Experimenting with user interface

Dockerfile

@@ -1,5 +1,3 @@
-# Trying to put qdrant into same container as chainlit application
-
 # Start with qdrant image
 FROM qdrant/qdrant:latest 
 
@@ -8,31 +6,27 @@ RUN apt-get update && apt-get install -y \
     --no-install-recommends curl ca-certificates\
     && rm -rf /var/lib/apt/lists/*
 
-
-
-# Install uv
+# Install uv - it installs in /root/.local/bin
 ADD https://astral.sh/uv/install.sh /uv-installer.sh
 RUN sh /uv-installer.sh && rm /uv-installer.sh
 
 
-# Set te home directory and path
-# ENV HOME=/home/user \
-#     PATH="/home/user/.local/bin:/root/.local/bin/:$PATH"
-
-
-ENV PATH="/root/.local/bin:$PATH"
+# Set the home directory and path, need access to uv
+ENV HOME=/home/user \
+    PATH="/home/user/.local/bin:/root/.local/bin/:$PATH"
 
 # # NEEDED FOR CHAINLIT IN HUGGING FACE SPACES
 ENV UVICORN_WS_PROTOCOL=websockets
 
-# # Set the working directory
-# WORKDIR $HOME/app
-WORKDIR /app
+# Set the working directory
+WORKDIR $HOME/app
+
 
 # # Copy the app to the container
-# COPY --chown=user . $HOME/app
-COPY . /app
+COPY --chown=user . $HOME/app
+
 RUN chmod 777 entrypoint.sh
+
 # # Install the dependencies
 RUN uv sync --frozen
 

app.py

@@ -89,7 +89,7 @@ async def on_chat_start():
     )
     await msg.send()
 
-    chunk_size = 2000
+    chunk_size = 3000
     chunk_overlap = 200
 
     text_splitter = RecursiveCharacterTextSplitter(
@@ -161,32 +161,88 @@ async def on_chat_start():
 
     await msg.send()
     
-    # Now let's test the application to make a consent document
-    start_time = time.time()
-    # Brute force method that just saves each generated section as string
-    summary = rag_chain.invoke({"question":summary_query()})
-    background = rag_chain.invoke({"question":background_query()})
-    number_of_participants = rag_chain.invoke({"question":number_of_participants_query()})
-    study_procedures = rag_chain.invoke({"question":study_procedures_query()})
-    alt_procedures = rag_chain.invoke({"question":alt_procedures_query()})
-    risks = rag_chain.invoke({"question":risks_query()})
-    benefits = rag_chain.invoke({"question":benefits_query()})
-
-    end_time = time.time()
-    execution_time = end_time - start_time
-
-    msg = cl.Message(
-        content=f"""
-        Brute force (sequential) execution time: {execution_time:.2f} seconds.
-        {summary}
-        {background}  
-        {number_of_participants} 
-        {study_procedures}
-        {alt_procedures}
-        {risks}
-        {benefits}
-        """
-
-    )
+        # Sending an action button within a chatbot message
+    actions = [
+        cl.Action(
+            name="summary_button",
+            icon="mouse-pointer-click",
+            payload={"value": "summary"},
+            label="Write summary"
+        ),
+        cl.Action(
+            name="risk_button",
+            icon="mouse-pointer-click",
+            payload={"value": "risks"},
+            label="Write risk section"
+        ),
+        cl.Action(
+            name="benefits_button",
+            icon="mouse-pointer-click",
+            payload={"value": "benefits"},
+            label="Write benefits section"
+        ),
+        cl.Action(
+            name="file_button",
+            icon="mouse-pointer-click",
+            payload={"value": "markdown"},
+            label="Create final file"
+        )
+    ]
+    await cl.Message(content="Select consent form sections:", actions=actions).send()
+
+    @cl.action_callback("summary_button")
+    async def on_action(action: cl.Action):
+        summary = rag_chain.invoke({"question":summary_query()})
+        await cl.Message(content=summary).send()
+        await cl.Message(content=f"Executed {action.payload["value"]}").send()
+        # await action.remove()
+
+    @cl.action_callback("risk_button")
+    async def on_action(action: cl.Action):
+        risks = rag_chain.invoke({"question":risks_query()})
+        await cl.Message(content=risks).send()
+        await cl.Message(content=f"Executed {action.payload["value"]}").send()
+        # await action.remove()
+
+    @cl.action_callback("benefits_button")
+    async def on_action(action: cl.Action):
+        benefits = rag_chain.invoke({"question":benefits_query()})
+        await cl.Message(content=benefits).send()
+        await cl.Message(content=f"Executed {action.payload["value"]}").send()
+        # await action.remove()
+
+    # @cl.action_callback("file_button")
+    # async def on_action(action: cl.Action):
+    #     await cl.Message(content=f"Executed {action.payload["value"]}").send()
+    #     await action.remove()
+
+
+    # # Now let's test the application to make a consent document
+    # start_time = time.time()
+    # # Brute force method that just saves each generated section as string
+    # summary = rag_chain.invoke({"question":summary_query()})
+    # background = rag_chain.invoke({"question":background_query()})
+    # number_of_participants = rag_chain.invoke({"question":number_of_participants_query()})
+    # study_procedures = rag_chain.invoke({"question":study_procedures_query()})
+    # alt_procedures = rag_chain.invoke({"question":alt_procedures_query()})
+    # risks = rag_chain.invoke({"question":risks_query()})
+    # benefits = rag_chain.invoke({"question":benefits_query()})
+
+    # end_time = time.time()
+    # execution_time = end_time - start_time
+
+    # msg = cl.Message(
+    #     content=f"""
+    #     Brute force (sequential) execution time: {execution_time:.2f} seconds.
+    #     {summary}
+    #     {background}  
+    #     {number_of_participants} 
+    #     {study_procedures}
+    #     {alt_procedures}
+    #     {risks}
+    #     {benefits}
+    #     """
+
+    # )
    
-    await msg.send() 
+    # await msg.send() 

control.py

@@ -0,0 +1,53 @@
+import chainlit as cl
+
+@cl.on_chat_start
+async def start():
+    # Sending an action button within a chatbot message
+    actions = [
+        cl.Action(
+            name="summary_button",
+            icon="mouse-pointer-click",
+            payload={"value": "summary"},
+            label="Write summary"
+        ),
+        cl.Action(
+            name="risk_button",
+            icon="mouse-pointer-click",
+            payload={"value": "risks"},
+            label="Write risk section"
+        ),
+        cl.Action(
+            name="benefits_button",
+            icon="mouse-pointer-click",
+            payload={"value": "benefits"},
+            label="Write benefits section"
+        ),
+        cl.Action(
+            name="file_button",
+            icon="mouse-pointer-click",
+            payload={"value": "markdown"},
+            label="Create final file"
+        )
+    ]
+
+    await cl.Message(content="Select consent form sections:", actions=actions).send()
+
+@cl.action_callback("summary_button")
+async def on_action(action: cl.Action):
+       await cl.Message(content=f"Executed {action.payload["value"]}").send()
+       await action.remove()
+
+@cl.action_callback("risk_button")
+async def on_action(action: cl.Action):
+       await cl.Message(content=f"Executed {action.payload["value"]}").send()
+       await action.remove()
+
+@cl.action_callback("benefits_button")
+async def on_action(action: cl.Action):
+       await cl.Message(content=f"Executed {action.payload["value"]}").send()
+       await action.remove()
+
+@cl.action_callback("file_button")
+async def on_action(action: cl.Action):
+       await cl.Message(content=f"Executed {action.payload["value"]}").send()
+       await action.remove()

defaults.py

@@ -6,4 +6,4 @@ OPENAI_API_KEY = os.getenv("NEWKEY")
 default_embedding_model = OpenAIEmbeddings(model="text-embedding-3-small", api_key=OPENAI_API_KEY)
 default_location = ":memory:"
 default_url = "http://localhost:6333"
-default_llm = ChatOpenAI(model="gpt-4o", api_key=OPENAI_API_KEY, streaming=True, temperature=0)
+default_llm = ChatOpenAI(model="gpt-4o", api_key=OPENAI_API_KEY, streaming=True, temperature=0.2)

entrypoint.sh

@@ -1,33 +1,5 @@
 #!/bin/bash
-echo "Current directory: $(pwd)"
-echo "Current path: $PATH"
 
-
-
-# Specify the starting directory (e.g., root or home)
-start_directory="/"
-
-# Use find to locate 'uv'
-found=$(find "$start_directory" -name "uv" 2>/dev/null)
-
-if [ -n "$found" ]; then
-    echo "'uv' found at:"
-    echo "$found"
-else
-    echo "'uv' not found in $start_directory or its subdirectories"
-fi
-
-
-directory="/"
-# directory="../root/.local/bin"
-if [ -d "$directory" ];
-then
-    echo "Directory exists: $directory"
-    echo "Files in $directory:"
-    find "$directory" -type f
-else
-    echo "Directory does not exist: $directory"
-fi
 # Start Qdrant in the background
 echo "Starting Qdrant server..."
 /qdrant/qdrant  &

queries.py

@@ -126,6 +126,8 @@ def risks_query():
 
     All the details of study risks should be specific to this protocol.
 
+    You must complete this task as it is a required part of the document.
+
     """
     return risks_query