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README.md

@@ -1,12 +1,3 @@
----
-license: mit
-title: Med-Copilot
-sdk: streamlit
-python_version: 3.10.0
-short_description: perform an AI based analysis of medications
-app_file: med_streamlit.py
-sdk_version: 1.42.2
----
 # Medication research CoPilot
 
 ## Introduction
@@ -38,4 +29,5 @@ streamlit run med_streamlit.py
 
 ## Prompt
 Note that the default system prompt can be found [here](med_streamlit.py). 
-Consider modifying the prompt to better suit your needs, for example for a specific disease or condition.
+Consider modifying the prompt to better suit your needs, for example for a specific disease or condition.
+

medication_copilot.py

@@ -0,0 +1,228 @@
+from functools import partial
+import gradio as gr
+
+from src.gradio_utils import (
+    extract_table_from_chat,
+    upload_file,
+    redo,
+    undo,
+    edit_or_save_changes,
+    update_llm_selection,
+)
+from src.llm_calls import query_llm
+from src.data_handler import generate_excel_base64
+
+SYSTEM_PROMPT = """You are a pharmacology assistant specialized in analyzing and structuring medical data.
+
+Inputs You Receive:
+A JSON dataset representing medications for Retinitis Pigmentosa
+
+A user query requesting additional details to be added to the dataset
+
+Your Task:
+Analyze the dataset and determine what new information is needed
+
+Research and generate new details based on the user’s request
+
+Enhance the dataset by adding the requested information
+
+Ensure completeness: The updated dataset must always include all medications
+
+Your Output:
+A succinct response explaining your findings and how the dataset was extended
+
+A fully updated JSON dataset, strictly following this format:
+
+json
+Copy
+Edit
+{
+  "Medications": [
+    {"Name": "Medication Name", "key1": "value1", "key2": "value2", ...},
+    {"Name": "Medication Name", "key1": "value1", "key2": "value2", ...}
+  ]
+}
+Key Requirements:
+- JSON output is mandatory in every response
+- All medications must be present in the JSON, even if unchanged
+- Extend the dataset with newly generated information—do not just retrieve existing data
+- No repetition of the example JSON—only return the updated data
+- Verify the JSON before responding to ensure it is well-formed and complete
+
+Always structure your response clearly:
+
+- Text Summary: Explanation of findings and dataset extensions
+
+- Updated JSON Dataset: Full dataset with all medications, including new information
+
+- References & Sources (if applicable)
+"""
+
+
+with gr.Blocks(theme=gr.themes.Glass()) as app:
+    df_before = gr.State([])  # Undo history
+    df_state = gr.State(None)  # Current DataFrame
+    df_after = gr.State([])  # Redo history
+    last_response = gr.State("")  # Store last LLM response
+    edit_mode = gr.State("Edit")  # Track edit mode
+    base64data = gr.State(None)
+
+    with gr.Sidebar():
+        gr.Markdown("### Configuration")
+        llm_type = gr.Radio(
+            choices=["OpenAI", "Perplexity"], label="LLM Type", value="OpenAI"
+        )
+
+        api_key = gr.Textbox(
+            label="OpenAI API Key", placeholder="Enter OpenAI API Key", interactive=True
+        )
+
+        gr.Markdown("### Upload existing data")
+        file_upload = gr.File(label="Upload Excel File", file_types=[".xlsx"])
+        gr.Markdown("### Download table to Excel")
+        excel_output = gr.State(None)
+
+        excel_data = gr.Textbox(visible=False)
+        download_button = gr.DownloadButton(label="Download dataset")
+
+        download_button.click(
+            generate_excel_base64, inputs=[df_state], outputs=[excel_data]
+        )
+
+        excel_data.change(
+            None,
+            [excel_data],
+            None,
+            js="""
+        (base64Data) => {
+            const binaryString = atob(base64Data);
+            const len = binaryString.length;
+            const bytes = new Uint8Array(len);
+            for (let i = 0; i < len; i++) {
+                bytes[i] = binaryString.charCodeAt(i);
+            }
+            const blob = new Blob([bytes], { type: "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" });
+
+            const link = document.createElement("a");
+            link.href = URL.createObjectURL(blob);
+            link.download = "dataset.xlsx";
+            document.body.appendChild(link);
+            link.click();
+            document.body.removeChild(link);
+        }
+        """,
+        )
+
+        llm_type.change(update_llm_selection, inputs=[llm_type], outputs=[api_key])
+        with gr.Accordion("System Prompt", open=False):
+            system_prompt_box = gr.Textbox(
+                value=SYSTEM_PROMPT, interactive=True, lines=10, label="System Prompt"
+            )
+
+    gr.Markdown("## Medications Data CoPilot")
+    # Chat Interface
+    chat = gr.ChatInterface(
+        fn=query_llm,
+        type="messages",
+        description="Chat with an LLM to create a data representation of medications.",
+        stop_btn=False,
+        save_history=False,
+        additional_inputs=[df_state, llm_type, api_key, system_prompt_box],
+        examples=[
+            [
+                "List 10 medications that are known to be effective for Retinitis Pigmentosa"
+            ],
+            [
+                "Add a column specifying if the medication passes the Retinal Blood Barrier"
+            ],
+            ["Add the safety profile for each medication"],
+            ["Create four columns, each specifying each of the ADME profile factors"],
+            [
+                "Categorize each column into up to five categories, for simple classification."
+            ],
+        ],
+    )
+    with gr.Row():
+        gr.Markdown("### Medications Table")
+    with gr.Row():
+        update_button = gr.Button(
+            "Update table using the chat information", scale=8, interactive=True
+        )
+    with gr.Row():
+        dataframe_display = gr.DataFrame(interactive=False)
+    with gr.Row():
+        prev_button = gr.Button("<-", interactive=False, scale=1)
+        edit_save_button = gr.Button("Edit", interactive=True, scale=2)
+        next_button = gr.Button("->", interactive=False, scale=1)
+
+    # Save user changes
+    edit_save_button.click(
+        edit_or_save_changes,
+        inputs=[dataframe_display, df_before, df_state, df_after, edit_mode],
+        outputs=[
+            dataframe_display,  # Updated DataFrame
+            df_before,  # Undo history
+            df_state,  # Current state
+            df_after,  # Redo history
+            prev_button,  # Update prev button
+            next_button,  # Update next button
+            dataframe_display,  # Update DataFrame interactivity
+            edit_save_button,  # Update button label
+            edit_mode,  # Update edit mode
+        ],
+    )
+    # Undo button
+    prev_button.click(
+        undo,
+        inputs=[df_before, df_state, df_after],
+        outputs=[
+            dataframe_display,
+            df_before,
+            df_state,
+            df_after,
+            prev_button,
+            next_button,
+        ],
+    )
+    # Redo button
+    next_button.click(
+        redo,
+        inputs=[df_before, df_state, df_after],
+        outputs=[
+            dataframe_display,
+            df_before,
+            df_state,
+            df_after,
+            prev_button,
+            next_button,
+        ],
+    )
+    # File upload event
+    file_upload.change(
+        upload_file,
+        inputs=[file_upload, df_before, df_state, df_after],
+        outputs=[
+            dataframe_display,
+            df_before,
+            df_state,
+            df_after,
+            prev_button,
+            next_button,
+        ],
+    )
+    # Update button copies chat history to text box
+    update_button.click(
+        partial(extract_table_from_chat, key="Medications"),
+        inputs=[chat.chatbot, df_before, df_state, df_after, llm_type, api_key],
+        outputs=[
+            dataframe_display,
+            df_before,
+            df_state,
+            df_after,
+            prev_button,
+            next_button,
+        ],
+    )
+
+# Launch App
+app.launch()

requirements.txt

@@ -1,8 +1,9 @@
 pandas
-streamlit
+gradio
 openai
 openpyxl
 xlsxwriter
 pydantic
 simplejson
-python-dotenv
+python-dotenv
+requests