Initial Commit

.gitignore

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+venv
+*.swo
+*.swp
+*.pyc

README.md

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 ---
-title: Docquery
-emoji: 🌖
-colorFrom: blue
-colorTo: gray
+title: DocQuery
+emoji: 🦉
+colorFrom: gray
+colorTo: pink
 sdk: streamlit
 sdk_version: 1.10.0
 app_file: app.py
-pinned: false
-license: mit
+pinned: true
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import streamlit as st
+
+import torch
+from docquery.pipeline import get_pipeline
+from docquery.document import load_bytes
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipeline = get_pipeline(device=device)
+
+
+def process_document(file, question):
+    # prepare encoder inputs
+    document = load_document(file.name)
+    return pipeline(question=question, **document.context)
+
+
+def ensure_list(x):
+    if isinstance(x, list):
+        return x
+    else:
+        return [x]
+
+
+st.title("DocQuery: Query Documents Using NLP")
+file = st.file_uploader("Upload a PDF or Image document")
+question = st.text_input("QUESTION", "")
+
+document = None
+
+if file is not None:
+    col1, col2 = st.columns(2)
+
+    document = load_bytes(file, file.name)
+    col1.image(document.preview, use_column_width=True)
+
+if document is not None and question is not None and len(question) > 0:
+    predictions = pipeline(question=question, **document.context)
+
+    col2.header("Probabilities")
+    for p in ensure_list(predictions):
+        col2.subheader(f"{ p['answer'] }: { round(p['score'] * 100, 1)}%")
+
+
+"DocQuery uses LayoutLMv1 fine-tuned on DocVQA, a document visual question answering dataset, as well as SQuAD, which boosts its English-language comprehension. To use it, simply upload an image or PDF, type a question, and click 'submit', or click one of the examples to load them."
+
+"[Github Repo](https://github.com/impira/docquery)"

requirements.txt

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+torch
+git+https://github.com/huggingface/transformers.git
+docquery