app update

app.py

@@ -1,27 +1,41 @@
-import gradio as gr
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
+import streamlit as st
 from huggingface_hub import snapshot_download
+from pdf2image import convert_from_bytes
+from PIL import Image
+import torch
 import os
 
-# Step 1: Download the model
-model_dir = snapshot_download('opendatalab/PDF-Extract-Kit-1.0')
-
-# Step 2: Initialize pipeline
-pipe = pipeline(
-    task=Tasks.document_segmentation,
-    model=model_dir
-)
-
-# Step 3: Define inference function
-def extract_info_from_pdf(pdf_file):
-    result = pipe({'file': pdf_file.name})
-    return str(result)
-
-# Step 4: Gradio UI
-gr.Interface(
-    fn=extract_info_from_pdf,
-    inputs=gr.File(type="binary", label="Upload PDF"),
-    outputs="text",
-    title="PDF Extractor (PDF-Extract-Kit)"
-).launch()
+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
+
+model_or_dir = load_model()
+
+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."
+
+st.title("📄 PDF Extract Kit: Question Answering")
+
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+question = st.text_input("Ask a question about the document")
+
+if uploaded_file and question:
+    st.write("Reading and converting PDF...")
+    images = convert_from_bytes(uploaded_file.read(), dpi=200)
+
+    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}")
+
+    with st.spinner("Finding answer..."):
+        answer = extract_answer(page_image, question)
+        st.success("Answer:")
+        st.write(answer)