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import sys
import os
# ✅ Add src to Python path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))
from txagent.txagent import TxAgent # ✅ Now this will work
import pandas as pd
import pdfplumber
import gradio as gr
def extract_structured_text_from_csv(file_path):
try:
df = pd.read_csv(file_path)
relevant_columns = [
"Booking Number", "Form Name", "Form Item",
"Item Response", "Interviewer", "Interview Date"
]
df = df[[col for col in relevant_columns if col in df.columns]]
return df.to_string(index=False)
except Exception as e:
return f"Error parsing CSV: {e}"
def extract_structured_text_from_pdf(file_path):
extracted = []
try:
with pdfplumber.open(file_path) as pdf:
for page in pdf.pages:
tables = page.extract_tables()
for table in tables:
for row in table:
if any(row):
extracted.append("\t".join([cell or "" for cell in row]))
return "\n".join(extracted)
except Exception as e:
return f"Error parsing PDF: {e}"
def create_ui(agent: TxAgent):
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("<h1 style='text-align: center;'>\ud83d\udc8a TxAgent: Therapeutic Reasoning</h1>")
chatbot = gr.Chatbot(label="TxAgent", height=600, type="messages")
file_upload = gr.File(label="Upload Medical File", file_types=[".pdf", ".txt", ".docx", ".jpg", ".png", ".csv"], file_count="multiple")
message_input = gr.Textbox(placeholder="Ask a biomedical question or just upload the files...", show_label=False)
send_button = gr.Button("Send", variant="primary")
conversation_state = gr.State([])
def handle_chat(message, history, conversation, uploaded_files):
context = (
"You are a clinical AI reviewing patient form data from interviews. "
"Your task is to analyze the responses, dates, and items, and reason step-by-step about "
"what the doctor might have overlooked. Do not summarize or answer yet — just reason step-by-step first."
)
if uploaded_files:
extracted_text = ""
for file in uploaded_files:
path = file.name
if path.endswith(".csv"):
extracted_text += extract_structured_text_from_csv(path) + "\n"
elif path.endswith(".pdf"):
extracted_text += extract_structured_text_from_pdf(path) + "\n"
message = f"{context}\n\n---\n{extracted_text.strip()}\n---\n\nNow reason what the doctor might have missed."
generator = agent.run_gradio_chat(
message=message,
history=history,
temperature=0.3,
max_new_tokens=1024,
max_token=8192,
call_agent=False,
conversation=conversation,
uploaded_files=uploaded_files,
max_round=30
)
for update in generator:
yield update
inputs = [message_input, chatbot, conversation_state, file_upload]
send_button.click(fn=handle_chat, inputs=inputs, outputs=chatbot)
message_input.submit(fn=handle_chat, inputs=inputs, outputs=chatbot)
gr.Examples([
["Upload the files"],
], inputs=message_input)
return demo |