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import sys
import os
import pandas as pd
import pdfplumber
import gradio as gr
# ✅ 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
def extract_all_text_from_csv_or_excel(file_path, progress=None, index=0, total=1):
try:
if file_path.endswith(".csv"):
df = pd.read_csv(file_path, low_memory=False)
elif file_path.endswith((".xls", ".xlsx")):
df = pd.read_excel(file_path)
else:
return f"Unsupported spreadsheet format: {file_path}"
if progress:
progress((index + 1) / total, desc=f"Processed table: {os.path.basename(file_path)}")
return df.to_string(index=False)
except Exception as e:
return f"Error parsing file: {e}"
def extract_all_text_from_pdf(file_path, progress=None, index=0, total=1):
extracted = []
try:
with pdfplumber.open(file_path) as pdf:
num_pages = len(pdf.pages)
for i, page in enumerate(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]))
if progress:
progress((index + i / num_pages) / total, desc=f"Parsing PDF: {os.path.basename(file_path)} ({i+1}/{num_pages})")
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;'>📋 CPS: Clinical Patient Support System</h1>")
chatbot = gr.Chatbot(label="CPS Assistant", height=600, type="messages")
file_upload = gr.File(
label="Upload Medical File",
file_types=[".pdf", ".txt", ".docx", ".jpg", ".png", ".csv", ".xls", ".xlsx"],
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, progress=gr.Progress()):
context = (
"You are an advanced clinical reasoning AI. You have just received raw medical data extracted from patient forms, lab reports, or interview tables. "
"Your goal is to analyze this data like a clinical expert. Go step by step to detect patterns, spot unusual or missing info, and identify clinical red flags "
"or overlooked findings. Use medically grounded reasoning. Be detailed. At the end, explain what the doctor may have missed and why it matters. "
"Include examples, reference clinical logic, and suggest what should have been asked or done. This response will help improve real-world diagnostics."
)
if uploaded_files:
extracted_text = ""
total_files = len(uploaded_files)
for index, file in enumerate(uploaded_files):
path = file.name
if path.endswith((".csv", ".xls", ".xlsx")):
extracted_text += extract_all_text_from_csv_or_excel(path, progress, index, total_files) + "\n"
elif path.endswith(".pdf"):
extracted_text += extract_all_text_from_pdf(path, progress, index, total_files) + "\n"
else:
extracted_text += f"(Uploaded file: {os.path.basename(path)})\n"
if progress:
progress((index + 1) / total_files, desc=f"Skipping unsupported file: {os.path.basename(path)}")
message = f"{context}\n\n---\n{extracted_text.strip()}\n---\n\nBegin your reasoning."
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 your medical form and ask what the doctor might’ve missed."],
["This patient was treated with antibiotics for UTI. What else should we check?"],
["Is there anything abnormal in the attached blood work report?"]
], inputs=message_input)
return demo
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