Upload MoDrAg_Chat_HFS.py

MoDrAg_Chat_HFS.py

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+import openai
+import os
+from openai import OpenAI
+import gradio as gr 
+from elevenlabs.client import ElevenLabs
+from elevenlabs import stream
+import base64
+
+api_key = os.getenv("OPENAI_API_KEY")
+eleven_key = os.getenv("eleven_key")
+
+elevenlabs = ElevenLabs(api_key=eleven_key)
+client = OpenAI(api_key=api_key)
+
+cafchem_tools = [
+		 {
+        "type" : "mcp",
+        "server_label":"cafiero-proteinagent",
+        "server_url":"https://cafierom-proteinagent.hf.space/gradio_api/mcp/",
+        "require_approval": "never",
+        "allowed_tools": ["ProteinAgent_ProteinAgent"],
+    },
+    {
+        "type" : "mcp",
+        "server_label":"cafiero-propagent",
+        "server_url":"https://cafierom-propagent.hf.space/gradio_api/mcp/",
+        "require_approval": "never",
+        "allowed_tools": ["PropAgent_PropAgent"],
+    },
+    {
+        "type" : "mcp",
+        "server_label":"cafiero-moleculeagent",
+        "server_url":"https://cafierom-moleculeagent.hf.space/gradio_api/mcp/",
+        "require_approval": "never",
+        "allowed_tools": ["MoleculeAgent_MoleculeAgent"],
+    },
+    {
+        "type" : "mcp",
+        "server_label":"cafiero-dockagent",
+        "server_url":"https://cafierom-dockagent.hf.space/gradio_api/mcp/",
+        "require_approval": "never",
+        "allowed_tools": ["DockAgent_DockAgent"],
+    },
+		 ]
+
+chat_history = []
+global last_id
+last_id = None
+
+def chat(prompt, tools, voice_choice):
+    chat_history.append(
+                    {"role": "user", "content": prompt}
+    )
+    global last_id
+
+    if tools == "Yes":
+        if (last_id != None):
+            response = client.responses.create(
+                        model = "o4-mini",
+                        tools = cafchem_tools,
+                        input=prompt,
+                        previous_response_id = last_id
+            )
+        else:
+            response = client.responses.create(
+                        model = "o4-mini",
+                        tools = cafchem_tools,
+                        input=prompt
+            )
+    else:
+        if (last_id != None):
+            response = client.responses.create(
+                        model = "o4-mini",
+        				input=prompt,
+                        previous_response_id = last_id
+            )
+        else:
+            response = client.responses.create(
+                    model = "o4-mini",
+    				input=prompt
+            )
+
+    chat_history.append(
+                    {"role": "assistant", "content": response.output_text}
+    )
+    last_id = response.id
+
+    if voice_choice == "On":
+        elita_text = response.output_text
+    
+        voice_settings = {
+              "stability": 0.37,
+              "similarity_boost": 0.90,
+              "style": 0.0,
+              "speed": 1.05
+              }
+    
+        audio_stream = elevenlabs.text_to_speech.convert(
+          text = elita_text,
+          voice_id = 'vxO9F6g9yqYJ4RsWvMbc',
+          model_id = 'eleven_multilingual_v2',
+          output_format='mp3_44100_128',
+          voice_settings=voice_settings
+        )
+    
+        audio_converted = b"".join(audio_stream)
+        audio = base64.b64encode(audio_converted).decode("utf-8")
+        audio_player = f'<audio src="data:audio/mpeg;base64,{audio}" controls autoplay></audio>'
+    else:
+        audio_player = ''
+    
+    return "", chat_history, audio_player
+
+def clear_history():
+	global chat_history
+	chat_history = []
+	global last_id
+	last_id = None
+
+def voice_from_file(file_name):
+  audio_file = file_name
+  with open(audio_file, 'rb') as audio_bytes:
+              audio = base64.b64encode(audio_bytes.read()).decode("utf-8")
+  audio_player = f'<audio src="data:audio/mpeg;base64,{audio}" controls autoplay></audio>'
+  return audio_player
+    
+def prot_workflow():
+  elita_text = "Starting with a protein, try searching for Uniprot IDs, followed by Chembl IDs. \
+Then you can look for bioactive molecules for each Chembl ID. You can also search for crystal structures \
+in the PDB and get titles of those structures, sequences, numbers of chains, and small molecules in the structure. \
+Generate novel bioactive molecules based on a protein Chembl ID using a GPT, or predict an IC50 for a molecule \
+based on a protein Chembl ID using a gradient-boosting model."
+  messages = [{'role': 'assistant', 'content': elita_text}]
+  audio_player = voice_from_file('protein_chat.mp3')
+  return audio_player, messages
+
+def mol_workflow():
+  elita_text = "Starting with a molecule, try finding it's Lipinski properties, or its pharmacophore similarity to a known active. \
+Find similar molecules or, if it has substituted rings, find analogues."
+  messages = [{'role': 'assistant', 'content': elita_text}]
+  audio_player = voice_from_file('mol_wf.mp3')
+  return audio_player, messages
+
+def combo_workflow():
+  elita_text ="Starting with a protein and a molecule, try docking the molecule in the protein. If you have a Chembl ID, predict the IC50 \
+value of the molecule in the protein."
+  messages = [{'role': 'assistant', 'content': elita_text}]
+  audio_player = voice_from_file('combo_wf.mp3')
+  return audio_player, messages
+
+def prot_accordions():
+  elita_text = 'Try queries like: find UNIPROT IDs for the protein MAOB; find PDB IDs for MAOB; how many chains \
+are in the PDB structure 4A7G; find PDB IDs matching the protein MAOB; list the bioactive molecules for the CHEMBL \
+ID CHEMBL2039; dock the molecule CCCC(F) in the protein DRD2; predict the IC50 value for CCCC(F) based on the CHEMBL \
+ID CHEMBL2039; or generate novel molecules based on the CHEMBL ID CHEMBL2039.'
+  messages = [{'role': 'assistant', 'content': elita_text}]
+  audio_player = voice_from_file('protein.mp3')
+  return audio_player, messages
+
+def mol_accordions():
+  elita_text = 'Try queries like: Find the name of CCCF, find the smiles for paracetamol, or find molecules similar to paracetamol.'
+  messages = [{'role': 'assistant', 'content': elita_text}]
+  audio_player = voice_from_file('mol.mp3')
+  return audio_player, messages 
+
+def prop_accordions():
+  elita_text = 'Try queries like: Find Lipinski properties for CCCF, find pharmacophore-similarity between \
+  CCCF and CCCBr, or generate analogues of c1ccc(O)cc1.'
+  messages = [{'role': 'assistant', 'content': elita_text}]
+  audio_player = voice_from_file('Props.mp3')
+  return audio_player, messages
+
+def dock_accordions():
+  elita_text = 'Try queries like: dock CCC(F) in the protein MAOB'
+  messages = [{'role': 'assistant', 'content': elita_text}]
+  audio_player = voice_from_file('Dock.mp3')
+  return audio_player, messages
+
+with gr.Blocks() as forest:
+  gr.Markdown(
+      """
+      # Chat with MoDrAg! OpenAI 04-mini can tap into Modrag through an MCP and use all of your favourite drug design tools.
+      - Currently using the tools below:
+      """)
+  with gr.Row():
+    with gr.Accordion("Protein Agent - Click to open/close.", open=False)as prot:
+      gr.Markdown('''
+                  - Find Uniprot IDs for a protein/gene name.
+                  - report the number of bioactive molecules for a protein, organized by Chembl ID.
+                  - report the SMILES and IC50 values of bioactive molecules for a particular Chembl ID.
+                  - find protein sequences, report number fo chains.
+                  - find small molecules present in a PDB structure.
+                  - find PDB IDs that match a protein.
+                  - predict the IC50 value of a small molecule based on a Chembl ID.
+                  - generate novel molecules based on a Chembl ID.
+      ''')
+    with gr.Accordion("Molecule Agent - Click to open/close.", open=False) as mol:
+      gr.Markdown('''
+                  - find the name of a molecule from the SMILES string.
+                  - find the SMILES string of a molecule from the name
+                  - find similar or related molecules with some basic properties from a name or SMILES.
+      ''')
+    with gr.Accordion("Property Agent - Click to open/close.", open=False) as prop:
+      gr.Markdown('''
+                  - calculate Lipinski properties from a SMILES string. 
+                  - find the pharmacophore-similarity between two molecules (a molecule and a reference).
+                  - generate analogues of ring molecules and report their QED values.
+      ''')
+    with gr.Accordion("Docking Agent - Click to open/close.", open=False) as dock:
+      gr.Markdown('''
+                  - Find the docking score and pose coordinates for a molecules defined by a SMILES string in on of the proteins below:
+                  - IGF1R,JAK2,KIT,LCK,MAPK14,MAPKAPK2,MET,PTK2,PTPN1,SRC,ABL1,AKT1,AKT2,CDK2,CSF1R,EGFR,KDR,MAPK1,FGFR1,ROCK1,MAP2K1,
+                  PLK1,HSD11B1,PARP1,PDE5A,PTGS2,ACHE,MAOB,CA2,GBA,HMGCR,NOS1,REN,DHFR,ESR1,ESR2,NR3C1,PGR,PPARA,PPARD,PPARG,AR,THRB,
+                  ADAM17,F10,F2,BACE1,CASP3,MMP13,DPP4,ADRB1,ADRB2,DRD2,DRD3,ADORA2A,CYP2C9,CYP3A4,HSP90AA1
+        ''')
+  with gr.Row():
+    molecule_workflow = gr.Button(value = "Sample Molecule Workflow")
+    protein_workflow = gr.Button(value = "Sample Protein Workflow")
+    combined_workflow = gr.Button(value = "Sample Combined Workflow")
+
+  with gr.Row():
+    tools = gr.Radio(choices = ["Yes", "No"],label="Use CafChem tools?",interactive=True, value = "Yes", scale = 2)
+    voice_choice = gr.Radio(choices = ['On', 'Off'],label="Audio Voice Response?", interactive=True, value='Off', scale = 2)
+  
+  chatbot = gr.Chatbot()
+
+  with gr.Row():
+    msg = gr.Textbox(label="Type your messages here and hit enter.", scale = 2)
+    chat_btn = gr.Button(value = "Send", scale = 0)
+  elitas_voice = gr.HTML() 
+  
+  clear = gr.ClearButton([msg, chatbot])
+  
+  chat_btn.click(chat, [msg, tools, voice_choice], [msg, chatbot, elitas_voice])
+  msg.submit(chat, [msg, tools, voice_choice], [msg, chatbot, elitas_voice])
+  mol.expand(mol_accordions, outputs = [talk_ele, chatbot])
+  prop.expand(prop_accordions, outputs = [talk_ele, chatbot])
+  prot.expand(prot_accordions, outputs = [talk_ele, chatbot])
+  dock.expand(dock_accordions, outputs = [talk_ele, chatbot])
+  molecule_workflow.click(mol_workflow, outputs = [talk_ele, chatbot])
+  protein_workflow.click(prot_workflow, outputs = [talk_ele, chatbot])
+  combined_workflow.click(combo_workflow, outputs = [talk_ele, chatbot])
+  clear.click(clear_history)
+
+if __name__ == "__main__":
+    forest.launch(debug=False, share=True)