Delete med_streamlit.py

med_streamlit.py

@@ -1,277 +0,0 @@
-import os
-import json
-import io
-from typing import Dict, List
-
-import pandas as pd
-import streamlit as st
-from dotenv import load_dotenv
-from openai import OpenAI
-
-import llm_calls
-from llm_calls import validate_llm_response
-
-# Load environment variables
-load_dotenv()
-
-CONDITION_NAME = "Retinitis Pigmentosa (RP)"
-
-SYSTEM_PROMPT = f"""
-You are a medical assistant specialized in modifying structured medical data.
-You will receive JSON input representing a dataset of medications for {CONDITION_NAME}.
-
-Your task is to:
-- Answer user requests about the provided medication data
-- Either Add new columns or rows if requested, or modify existing ones
-- Provide references, explanations and additional remarks
-Always return only a JSON object with:
-- "dataset": updated dataset
-- "explanation": explanation of changes and additional information related to the findings. 
-Specify the change made for each medication
-- "references": References for findings, i.e. links to scientific papers or websites. 
-Specify which reference relates to which finding on each medication.
-
-Additional guidelines:
-1. Please respond in valid JSON format only.
-2. Make sure the JSON is valid, e.g. has no unterminated strings or missing commas.
-3. Ensure the response starts with `{{` and ends with `}}` without any trailing text.
-"""
-
-
-def update_dataframe(records: List[Dict] | pd.DataFrame):
-    """Update the DataFrame with new records. """
-    print(f"UPDATING DATAFRAME: {records}")
-    if isinstance(records, pd.DataFrame):
-        new_data = records
-    else:
-        new_data = pd.DataFrame(records)
-
-    st.session_state.df = new_data  # Assign the updated DataFrame
-    #st.rerun()  # Trigger a rerun
-
-def undo():
-    """Undo the last operation by restoring the previous DataFrame."""
-    if st.session_state.prev_df is not None:
-        st.session_state.df = st.session_state.prev_df
-        st.session_state.prev_df = None
-
-    if st.session_state.history:
-        st.session_state.history.pop()
-    if st.session_state.explanation:
-        st.session_state.explanation = "Changes undone."
-    if st.session_state.references:
-        st.session_state.references = ""
-
-
-# Page config
-st.set_page_config(layout="wide", page_title="RP Medication Analyzer")
-col1, col2 = st.columns([2, 18])
-col1.image("rp_logo.jpg", use_container_width=True)
-col2.title("Analyze RP Related Medications")
-
-# Sidebar for API Key settings
-with st.sidebar:
-    st.subheader("Select AI service")
-    llm_provider = st.radio(options=["Perplexity.ai", "OpenAI"], index=0, label="API")
-
-    api_key = None  # Initialize API key
-
-    if llm_provider == "OpenAI":
-        st.subheader("OpenAI API key")
-        api_base_input = st.text_input(
-            "Enter API Base (Leave empty to use env variable)",
-            value=os.environ.get("OPENAI_API_BASE", ""),
-        )
-        api_key_input = st.text_input(
-            "Enter API Key",
-            type="password",
-            value=os.environ.get("OPENAI_API_KEY", ""),
-        )
-
-        openai_api_base = api_base_input if api_base_input else os.environ.get("OPENAI_API_BASE")
-        api_key = api_key_input if api_key_input else os.environ.get("OPENAI_API_KEY")
-
-        # Validate API key presence
-        if not api_key:
-            st.error("🚨 OpenAI API key is required!")
-
-        openai_client = OpenAI(api_key=api_key)
-        openai_client.api_base = openai_api_base
-
-    elif llm_provider == "Perplexity.ai":
-        st.subheader("Perplexity.ai API key")
-        api_key_input = st.text_input(
-            "Enter API Key",
-            type="password",
-            value=os.environ.get("PERPLEXITY_API_KEY", ""),
-        )
-        api_key = api_key_input if api_key_input else os.environ.get("PERPLEXITY_API_KEY")
-
-        # Validate API key presence
-        if not api_key:
-            st.error("🚨 Perplexity.ai API key is required!")
-
-
-# Ensure session persistence
-if "df" not in st.session_state:
-    st.session_state.df = None
-if "uploaded_file" not in st.session_state:
-    st.session_state.uploaded_file = None
-if "explanation" not in st.session_state:
-    st.session_state.explanation = "No modifications yet."
-if "references" not in st.session_state:
-    st.session_state.references = "No additional references."
-if "last_prompt" not in st.session_state:
-    st.session_state.last_prompt = ""
-if "last_response" not in st.session_state:
-    st.session_state.last_response = {}
-if "history" not in st.session_state:
-    st.session_state.history = []  # Stores all past interactions
-if "prev_df" not in st.session_state:
-    st.session_state.prev_df = None  # Stores the previous DataFrame for undo
-
-
-# File uploader
-file = st.file_uploader("Upload an Excel file", type=["xlsx"])
-
-print(f"FILE: {file}")
-if file and file != st.session_state.uploaded_file:
-    try:
-        with pd.ExcelFile(file) as xls:
-            if "Metadata" in xls.sheet_names:
-                st.session_state.history = pd.read_excel(xls, sheet_name="Metadata").to_dict(orient="records")
-            if "Data" in xls.sheet_names:
-                data_df = pd.read_excel(xls, sheet_name="Data")
-                update_dataframe(data_df)
-            else:
-                st.error("🚨 No 'Data' sheet found in the uploaded file. Make sure the file has it")
-            print(f"History: {st.session_state.history}")
-        st.session_state.uploaded_file = file
-        print("File uploaded successfully!")
-        st.success("✅ File uploaded successfully!")
-    except Exception as e:
-        print(f"Error reading file: {e}")
-        st.error(f"🚨 Error reading file: {e}")
-
-
-if st.session_state.df is not None:
-    st.write("### Updated Dataset")
-    st.dataframe(st.session_state.df, use_container_width=True)
-else:
-    st.warning("⚠️ Upload a file to proceed.")
-
-# Explanation & remarks
-if st.session_state.explanation:
-    with st.expander("Explanation and remarks"):
-        st.info(st.session_state.explanation)
-if st.session_state.references:
-    with st.expander("References"):
-        st.warning(st.session_state.references)
-if st.session_state.last_prompt:
-    with st.expander("📜 Sent Prompt"):
-        st.code(st.session_state.last_prompt, language="plaintext")
-
-# if st.session_state.last_response:
-#     with st.expander("🧠 LLM Response (Raw)"):
-#         st.json(st.session_state.last_response)
-
-# User query input
-input_text = st.chat_input("Type your prompt here")
-
-# 🚨 Validate: Ensure both API key and dataset are present before making an API call
-if input_text:
-    if not api_key:
-        st.error("🚨 API key is missing! Please provide a valid key before proceeding.")
-    elif st.session_state.df is None:
-        st.error("🚨 No dataset uploaded! Please upload an Excel file.")
-    else:
-        # Convert dataframe to JSON for LLM processing
-        json_data = st.session_state.df.to_json(orient="records")
-        with st.spinner(f"Processing request: *{input_text}*..."):
-            response = None  # Ensure response is defined before use
-
-            # Call the appropriate LLM provider
-            if llm_provider == "OpenAI":
-                response = llm_calls.query_openai(
-                    system_prompt=SYSTEM_PROMPT,
-                    user_prompt=input_text,
-                    json_data=json_data,
-                    openai_client=openai_client,
-                )
-            elif llm_provider == "Perplexity.ai":
-                response = llm_calls.query_perplexity(
-                    system_prompt=SYSTEM_PROMPT,
-                    user_prompt=input_text,
-                    json_data=json_data,
-                    api_key=api_key,
-                )
-
-        # Ensure response exists before processing
-        if response:
-            st.session_state.prev_df = st.session_state.df
-            try:
-                parsed_response = validate_llm_response(response)
-
-                st.session_state.last_prompt = input_text
-                st.session_state.last_response = response  # Keep full JSON response
-
-                # Display structured output
-                if "error" in parsed_response:
-                    st.error(parsed_response["error"])
-                else:
-                    update_dataframe(parsed_response["dataset"])
-                    st.session_state.explanation = parsed_response["explanation"]
-                    st.session_state.references = parsed_response["references"]
-                    st.session_state.history.append({
-                        "Prompt": input_text,
-                        "Explanation": parsed_response["explanation"],
-                        "References": parsed_response["references"]
-                    })
-            except json.JSONDecodeError:
-                st.error("🚨 Error parsing response: Invalid JSON format.")
-            except Exception as e:
-                st.error(f"🚨 Unexpected error: {e}")
-
-            st.rerun()
-
-
-# 📥 Download Updated Excel
-if st.session_state.df is not None:
-    st.sidebar.subheader("Download Updated Dataset")
-
-    def generate_excel(dataframe, history):
-        output_stream = io.BytesIO()
-        with pd.ExcelWriter(output_stream, engine="xlsxwriter") as writer:
-            dataframe.to_excel(writer, index=False, sheet_name="Data")
-            # Convert history to DataFrame and save in a new sheet
-            if history:
-                history_df = pd.DataFrame(history)
-                history_df.to_excel(writer, index=False, sheet_name="Metadata")
-
-            workbook = writer.book
-
-            # Apply word wrapping
-            for sheet_name in ["Data", "Metadata"]:
-                if sheet_name in writer.sheets:
-                    worksheet = writer.sheets[sheet_name]
-                    wrap_format = workbook.add_format({"text_wrap": True, "align": "top", "valign": "top"})
-
-                    # Apply word wrap to all columns
-                    df_to_format = dataframe if sheet_name == "Data" else history_df
-                    for col_num, col_name in enumerate(df_to_format.columns):
-                        worksheet.set_column(col_num, col_num, 30, wrap_format)  # Adjust width if needed
-
-        output_stream.seek(0)
-        return output_stream
-
-
-    st.sidebar.download_button(
-        "📥 Download Excel File",
-        data=generate_excel(st.session_state.df, st.session_state.history),
-        file_name="updated_dataset.xlsx",
-        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-    )
-
-st.sidebar.subheader("Undo Changes")
-if st.sidebar.button("Undo", disabled=st.session_state.prev_df is None):
-    undo()