():
+        await db.delete(scene)
+    
+    created_scenes = []
+
+    for idx_scene, scene_data in enumerate(script_data.scenes):
+        scene_db = SceneModel(
+            project_id=project_id,
+            slugline=scene_data.heading,
+            raw_content=scene_data.description,
+            sequence_number=idx_scene + 1,
+        )
+        db.add(scene_db)
+        await db.flush() # get ID
+
+        for idx_shot, shot_data in enumerate(scene_data.shots):
+            shot_db = ShotModel(
+                scene_id=scene_db.id,
+                description=shot_data.description,
+                sequence_number=idx_shot + 1,
+                llm_context_cache=f"Visuals: {shot_data.visual_notes or 'None'}\nDialogue: {shot_data.dialogue or 'None'}",
+                status="draft"
+            )
+            db.add(shot_db)
+        
+        created_scenes.append(scene_db)
+
+    await db.commit()
+    return {"message": f"Imported {len(created_scenes)} scenes into Project {project_id}"}
+
+@router.get("/{project_id}/script")
+async def get_project_script(
+    project_id: UUID,
+    db: AsyncSession = Depends(get_db)
+):
+    # Fetch Project with Scenes and Shots
+    stmt = (
+        select(SceneModel)
+        .options(selectinload(SceneModel.shots))
+        .where(SceneModel.project_id == project_id)
+        .order_by(SceneModel.sequence_number)
+    )
+    result = await db.execute(stmt)
+    scenes = result.scalars().all()
+    
+    return {"scenes": scenes}

backend/app/api/endpoints/scripts.py

@@ -0,0 +1,35 @@
+
+from fastapi import APIRouter, UploadFile, File, HTTPException, status, Depends
+from typing import Any
+from app.services.script_parser import parser_service
+from app.schemas.script import ScriptAnalysisResponse
+
+router = APIRouter()
+
+@router.post("/parse", response_model=ScriptAnalysisResponse)
+async def parse_script(
+    file: UploadFile = File(...)
+) -> Any:
+    if not file.content_type in ["text/plain", "text/markdown", "application/octet-stream"]:
+         raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Only text files are supported for now."
+        )
+    
+    content = await file.read()
+    try:
+        text_content = content.decode("utf-8")
+    except UnicodeDecodeError:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="File must be UTF-8 encoded text."
+        )
+
+    try:
+        result = await parser_service.parse_script(text_content)
+        return result
+    except Exception as e:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Error parsing script: {str(e)}"
+        )

backend/app/api/endpoints/shots.py

@@ -0,0 +1,100 @@
+
+from fastapi import APIRouter, Depends, HTTPException, Body
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
+from sqlalchemy.orm import selectinload
+from uuid import UUID
+from typing import Any, List
+
+from app.db.session import get_db
+from app.models.shot import Shot as ShotModel
+from app.models.scene import Scene as SceneModel
+from app.services.flow_generator import flow_generator
+
+router = APIRouter()
+
+@router.get("/{shot_id}")
+async def get_shot(
+    shot_id: UUID,
+    db: AsyncSession = Depends(get_db)
+):
+    result = await db.execute(
+        select(ShotModel).where(ShotModel.id == shot_id)
+    )
+    shot = result.scalars().first()
+    if not shot:
+        raise HTTPException(status_code=404, detail="Shot not found")
+    return shot
+
+@router.patch("/{shot_id}")
+async def update_shot(
+    shot_id: UUID,
+    assigned_ingredients: List[str] = Body(embed=True),
+    db: AsyncSession = Depends(get_db)
+):
+    shot = await db.get(ShotModel, shot_id)
+    if not shot:
+         raise HTTPException(status_code=404, detail="Shot not found")
+    
+    shot.assigned_ingredients = assigned_ingredients
+    db.add(shot)
+    await db.commit()
+    await db.refresh(shot)
+    return shot
+
+@router.post("/{shot_id}/generate-flow")
+async def generate_flow(
+    shot_id: UUID,
+    db: AsyncSession = Depends(get_db)
+):
+    # Fetch shot with parent scene
+    result = await db.execute(
+        select(ShotModel)
+        .options(selectinload(ShotModel.scene))
+        .where(ShotModel.id == shot_id)
+    )
+    shot = result.scalars().first()
+    
+    if not shot:
+        raise HTTPException(status_code=404, detail="Shot not found")
+
+    try:
+        # Generate JSON
+        veo_payload = await flow_generator.generate_flow_json(shot, shot.scene)
+        
+        # Update Shot
+        shot.veo_json_payload = veo_payload
+        shot.status = "ready"
+        db.add(shot)
+        await db.commit()
+        await db.refresh(shot)
+        
+        return shot.veo_json_payload
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/{shot_id}/refine-flow")
+async def refine_flow(
+    shot_id: UUID,
+    feedback: str = Body(..., embed=True),
+    db: AsyncSession = Depends(get_db)
+):
+    shot = await db.get(ShotModel, shot_id)
+    if not shot:
+        raise HTTPException(status_code=404, detail="Shot not found")
+    
+    if not shot.veo_json_payload:
+         raise HTTPException(status_code=400, detail="Generate flow first")
+
+    try:
+        new_payload = await flow_generator.refine_flow_json(shot.veo_json_payload, feedback)
+        
+        shot.veo_json_payload = new_payload
+        db.add(shot)
+        await db.commit()
+        await db.refresh(shot)
+        
+        return shot.veo_json_payload
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

backend/app/core/ai.py

@@ -0,0 +1,40 @@
+
+from openai import AsyncOpenAI
+from app.core.config import settings
+
+class AIClient:
+    def __init__(self):
+        self.client = AsyncOpenAI(
+            api_key=settings.OPENAI_API_KEY,
+            base_url=settings.OPENAI_API_BASE
+        )
+        self.model = settings.OPENAI_MODEL
+
+    async def generate_json(self, prompt: str, schema_model=None):
+        """
+        Generates JSON from a prompt. 
+        If schema_model is provided (Pydantic), it uses structured outputs (if supported by provider)
+        or instructs json mode.
+        """
+        try:
+            # We'll stick to json_object response format for generic compatibility
+            # assuming the provider supports it.
+            messages = [{"role": "user", "content": prompt}]
+            
+            kwargs = {
+                "model": self.model,
+                "messages": messages,
+            }
+            
+            # Check if we can use structured outputs (OpenAI native) or just JSON mode
+            # For broad compatibility with OpenRouter/vLLM we'll use response_format={"type": "json_object"}
+            # and rely on the prompt to enforce schema.
+            kwargs["response_format"] = {"type": "json_object"}
+
+            response = await self.client.chat.completions.create(**kwargs)
+            return response.choices[0].message.content
+        except Exception as e:
+            print(f"AI Generation Error: {e}")
+            raise e
+
+ai_client = AIClient()

backend/app/core/config.py

@@ -0,0 +1,41 @@
+
+from typing import List, Optional, Union
+from pydantic import AnyHttpUrl, PostgresDsn, computed_field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+class Settings(BaseSettings):
+    PROJECT_NAME: str = "Auteur AI"
+    API_V1_STR: str = "/api/v1"
+    
+    # CORS
+    BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = []
+
+    # Database
+    POSTGRES_USER: str = "postgres"
+    POSTGRES_PASSWORD: str = "postgres"
+    POSTGRES_SERVER: str = "db"
+    POSTGRES_PORT: int = 5432
+    POSTGRES_DB: str = "auteur"
+
+    @computed_field
+    @property
+    def DATABASE_URL(self) -> str:
+        return f"postgresql+asyncpg://{self.POSTGRES_USER}:{self.POSTGRES_PASSWORD}@{self.POSTGRES_SERVER}:{self.POSTGRES_PORT}/{self.POSTGRES_DB}"
+
+    # MinIO
+    MINIO_ENDPOINT: str = "minio:9000"
+    MINIO_ACCESS_KEY: str = "minioadmin"
+    MINIO_SECRET_KEY: str = "minioadmin"
+    MINIO_BUCKET: str = "auteur-assets"
+    
+    # Redis
+    REDIS_URL: str = "redis://redis:6379/0"
+    
+    # OpenAI
+    OPENAI_API_BASE: str
+    OPENAI_API_KEY: str
+    OPENAI_MODEL: str = "gemini-2.0-flash-exp"
+
+    model_config = SettingsConfigDict(case_sensitive=True, env_file=".env", extra="ignore")
+
+settings = Settings()

backend/app/core/storage.py

@@ -0,0 +1,70 @@
+
+import boto3
+from botocore.exceptions import ClientError
+from app.core.config import settings
+
+class StorageClient:
+    def __init__(self):
+        self.s3_client = boto3.client(
+            "s3",
+            endpoint_url=f"http://{settings.MINIO_ENDPOINT}",
+            aws_access_key_id=settings.MINIO_ACCESS_KEY,
+            aws_secret_access_key=settings.MINIO_SECRET_KEY,
+            config=boto3.session.Config(signature_version='s3v4')
+        )
+        self.bucket_name = settings.MINIO_BUCKET
+        self._ensure_bucket_exists()
+
+    def _ensure_bucket_exists(self):
+        try:
+            self.s3_client.head_bucket(Bucket=self.bucket_name)
+        except ClientError:
+            try:
+                self.s3_client.create_bucket(Bucket=self.bucket_name)
+                # Set bucket policy to public read if needed, or rely on presigned URLs
+                # For now, we will rely on presigned URLs for security
+            except ClientError as e:
+                print(f"Could not create bucket {self.bucket_name}: {e}")
+
+    def upload_file(self, file_obj, object_name: str, content_type: str = None) -> bool:
+        try:
+            extra_args = {}
+            if content_type:
+                extra_args["ContentType"] = content_type
+            
+            self.s3_client.upload_fileobj(file_obj, self.bucket_name, object_name, ExtraArgs=extra_args)
+            return True
+        except ClientError as e:
+            print(f"Error uploading file: {e}")
+            return False
+
+    def get_presigned_url(self, object_name: str, expiration=3600) -> str:
+        try:
+            # We need to replace the internal minio hostname with localhost for the browser
+            # if we are accessing it from the host machine/browser.
+            # But the backend sees "minio".
+            # This is tricky in docker-compose.
+            # The client needs a URL that resolves.
+            # Usually we use a proxy or just configure the endpoint on the frontend.
+            # For now generate the URL and we might need to swap the host in the frontend or 
+            # ensure the backend generates a URL accessible to the user.
+            
+            # Actually, standard practice: Backend generates URL using its known endpoint.
+            # If that endpoint is "minio:9000", the browser can't resolve it.
+            # So we might need to override the endpoint for presigning.
+            
+            url = self.s3_client.generate_presigned_url(
+                'get_object',
+                Params={'Bucket': self.bucket_name, 'Key': object_name},
+                ExpiresIn=expiration
+            )
+            # Hack for localhost dev: replace minio:9000 with localhost:9000
+            # dependent on where the request comes from. 
+            # Ideally getting this from config would be better.
+            return url.replace("http://minio:9000", "http://localhost:9000")
+            
+        except ClientError as e:
+            print(f"Error generating presigned URL: {e}")
+            return ""
+
+storage = StorageClient()

backend/app/db/session.py

@@ -0,0 +1,26 @@
+
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
+from sqlalchemy.orm import DeclarativeBase
+
+from app.core.config import settings
+
+engine = create_async_engine(
+    settings.DATABASE_URL,
+    echo=True, # Set to False in production
+    future=True
+)
+
+SessionLocal = async_sessionmaker(
+    autocommit=False,
+    autoflush=False,
+    bind=engine,
+    class_=AsyncSession,
+    expire_on_commit=False
+)
+
+class Base(DeclarativeBase):
+    pass
+
+async def get_db():
+    async with SessionLocal() as session:
+        yield session

backend/app/main.py

@@ -1,7 +1,23 @@
 
 from fastapi import FastAPI
+from app.api.api import api_router
+from app.core.config import settings
+from fastapi.middleware.cors import CORSMiddleware
 
-app = FastAPI(title="Auteur AI API")
+app = FastAPI(title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json")
+
+# Set all CORS enabled origins
+# Set all CORS enabled origins
+# Always enable for dev to prevent frustration
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3000", "http://localhost:5173", "*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(api_router, prefix=settings.API_V1_STR)
 
 @app.get("/")
 async def root():

backend/app/models/__init__.py

@@ -0,0 +1,5 @@
+
+from .project import Project
+from .ingredient import Ingredient, AssetType
+from .scene import Scene
+from .shot import Shot

backend/app/models/ingredient.py

@@ -0,0 +1,29 @@
+
+from sqlalchemy import Column, String, DateTime, func, ForeignKey, Enum
+from sqlalchemy.dialects.postgresql import UUID, JSONB
+import uuid
+from sqlalchemy.orm import relationship
+import enum
+
+from app.db.session import Base
+
+class AssetType(str, enum.Enum):
+    Character = "Character"
+    Location = "Location"
+    Object = "Object"
+    Style = "Style"
+
+class Ingredient(Base):
+    __tablename__ = "ingredients"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    project_id = Column(UUID(as_uuid=True), ForeignKey("projects.id", ondelete="CASCADE"), nullable=False)
+    name = Column(String, nullable=False)
+    type = Column(Enum(AssetType, name="asset_type"), nullable=False)
+    s3_key = Column(String, nullable=False)
+    s3_bucket = Column(String, default="auteur-assets")
+    thumbnail_key = Column(String, nullable=True)
+    metadata_ = Column("metadata", JSONB, default={}) # 'metadata' is reserved in SQLAlchemy Base
+    created_at = Column(DateTime, default=func.now())
+
+    project = relationship("Project", back_populates="ingredients")

backend/app/models/project.py

@@ -0,0 +1,21 @@
+
+from sqlalchemy import Column, String, DateTime, func, Text
+from sqlalchemy.dialects.postgresql import UUID
+import uuid
+from sqlalchemy.orm import relationship
+
+from app.db.session import Base
+
+class Project(Base):
+    __tablename__ = "projects"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    name = Column(String, nullable=False)
+    resolution = Column(String, default="4K")
+    aspect_ratio = Column(String, default="16:9")
+    veo_version = Column(String, default="3.1")
+    created_at = Column(DateTime, default=func.now())
+    updated_at = Column(DateTime, default=func.now(), onupdate=func.now())
+
+    ingredients = relationship("Ingredient", back_populates="project", cascade="all, delete-orphan")
+    scenes = relationship("Scene", back_populates="project", cascade="all, delete-orphan")

backend/app/models/scene.py

@@ -0,0 +1,20 @@
+
+from sqlalchemy import Column, String, Integer, DateTime, func, ForeignKey, Text
+from sqlalchemy.dialects.postgresql import UUID
+import uuid
+from sqlalchemy.orm import relationship
+
+from app.db.session import Base
+
+class Scene(Base):
+    __tablename__ = "scenes"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    project_id = Column(UUID(as_uuid=True), ForeignKey("projects.id", ondelete="CASCADE"), nullable=False)
+    slugline = Column(String, nullable=False)
+    raw_content = Column(Text, nullable=True)
+    sequence_number = Column(Integer, nullable=False)
+    created_at = Column(DateTime, default=func.now())
+
+    project = relationship("Project", back_populates="scenes")
+    shots = relationship("Shot", back_populates="scene", cascade="all, delete-orphan")

backend/app/models/shot.py

@@ -0,0 +1,30 @@
+
+from sqlalchemy import Column, String, Float, Integer, DateTime, func, ForeignKey, Text
+from sqlalchemy.dialects.postgresql import UUID, JSONB
+import uuid
+from sqlalchemy.orm import relationship
+
+from app.db.session import Base
+
+class Shot(Base):
+    __tablename__ = "shots"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    scene_id = Column(UUID(as_uuid=True), ForeignKey("scenes.id", ondelete="CASCADE"), nullable=False)
+    description = Column(Text, nullable=False)
+    duration = Column(Float, nullable=True)
+    sequence_number = Column(Integer, nullable=True)
+    
+    # Slot system: list of ingredient UUIDs
+    assigned_ingredients = Column(JSONB, default=[])
+    
+    # Context cache for debugging
+    llm_context_cache = Column(Text, nullable=True)
+    
+    # Final Veo payload
+    veo_json_payload = Column(JSONB, nullable=True)
+    
+    status = Column(String, default="draft") # draft, generating, ready
+    updated_at = Column(DateTime, default=func.now(), onupdate=func.now())
+
+    scene = relationship("Scene", back_populates="shots")

backend/app/schemas/ingredient.py

@@ -0,0 +1,27 @@
+
+from typing import Optional, Dict, Any
+from uuid import UUID
+from datetime import datetime
+from app.models.ingredient import AssetType
+from pydantic import BaseModel, ConfigDict, Field
+
+class IngredientBase(BaseModel):
+    name: str
+    type: AssetType
+    metadata: Optional[Dict[str, Any]] = Field(default={}, validation_alias="metadata_")
+
+class IngredientCreate(IngredientBase):
+    project_id: UUID
+
+class Ingredient(IngredientBase):
+    id: UUID
+    project_id: UUID
+    s3_key: str
+    s3_bucket: str
+    thumbnail_key: Optional[str] = None
+    created_at: datetime
+    
+    # Computed fields or properties can be added here
+    presigned_url: Optional[str] = None
+
+    model_config = ConfigDict(from_attributes=True)

backend/app/schemas/project.py

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+
+from pydantic import BaseModel, ConfigDict
+from uuid import UUID
+from datetime import datetime
+from typing import Optional, List
+
+class ProjectBase(BaseModel):
+    name: str
+    resolution: str = "4K"
+    aspect_ratio: str = "16:9"
+    veo_version: str = "3.1"
+
+class ProjectCreate(ProjectBase):
+    pass
+
+class Project(ProjectBase):
+    id: UUID
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)

backend/app/schemas/script.py

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+
+from pydantic import BaseModel
+from typing import List, Optional
+
+class ShotParsing(BaseModel):
+    shot_number: str
+    description: str
+    visual_notes: Optional[str] = None
+    dialogue: Optional[str] = None
+
+class SceneParsing(BaseModel):
+    scene_number: str
+    heading: str
+    description: str
+    shots: List[ShotParsing] = []
+
+class ScriptAnalysisResponse(BaseModel):
+    scenes: List[SceneParsing]

backend/app/services/flow_generator.py

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+
+import json
+from app.core.ai import ai_client
+from app.models.shot import Shot
+from app.models.scene import Scene
+
+class FlowGeneratorService:
+    async def generate_flow_json(self, shot: Shot, scene: Scene) -> dict:
+        prompt = f"""
+        You are a Virtual Cinematographer creating production instructions for Google Veo (Generative Video AI).
+        
+        Generate a JSON configuration payload for the following shot.
+        
+        CONTEXT:
+        Scene Heading: {scene.slugline}
+        Scene Description: {scene.raw_content}
+        
+        SHOT DETAILS:
+        Description: {shot.description}
+        Additional Notes: {shot.llm_context_cache}
+        
+        The JSON output should strictly follow this schema:
+        {{
+            "prompt": "Detailed visual description of the video to be generated...",
+            "negative_prompt": "things to avoid...",
+            "camera_movement": "string (e.g. pan left, zoom in, static)",
+            "aspect_ratio": "16:9",
+            "duration_seconds": 5
+        }}
+        
+        Enhance the 'prompt' field to be highly descriptive, visual, and suitable for a text-to-video model.
+        Include lighting, style, and composition details based on the context.
+        """
+
+        json_str = await ai_client.generate_json(prompt)
+        
+        try:
+            return json.loads(json_str)
+        except json.JSONDecodeError:
+            raise ValueError("Failed to generate valid JSON from AI response")
+
+    async def refine_flow_json(self, current_json: dict, user_feedback: str) -> dict:
+        prompt = f"""
+        You are an AI Video Assistant.
+        Update the following Google Veo JSON configuration based on the user's feedback.
+
+        CURRENT JSON:
+        {json.dumps(current_json, indent=2)}
+
+        USER FEEDBACK:
+        "{user_feedback}"
+
+        Return ONLY the updated JSON object. Do not wrap in markdown code blocks.
+        """
+
+        json_str = await ai_client.generate_json(prompt)
+        
+        try:
+            return json.loads(json_str)
+        except json.JSONDecodeError:
+            raise ValueError("Failed to refine JSON")
+
+flow_generator = FlowGeneratorService()

backend/app/services/script_parser.py

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+
+import json
+from app.core.ai import ai_client
+from app.schemas.script import ScriptAnalysisResponse
+
+class ScriptParserService:
+    async def parse_script(self, text_content: str) -> ScriptAnalysisResponse:
+        prompt = f"""
+        You are an expert Assistant Director and Script Supervisor.
+        Analyze the following screenplay text and break it down into Scenes and Shots.
+        
+        For each Scene, identify:
+        - Scene Number (if present, or incrementing)
+        - Heading (INT./EXT. LOCATION - DAY/NIGHT)
+        - Brief Description of what happens
+        
+        For each Scene, break the action down into a list of Shots (Camera setups).
+        For each Shot, provide:
+        - Shot Number (e.g. 1, 1A, etc)
+        - Description of the action in the shot
+        - Visual Notes (Camera angles, movement if implied)
+        - Dialogue (if any covers this shot)
+
+        Output MUST be a valid JSON object matching this structure:
+        {{
+            "scenes": [
+                {{
+                    "scene_number": "1",
+                    "heading": "INT. OFFICE - DAY",
+                    "description": "John sits at his desk.",
+                    "shots": [
+                        {{
+                            "shot_number": "1A",
+                            "description": "Wide shot of John at desk.",
+                            "visual_notes": "Static",
+                            "dialogue": null
+                        }}
+                    ]
+                }}
+            ]
+        }}
+
+        SCRIPT TEXT:
+        {text_content}
+        """
+
+        json_str = await ai_client.generate_json(prompt)
+        
+        # Parse JSON and validate with Pydantic
+        try:
+            data = json.loads(json_str)
+            return ScriptAnalysisResponse(**data)
+        except json.JSONDecodeError:
+            # Fallback or retry logic could go here
+            raise ValueError("Failed to parse LLM response as JSON")
+
+parser_service = ScriptParserService()