File size: 10,402 Bytes
9eaf163 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 3470339 805d716 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
# managers/gemini_manager.py
#
# Copyright (C) August 4, 2025 Carlos Rodrigues dos Santos
#
# Version: 1.1.0
#
# This file defines the GeminiManager, a specialist responsible for all Natural
# Language Processing, reasoning, and vision-language tasks. It acts as the
# Scriptwriter, Editor, and Cinematic Director for the ADUC framework, generating
# storyboards, prompts, and making creative decisions.
import os
import logging
import json
import gradio as gr
from PIL import Image
import google.generativeai as genai
import re
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def robust_json_parser(raw_text: str) -> dict:
"""
Parses a JSON object from a string that might contain extra text,
such as Markdown code blocks from an LLM's response.
"""
clean_text = raw_text.strip()
try:
# Try to find JSON delimited by ```json ... ```
match = re.search(r'```json\s*(\{.*?\})\s*```', clean_text, re.DOTALL)
if match:
json_str = match.group(1)
return json.loads(json_str)
# If not found, try to find the first '{' and the last '}'
start_index = clean_text.find('{')
end_index = clean_text.rfind('}')
if start_index != -1 and end_index != -1 and end_index > start_index:
json_str = clean_text[start_index : end_index + 1]
return json.loads(json_str)
else:
raise ValueError("No valid JSON object could be found in the AI's response.")
except json.JSONDecodeError as e:
logger.error(f"Failed to decode JSON. The AI returned the following text:\n---\n{raw_text}\n---")
raise ValueError(f"The AI returned an invalid JSON format: {e}")
class GeminiManager:
"""
Manages interactions with the Google Gemini API, acting as the primary
reasoning and language specialist for the ADUC framework.
"""
def __init__(self):
self.api_key = os.environ.get("GEMINI_API_KEY")
if self.api_key:
genai.configure(api_key=self.api_key)
self.model = genai.GenerativeModel('gemini-1.5-pro-latest')
logger.info("Gemini Specialist (1.5 Pro) initialized successfully.")
else:
self.model = None
logger.warning("Gemini API key not found. Specialist disabled.")
def _check_model(self):
"""Raises an error if the Gemini API is not configured."""
if not self.model:
raise gr.Error("The Google Gemini API key is not configured (GEMINI_API_KEY).")
def _read_prompt_template(self, filename: str) -> str:
"""Reads a prompt template file from the 'prompts' directory."""
try:
# Assuming the 'prompts' directory is in the root of the project
prompts_dir = Path(__file__).resolve().parent.parent / "prompts"
with open(prompts_dir / filename, "r", encoding="utf-8") as f:
return f.read()
except FileNotFoundError:
raise gr.Error(f"Prompt template file not found: prompts/{filename}")
def generate_storyboard(self, prompt: str, num_keyframes: int, ref_image_paths: list[str]) -> list[str]:
"""Delegated task: Acts as a Scriptwriter to generate a storyboard."""
self._check_model()
try:
template = self._read_prompt_template("unified_storyboard_prompt.txt")
storyboard_prompt = template.format(user_prompt=prompt, num_fragments=num_keyframes)
model_contents = [storyboard_prompt] + [Image.open(p) for p in ref_image_paths]
logger.info("Calling Gemini to generate storyboard...")
response = self.model.generate_content(model_contents)
logger.info(f"Gemini responded with (raw storyboard): {response.text}")
storyboard_data = robust_json_parser(response.text)
storyboard = storyboard_data.get("scene_storyboard", [])
if not storyboard or len(storyboard) != num_keyframes:
raise ValueError(f"Incorrect number of scenes generated. Expected {num_keyframes}, got {len(storyboard)}.")
return storyboard
except Exception as e:
raise gr.Error(f"The Scriptwriter (Gemini) failed: {e}")
def select_keyframes_from_pool(self, storyboard: list, base_image_paths: list[str], pool_image_paths: list[str]) -> list[str]:
"""Delegated task: Acts as a Photographer/Editor to select keyframes."""
self._check_model()
if not pool_image_paths:
raise gr.Error("The 'image pool' (Additional Images) is empty.")
try:
template = self._read_prompt_template("keyframe_selection_prompt.txt")
image_map = {f"IMG-{i+1}": path for i, path in enumerate(pool_image_paths)}
base_image_map = {f"BASE-{i+1}": path for i, path in enumerate(base_image_paths)}
model_contents = ["# Reference Images (Story Base)"]
for identifier, path in base_image_map.items():
model_contents.extend([f"Identifier: {identifier}", Image.open(path)])
model_contents.append("\n# Image Pool (Scene Bank)")
for identifier, path in image_map.items():
model_contents.extend([f"Identifier: {identifier}", Image.open(path)])
storyboard_str = "\n".join([f"- Scene {i+1}: {s}" for i, s in enumerate(storyboard)])
selection_prompt = template.format(storyboard_str=storyboard_str, image_identifiers=list(image_map.keys()))
model_contents.append(selection_prompt)
logger.info("Calling Gemini to select keyframes from pool...")
response = self.model.generate_content(model_contents)
logger.info(f"Gemini responded with (raw keyframe selection): {response.text}")
selection_data = robust_json_parser(response.text)
selected_identifiers = selection_data.get("selected_image_identifiers", [])
if len(selected_identifiers) != len(storyboard):
raise ValueError("The AI did not select the correct number of images for the scenes.")
selected_paths = [image_map[identifier] for identifier in selected_identifiers]
return selected_paths
except Exception as e:
raise gr.Error(f"The Photographer (Gemini) failed to select images: {e}")
def get_anticipatory_keyframe_prompt(self, global_prompt: str, scene_history: str, current_scene_desc: str, future_scene_desc: str, last_image_path: str, fixed_ref_paths: list[str]) -> str:
"""Delegated task: Acts as an Art Director to generate an image prompt."""
self._check_model()
try:
template = self._read_prompt_template("anticipatory_keyframe_prompt.txt")
director_prompt = template.format(
historico_prompt=scene_history,
cena_atual=current_scene_desc,
cena_futura=future_scene_desc
)
model_contents = [
"# CONTEXT:",
f"- Global Story Goal: {global_prompt}",
"# VISUAL ASSETS:",
"Current Base Image [IMG-BASE]:",
Image.open(last_image_path)
]
ref_counter = 1
for path in fixed_ref_paths:
if path != last_image_path:
model_contents.extend([f"General Reference Image [IMG-REF-{ref_counter}]:", Image.open(path)])
ref_counter += 1
model_contents.append(director_prompt)
logger.info("Calling Gemini to generate anticipatory keyframe prompt...")
response = self.model.generate_content(model_contents)
logger.info(f"Gemini responded with (raw keyframe prompt): {response.text}")
final_flux_prompt = response.text.strip().replace("`", "").replace("\"", "")
return final_flux_prompt
except Exception as e:
raise gr.Error(f"The Art Director (Gemini) failed: {e}")
def get_cinematic_decision(self, global_prompt: str, story_history: str,
past_keyframe_path: str, present_keyframe_path: str, future_keyframe_path: str,
past_scene_desc: str, present_scene_desc: str, future_scene_desc: str) -> dict:
"""
Delegated task: Acts as a Film Director to make editing decisions and generate motion prompts.
"""
self._check_model()
try:
template = self._read_prompt_template("cinematic_director_prompt.txt")
prompt_text = template.format(
global_prompt=global_prompt,
story_history=story_history,
past_scene_desc=past_scene_desc,
present_scene_desc=present_scene_desc,
future_scene_desc=future_scene_desc
)
model_contents = [
prompt_text,
"[PAST_IMAGE]:", Image.open(past_keyframe_path),
"[PRESENT_IMAGE]:", Image.open(present_keyframe_path),
"[FUTURE_IMAGE]:", Image.open(future_keyframe_path)
]
logger.info("Calling Gemini to generate cinematic decision...")
response = self.model.generate_content(model_contents)
logger.info(f"Gemini responded with (raw cinematic decision): {response.text}")
decision_data = robust_json_parser(response.text)
if "transition_type" not in decision_data or "motion_prompt" not in decision_data:
raise ValueError("AI response (Cinematographer) is malformed. Missing 'transition_type' or 'motion_prompt'.")
return decision_data
except Exception as e:
logger.error(f"The Film Director (Gemini) failed: {e}. Using fallback to 'continuous'.")
return {
"transition_type": "continuous",
"motion_prompt": f"A smooth, continuous cinematic transition from '{present_scene_desc}' to '{future_scene_desc}'."
}
# --- Singleton Instance ---
gemini_manager_singleton = GeminiManager() |