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import gradio as gr
from PyPDF2 import PdfReader
import docx
def extract_text_from_uploaded_file(file_path):
if file_path.endswith(".pdf"):
try:
reader = PdfReader(file_path)
return "\n".join(page.extract_text() or "" for page in reader.pages)
except Exception as e:
return f"[Error extracting PDF text: {e}]"
elif file_path.endswith(".txt"):
try:
with open(file_path, "r", encoding="utf-8") as f:
return f.read()
except Exception as e:
return f"[Error reading text file: {e}]"
elif file_path.endswith(".docx"):
try:
doc = docx.Document(file_path)
return "\n".join([p.text for p in doc.paragraphs])
except Exception as e:
return f"[Error reading DOCX: {e}]"
else:
return "[Unsupported file type for text extraction]"
def create_ui(agent):
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("<h1 style='text-align: center;'>💊 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"])
message_input = gr.Textbox(placeholder="Ask a biomedical question...", show_label=False)
send_button = gr.Button("Send", variant="primary")
conversation_state = gr.State([])
def handle_chat(message, history, conversation, uploaded_file):
file_text = ""
if uploaded_file:
file_text = extract_text_from_uploaded_file(uploaded_file.name)
# Append file text to the question
if file_text:
message += f"\n\n[Document Content Extracted from Upload]:\n{file_text}"
generator = agent.run_gradio_chat(
message=message,
history=history,
temperature=0.3,
max_new_tokens=1024,
max_token=8192,
call_agent=False,
conversation=conversation,
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(
examples=[
["Upload the files"],
],
inputs=message_input,
)
return demo
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