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