commit

backend/app/services/flow_generator.py

@@ -0,0 +1,63 @@
+
+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

@@ -0,0 +1,57 @@
+
+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()