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os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
import gradio as gr
import requests
# =============================
# BACKEND CONFIG
# =============================
BACKEND_URL = "http://127.0.0.1:8000"
RAG_URL = f"{BACKEND_URL}/rag/ask"
UPLOAD_URL = f"{BACKEND_URL}/upload-pdf/"
VISUALIZE_URL = f"{BACKEND_URL}/visualize/"
GRADE_URL = f"{BACKEND_URL}/grade-annotation/"
# =============================
# π¬ RAG CHAT
# =============================
def ask_question(message, history):
if history is None:
history = []
try:
r = requests.post(
RAG_URL,
json={"question": message},
timeout=120,
)
r.raise_for_status()
data = r.json()
answer = data.get("answer", "β οΈ No answer returned.")
# Render images returned from backend
images_md = ""
for img in data.get("images", []):
images_md += f"\n\n"
history.append({
"role": "user",
"content": message
})
history.append({
"role": "assistant",
"content": answer + images_md
})
return "", history
except Exception as e:
history.append({
"role": "assistant",
"content": f"β Error: {str(e)}"
})
return "", history
# =============================
# π UPLOAD PDF
# =============================
def upload_pdf(file):
if file is None:
return "β οΈ Please upload a PDF."
try:
with open(file.name, "rb") as f:
r = requests.post(
UPLOAD_URL,
files={"file": f},
timeout=300,
)
r.raise_for_status()
return r.json().get("message", "β
Uploaded")
except Exception as e:
return f"β {str(e)}"
# =============================
# π§ VISUALIZE ANATOMY
# =============================
def visualize(image, question):
if image is None:
return "β οΈ Upload an image.", None
try:
with open(image, "rb") as f:
r = requests.post(
VISUALIZE_URL,
files={"image": f},
data={"question": question},
timeout=120,
)
r.raise_for_status()
data = r.json()
output_image_url = None
if data.get("output_image"):
output_image_url = f"{BACKEND_URL}/{data['output_image']}"
return data.get("answer", ""), output_image_url
except Exception as e:
return f"β {str(e)}", None
# =============================
# π GRADE ANNOTATION
# =============================
def grade(image):
if image is None:
return "β οΈ Upload an image."
try:
with open(image, "rb") as f:
r = requests.post(
GRADE_URL,
files={"file": f},
timeout=120,
)
r.raise_for_status()
return r.json().get("result", "No result returned.")
except Exception as e:
return f"β {str(e)}"
# ==================================================
# π¨ UI
# ==================================================
with gr.Blocks() as demo:
gr.Markdown("# π§ Multimodal RAG Anatomy Tutor")
# ================= CHAT =================
with gr.Tab("π¬ Chat"):
chatbot = gr.Chatbot(height=500)
msg = gr.Textbox(
placeholder="Ask a question...",
show_label=False
)
msg.submit(
ask_question,
[msg, chatbot],
[msg, chatbot],
show_progress=True
)
# ================= UPLOAD =================
with gr.Tab("π Upload PDF"):
file = gr.File(file_types=[".pdf"])
upload_btn = gr.Button("Upload & Ingest")
upload_output = gr.Textbox()
upload_btn.click(
upload_pdf,
file,
upload_output,
show_progress=True
)
# ================= VISUALIZE =================
with gr.Tab("π§ Visualize Anatomy"):
img = gr.Image(type="filepath")
q = gr.Textbox(label="Ask about the image")
viz_btn = gr.Button("Visualize")
viz_answer = gr.Textbox(label="Answer")
viz_image = gr.Image(label="Annotated Image")
viz_btn.click(
visualize,
[img, q],
[viz_answer, viz_image],
show_progress=True
)
# ================= GRADE =================
with gr.Tab("π Annotation Grading"):
grade_img = gr.Image(type="filepath")
grade_btn = gr.Button("Grade")
grade_result = gr.Textbox(lines=15)
grade_btn.click(
grade,
grade_img,
grade_result,
show_progress=True
)
# =============================
# π LAUNCH
# =============================
demo.queue().launch() |