Text update

app.py

@@ -64,7 +64,7 @@ def select_best_model(
 # Definition of the tools for the MCP server 
 # Function to return a fasta file
 def create_fasta_file(file_content: str, name: Optional[str] = None, seq_name: Optional[str] = None) -> str:
-    """Create a FASTA file from a protein sequence string with a unique name.
+    """Create a FASTA file from a biomolecule sequence string with a unique name.
     
     Args:
         file_content (str): The content of the FASTA file required with optional line breaks
@@ -232,7 +232,7 @@ def compute_Chai1(
 
 # Function to plot the 3D protein structure
 def plot_protein(result_df) -> str:
-    """Plot the 3D structure of a protein using the DataFrame from compute_Chai1.
+    """Plot the 3D structure of a biomolecule using the DataFrame from compute_Chai1.
 
     Args:
         result_df (pd.DataFrame): DataFrame containing model information and scores
@@ -262,7 +262,7 @@ def show_cif_file(cif_file):
     """Plot a 3D structure from a CIF file with the Molecule3D library.
 
     Args:
-        cif_file: A protein structure file in CIF format. This can be a file uploaded by the user.
+        cif_file: A biomolecule structure file in CIF format. This can be a file uploaded by the user.
             If None, the function will return None.
 
     Returns:
@@ -288,12 +288,12 @@ with gr.Blocks(theme=theme) as demo:
     gr.Markdown(
     """
     # Protein Folding Simulation Interface
-    This interface provides the tools to fold FASTA chains based on Chai-1 model. Also, this is a MCP server to provide all the tools to automate the process of folding proteins with LLMs.     
+    This interface provides the tools to fold FASTA chains based on Chai-1 model. Also, this is a MCP server to provide all the tools to automate the process of folding biomolecules with LLMs.     
     """) 
     
     with gr.Tab("Introduction 🔭"):
         
-        gr.Image("images/logo1.png", show_label=False, width=600, show_download_button=False, show_fullscreen_button=False)
+        gr.Image("images/logo1.png", show_label=False, width=600, show_download_button=False, show_fullscreen_button=False, show_share_button=False)
         
         gr.Markdown(
         """
@@ -302,22 +302,26 @@ with gr.Blocks(theme=theme) as demo:
         The industry is undergoing a profound transformation due to the development of Large Language Models (LLMs) and the recent advancements that enable them to access external tools. 
         For years, companies have leveraged simulation tools to accelerate and reduce the costs of product development. 
         One of the primary challenges in the coming years will be to create agents capable of setting up, running, and processing simulations to further expedite innovation.
+        Engineers will focus on analysis rather than simulation setup, allowing them to concentrate on the most critical aspects of their work.
 
         # Objective
 
-        This project represents an initial step towards developing AI agents that can perform simulations using existing engineer softwares. It enables engineers to focus on analysis rather than setup.
+        This project represents a first step towards developing AI agents that can perform simulations using existing engineering softwares. 
         Key domains of application include:
         - **CFD** (Computational Fluid Dynamics) simulations
         - **Biology** (Protein Folding, Molecular Dynamics, etc.)
         - **Neural network applications**
 
-        While this project focuses on protein folding, the principles employed can be extended to other domains. 
-        Specifically, it utilizes [Chai-1](https://www.chaidiscovery.com/blog/introducing-chai-1), a multi-modal foundation model for molecular structure prediction that achieves state-of-the-art performance across various benchmarks. 
+        While this project focuses on biomolecules folding, the principles employed can be extended to other domains. 
+        Specifically, it uses [Chai-1](https://www.chaidiscovery.com/blog/introducing-chai-1), a multi-modal foundation model for molecular structure prediction that achieves state-of-the-art performance across various benchmarks. 
         Chai-1 enables unified prediction of proteins, small molecules, DNA, RNA, glycosylations, and more. 
 
-        Industrial computations are frequently performed on High-Performance Computing (HPC) clusters with substantial resources, necessitating that simulations typically run on separate servers. 
-        To provide comprehensive answers to users, the LLM must be able to access simulation results. To this end, [Modal Labs](https://modal.com/), a serverless platform that offers a straightforward method to run any application with the latest CPU and GPU hardware, will be used.
+        Industrial computations frequently require substantial resources (large number of CPUs and GPUs) that are performed on High-Performance Computing (HPC) clusters. 
+        To this end, [Modal Labs](https://modal.com/), a serverless platform that offers a straightforward method to run any application with the latest CPU and GPU hardware, will be used.
         
+        MCP servers are an efficient solution to connect LLMs to real world engineering applications by providing access to a set of tools. 
+        The purpose of this project is to enable users to run biomolecule folding simulations using the Chai-1 model through any LLM chat or with a Gradio interface.
+                
         # Benefits
 
         1. **Efficiency**: The MCP server's connected to high-performance computing capabilities ensure that simulations are run quickly and efficiently.
@@ -326,7 +330,7 @@ with gr.Blocks(theme=theme) as demo:
 
         3. **Integration**: The seamless integration between the LLM's chat interface and the MCP server allows for a streamlined workflow, from simulation setup to results analysis.
         
-        The following video illustrates a practical use of the MCP server to run a protein folding simulation using the Chai-1 model. 
+        The following video illustrates a practical use of the MCP server to run a biomolecule folding simulation using the Chai-1 model. 
         In this scenario, Copilot is used in Agent mode with Claude 3.5 Sonnet to leverage the tools provided by the MCP server.
 
         """
@@ -348,10 +352,10 @@ with gr.Blocks(theme=theme) as demo:
         gr.Markdown(
         """
         # MCP tools
-        1. `create_fasta_file`: Create a FASTA file from a protein sequence string with a unique name.
+        1. `create_fasta_file`: Create a FASTA file from a biomolecule sequence string with a unique name.
         2. `create_json_config`: Create a JSON configuration file from the Gradio interface inputs.
         3. `compute_Chai1`: Compute a Chai-1 simulation on Modal labs server. Return a DataFrame with protein scores.
-        4. `plot_protein`: Plot the 3D structure of a protein using the DataFrame from `compute_Chai1` (Use for Gradio interface).
+        4. `plot_protein`: Plot the 3D structure of a biomolecule using the DataFrame from `compute_Chai1` (Use for Gradio interface).
         5. `show_cif_file`: Plot a 3D structure from a CIF file with the Molecule3D library (Use for the Gradio interface).
         """)
         
@@ -366,15 +370,15 @@ with gr.Blocks(theme=theme) as demo:
         The simulation was run with the default configuration and the image is 3D view from the Gradio interface.
         """)    
         
-        gr.Image("images/protein.png", show_label=True, width=400, label="Protein Folding example", show_download_button=False, show_fullscreen_button=False)
+        gr.Image("images/protein.png", show_label=True, width=400, label="Protein Folding example", show_download_button=False, show_fullscreen_button=False, show_share_button=False)
     
         gr.Markdown(
         """
         # What's next?
-        1. Expose additional tools to post-process the results of the simulations. 
-        The current post-processong tools are suited for the Gradio interface (ex: Plot images of the molecule structure from a file).
+        1. Expose additional tools to post-process the results of the simulations (ex: Plot images of the molecule structure from a file). 
+        The current post-processing tools are suited for the Gradio interface.
         2. Continue the pipeline by adding softawres like [OpenMM](https://openmm.org/) or [Gromacs](https://www.gromacs.org/) for molecular dynamics simulations.
-        3. Perform complete simulation plans including loops over parameters fully automated by the LLM.
+        3. Perform full simulation plans including loops over parameters fully automated by the LLM.
         
         # Contact
         For any issues or questions, please contact the developer or refer to the documentation.

introduction_page.md

@@ -60,7 +60,7 @@ CCCCCCCCCCCCCC(=O)O
 </div>
 <small>For a peptide, use `protein` as the molecule type.</small>
 
-**Other example:**
+**Other examples:**
 <div style="background-color:#ffffff; border-radius:8px; padding:18px 24px; margin-bottom:24px; border:1px solid #cccccc;">
 
 ```fasta
@@ -68,6 +68,12 @@ CCCCCCCCCCCCCC(=O)O
 MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPDLNAAKSELDKAIGRNCNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRCAAINQVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPDRAKRVITTFRTGTWDAYKNL
 ```
 
+```fasta
+>rna|Chain B
+UUAGGCGGCCACAGCGGUGGGGUUGCCUCCCGUACCCAUCCCGAACACGGAAGAUAAGCCCACCAGCGUUCCGGGGAGUACUGGAGUGCGCGAGCCUCUGGGAAACCCGGUUCGCCGCCACC
+MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPDLNAAKSELDKAIGRNCNGVITKDEAEKLFNQDVDAAVRGILRNAKLKPVYDSLDAVRRCAAINQVFQMGETGVAGFTNSLRMLQQKRWDEAAVNLAKSRWYNQTPDRAKRVITTFRTGTWDAYKNL
+```
+
 </div>
 
 ### 3. <span style="color:#e98935;">Select your config and FASTA files</span>
@@ -77,7 +83,7 @@ In the `Run folding simulation 🚀` window, refresh the file list by clicking o
 
 ### 4. <span style="color:#e98935;">Run the simulation</span>
 
-Press the `Run Simulation` button to start de folding Simulation. Five proteins folding simulations will be performed. This parameter is hard coded in Chai-1. The simulation time is expected to be from 2min to 10min depending on the molecule.
+Press the `Run Simulation` button to start the folding simulation. Five biomolecules folding simulations will be performed. This parameter is hard coded in Chai-1. The simulation time is expected to be from 2min to 10min depending on the molecule.
 
 ### 5. <span style="color:#e98935;">Analyse the results of your simulation</span>