partial readme update

README.md

@@ -10,4 +10,66 @@ pinned: false
 license: mit
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/683ed65c9471bc9e3db5e4be/LNd2Yxa2unaqk0jFtl1oC.png)
+
+**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