Update app.py

app.py

@@ -1,41 +1,32 @@
 import streamlit as st
+import fitz  # PyMuPDF
 from huggingface_hub import snapshot_download
-from pdf2image import convert_from_bytes
-from PIL import Image
-import torch
 import os
+from pdf2image import convert_from_path
+from PIL import Image
+import tempfile
 
-st.set_page_config(page_title="PDF Extract Kit QA", layout="centered")
-
-@st.cache_resource
-def load_model():
-    model_dir = snapshot_download(repo_id="opendatalab/pdf-extract-kit-1.0", local_dir="./pdf_model", max_workers=4)
-    # TODO: Load model from model_dir using correct logic, e.g.:
-    # model = torch.load(os.path.join(model_dir, "model.pt"))
-    # return model
-    return model_dir  # TEMP placeholder
+# Download the model if not already downloaded
+model_dir = "./pdf-extract-kit"
+if not os.path.exists(model_dir):
+    snapshot_download(repo_id="opendatalab/pdf-extract-kit-1.0", local_dir=model_dir, max_workers=20)
 
-model_or_dir = load_model()
+st.title("PDF Table Extractor with PDF-Extract-Kit-1.0")
 
-def extract_answer(image, question):
-    # TODO: Implement the actual inference using the model
-    # For now, we return a placeholder
-    return "Answering is not implemented yet. Replace this with model inference."
+uploaded_file = st.file_uploader("Upload a PDF", type=["pdf"])
 
-st.title("📄 PDF Extract Kit: Question Answering")
+if uploaded_file:
+    st.write("Converting PDF to images...")
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_pdf:
+        tmp_pdf.write(uploaded_file.read())
+        tmp_pdf_path = tmp_pdf.name
 
-uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
-question = st.text_input("Ask a question about the document")
+    images = convert_from_path(tmp_pdf_path)
 
-if uploaded_file and question:
-    st.write("Reading and converting PDF...")
-    images = convert_from_bytes(uploaded_file.read(), dpi=200)
+    for i, img in enumerate(images):
+        st.image(img, caption=f"Page {i+1}", use_column_width=True)
 
-    page_number = st.number_input("Select page", min_value=1, max_value=len(images), value=1, step=1)
-    page_image = images[page_number - 1]
-    st.image(page_image, caption=f"Page {page_number}")
+        # Here you would call the table detection model on each image
+        st.info("🛠 Table detection model would run here... (to be implemented)")
 
-    with st.spinner("Finding answer..."):
-        answer = extract_answer(page_image, question)
-        st.success("Answer:")
-        st.write(answer)
+    st.success("Done processing PDF!")