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| title: MultiAgent System For Screenplay Creation | |
| emoji: 🏆 | |
| colorFrom: yellow | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.32.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # TODO NAME OF THE AGENT | |
| ## Agent capabilities | |
| TODO: BETTER INTRO | |
| The aim of our agent is to support authors in their creative process for scenarios and storyboards. | |
| ### Agent Flow | |
|  | |
| **A** | |
| Starting the agent | |
| **B** | |
| The agent receives as input a text file containing the script, either in plain text format or in structured formats (e.g. PDF, DOCX), which it then converts into plain text for processing. | |
| **C** | |
| The agent extracts a summary of the overall content of the scenario, identifying the main narrative lines and the time frame. | |
| This will help creating a big picture version of the draft for the next steps | |
| **D** | |
| The agent will identify the main entities (characters, locations, events) and key themes in the script. | |
| It will also generate a small abstract (~5 sentences) with enough details to understand the overall plot and tone. | |
| **E** | |
| The agent checks whether the input text matches a known or published script. | |
| If it does, it will check the license and availability of rights to understand if it is possible to operate on it. | |
| In case of any limitations, the agent will warn the user about restrictions. | |
| **F** | |
| The agent will perform an analysis of the main points of the sctipt: | |
| - Characters: extract and catalog the names of the characters, classifying them by role (protagonist, antagonist, secondary characters), gender and age/physical description. | |
| - Locations: Detect the places where the scenes take place (interiors, exteriors, historical periods, geographical location) and catalogue them. | |
| - Plot points: Isolate key plot points | |
| - | |
| ### Main Techniques | |
| - Transformer-based NLP architectures (BERT, GPT-4) to produce a coherent text synthesis | |
| - Named Entity Recognition (NER) and context analysis, to identify human characters and their roles | |
| - Semantic analysis of textual descriptions, toponym extraction, creation of an internal scene map | |
| - Detection of text patterns (turning expressions such as “Suddenly”, “In the meantime”) and classification using a Story Understanding model | |
| - | |
| ### Code overview | |
| ### Use cases | |