Add .idea config files, update README with detailed instructions, implement invoice extraction in app.py, and list dependencies in requirements.txt.

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="PROJECT_PROFILE" value="Laravel" />
+    <option name="USE_PROJECT_PROFILE" value="false" />
+  </settings>
+</component>

.idea/invoice-ai-extractor.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.11 (invoice-ai-extractor)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11 (invoice-ai-extractor)" />
+  </component>
+  <component name="ComposerSettings">
+    <execution />
+  </component>
+  <component name="ProjectInspectionProfilesVisibleTreeState">
+    <entry key="Customized">
+      <profile-state>
+        <expanded-state>
+          <State />
+          <State>
+            <id>Angular</id>
+          </State>
+          <State>
+            <id>GeneralJavaScript</id>
+          </State>
+          <State>
+            <id>JavaScript</id>
+          </State>
+          <State>
+            <id>PHP</id>
+          </State>
+          <State>
+            <id>Type compatibilityPHP</id>
+          </State>
+        </expanded-state>
+        <selected-state>
+          <State>
+            <id>AngularAmbiguousComponentTag</id>
+          </State>
+        </selected-state>
+      </profile-state>
+    </entry>
+    <entry key="Project Default">
+      <profile-state>
+        <expanded-state>
+          <State />
+          <State>
+            <id>Angular</id>
+          </State>
+        </expanded-state>
+        <selected-state>
+          <State>
+            <id>AngularInaccessibleComponentMemberInAotMode</id>
+          </State>
+        </selected-state>
+      </profile-state>
+    </entry>
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (invoice-ai-extractor)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/invoice-ai-extractor.iml" filepath="$PROJECT_DIR$/.idea/invoice-ai-extractor.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

README.md

@@ -1,14 +1,40 @@
----
-title: Invoice Ai Extractor
-emoji: 🐨
-colorFrom: yellow
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.42.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Extract key data from invoices with LayoutLM.
----
+# Invoice AI Extractor 🧾
+
+An AI-powered invoice information extraction tool that automatically extracts key information from your invoices using
+LayoutLM.
+
+## Features
+
+- Upload PDFs, JPGs, or PNGs 📄
+- Paste images directly from clipboard 📎
+- Extract key information:
+    - Invoice Numbers
+    - Dates
+    - Total Amounts
+    - Company Names
+    - Due Dates
+    - Tax Amounts
+- View confidence scores for each extracted field
+- Export results in JSON, CSV, or TXT format
+
+## How to Use
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+1. Upload your invoice using the file uploader or paste from clipboard
+2. Wait for the AI to process your document
+3. View the extracted information with confidence scores
+4. Download the results in your preferred format
+
+## Model
+
+Uses LayoutLM fine-tuned on invoice data to understand and extract information from document images. The model combines
+text, layout, and image information for accurate extraction.
+
+## Tips
+
+- Ensure documents are clearly scanned or photographed
+- Supported formats: PDF, JPG, PNG
+- Maximum file size: 10MB
+- Supported language: English
+
+---
+Created by [Arif] | [GitHub](https://github.com/doganarif)

app.py

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+import io
+import json
+
+import fitz
+import streamlit as st
+import torch
+from PIL import Image, ImageGrab
+from transformers import pipeline
+
+# --- Configuration and Setup ---
+
+DEVICE = 0 if torch.cuda.is_available() else -1
+
+st.set_page_config(
+    page_title="Invoice AI | by Arif Dogan",
+    page_icon="🧾",
+    layout="wide",
+    initial_sidebar_state="collapsed",
+)
+
+# --- Styling ---
+
+st.markdown(
+    """
+    <style>
+    .stApp {max-width: 1200px; margin: 0 auto}
+    .stButton>button {background-color: #4CAF50; color: white; border-radius: 5px;}
+    .stProgress>div>div {background-color: #4CAF50}
+    footer {visibility: hidden}
+    .high {color: #4CAF50; font-weight: bold}
+    .medium {color: #FFA726; font-weight: bold}
+    .low {color: #EF5350; font-weight: bold}
+    div[data-testid="stToolbar"] {visibility: hidden; height: 0}
+    [data-testid="stExpanderContent"] {background-color: rgba(67, 76, 94, 0.5);}
+    .stTextInput>div>div {background-color: rgba(67, 76, 94, 0.5)}
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+
+# --- Functions ---
+
+
+@st.cache_resource
+def load_model():
+    return pipeline(
+        "document-question-answering",
+        model="faisalraza/layoutlm-invoices",
+        device=DEVICE,
+    )
+
+
+def process_pdf(pdf_file):
+    pdf_content = pdf_file.read()
+    pdf_stream = io.BytesIO(pdf_content)
+    try:
+        with fitz.open(stream=pdf_stream, filetype="pdf") as pdf_document:
+            if pdf_document.page_count > 0:
+                page = pdf_document[0]
+                pix = page.get_pixmap(matrix=fitz.Matrix(300 / 72, 300 / 72))
+                img_data = pix.tobytes("png")
+                return Image.open(io.BytesIO(img_data)), pdf_document.page_count
+            else:
+                raise ValueError("PDF has no pages")
+    except Exception as e:
+        raise e
+    finally:
+        pdf_stream.close()
+
+
+def process_image(uploaded_file):
+    uploaded_file.seek(0)
+    if uploaded_file.type == "application/pdf":
+        return process_pdf(uploaded_file)
+    return Image.open(uploaded_file), 1
+
+
+def get_clipboard_image():
+    try:
+        img = ImageGrab.grabclipboard()
+        return (img, 1) if isinstance(img, Image.Image) else (None, 0)
+    except Exception:
+        return None, 0
+
+
+def prepare_export_data(extracted_info, format_type):
+    if format_type == "JSON":
+        return json.dumps(
+            {field: data["value"] for field, data in extracted_info.items()}, indent=2
+        )
+    elif format_type == "CSV":
+        header = ",".join(extracted_info.keys())
+        values = ",".join(f'"{data["value"]}"' for data in extracted_info.values())
+        return f"{header}\n{values}"
+    else:  # TXT
+        return "\n".join(
+            f"{field}: {data['value']}" for field, data in extracted_info.items()
+        )
+
+
+def extract_information(model, image, questions, progress_bar, status_text):
+    extracted_info = {}
+    for idx, question in enumerate(questions):
+        try:
+            # Update progress bar and status text
+            progress_bar.progress((idx + 1) / len(questions))
+            status_text.text(f"Processing: {question} ({idx + 1}/{len(questions)})")
+
+            response = model(image=image, question=question)
+            if (
+                response and response[0].get("answer", "").strip()
+            ):  # Check for non-empty answer
+                answer = response[0]["answer"]
+                confidence = response[0]["score"]
+
+                if confidence > 0.1:
+                    field = (
+                        question.replace("What is the ", "").replace("?", "").title()
+                    )
+                    extracted_info[field] = {"value": answer, "confidence": confidence}
+        except Exception:
+            continue  # Handle potential errors during model processing
+
+    return extracted_info
+
+
+# --- Initialization ---
+
+if "processed_image" not in st.session_state:
+    st.session_state.processed_image = None
+if "extracted_info" not in st.session_state:
+    st.session_state.extracted_info = {}
+
+# --- UI Layout ---
+
+st.markdown(
+    """
+    <div style='text-align: center; padding: 1rem;'>
+        <h1>🧾 Invoice AI Extractor</h1>
+        <p style='font-size: 1.2em; color: #999;'>Powered by LayoutLM</p>
+    </div>
+    """,
+    unsafe_allow_html=True,
+)
+
+model = load_model()
+
+col1, col2 = st.columns([2, 1])
+
+with col1:
+    uploaded_file = st.file_uploader(
+        "Drop invoice (PDF, JPG, PNG)", type=["pdf", "jpg", "jpeg", "png"]
+    )
+
+with col2:
+    st.write("Or paste from clipboard (Ctrl/Cmd + V)")
+    check_clipboard = st.button("📎 Check Clipboard")
+
+# --- Image Processing Logic ---
+
+if uploaded_file:
+    try:
+        image, _ = process_image(uploaded_file)
+        st.session_state.processed_image = image
+        st.session_state.extracted_info = {}  # Reset on new upload
+    except Exception as e:
+        st.error(f"Error processing file: {e}")
+
+elif check_clipboard:
+    clipboard_image, _ = get_clipboard_image()
+    if clipboard_image:
+        st.session_state.processed_image = clipboard_image
+        st.session_state.extracted_info = {}
+        st.success("Image loaded from clipboard")
+    else:
+        st.warning("No image found in clipboard")
+
+# --- Display and Information Extraction ---
+
+if st.session_state.processed_image:
+    try:
+        image = st.session_state.processed_image.convert("RGB")
+
+        col1, col2 = st.columns([1, 1])
+
+        with col1:
+            st.image(image, caption="Document", use_container_width=True)
+
+        with col2:
+            st.markdown("### 📊 Extracted Information")
+
+            if not st.session_state.extracted_info:
+                questions = [
+                    "What is the invoice number?",
+                    "What is the invoice date?",
+                    "What is the total amount?",
+                    "What is the company name?",
+                    "What is the due date?",
+                    "What is the tax amount?",
+                ]
+
+                # Create progress bar and status text elements
+                progress_bar = st.progress(0)
+                status_text = st.empty()
+
+                st.session_state.extracted_info = extract_information(
+                    model, image, questions, progress_bar, status_text
+                )
+
+                # Clear status text after completion
+                status_text.empty()
+
+            if st.session_state.extracted_info:
+                for field, data in st.session_state.extracted_info.items():
+                    conf_col, val_col = st.columns([1, 4])
+                    with val_col:
+                        st.text_input(
+                            field, data["value"], disabled=True, key=f"input_{field}"
+                        )  # added key
+                    with conf_col:
+                        confidence = data["confidence"]
+                        css_class = (
+                            "high"
+                            if confidence > 0.7
+                            else "medium"
+                            if confidence > 0.4
+                            else "low"
+                        )
+                        st.markdown(
+                            f"<p class='{css_class}'>{confidence:.1%}</p>",
+                            unsafe_allow_html=True,
+                        )
+
+                st.markdown("### 📥 Export")
+                export_format = st.selectbox("Format", ["JSON", "CSV", "TXT"])
+                export_data = prepare_export_data(
+                    st.session_state.extracted_info, export_format
+                )
+                file_extension = export_format.lower()
+                st.download_button(
+                    "Download",
+                    export_data,
+                    file_name=f"invoice_data.{file_extension}",
+                    mime=f"text/{file_extension}",
+                )
+            else:
+                st.warning(
+                    "Could not extract information. Please ensure the document is clear."
+                )
+
+    except Exception as e:
+        st.error(f"Error during processing: {e}")
+
+# --- Footer ---
+
+st.markdown("---")
+st.markdown(
+    """
+    <div style='text-align: center'>
+        <p>Created by <a href='https://github.com/doganarif' target='_blank'>Arif Dogan</a> |
+        <a href='https://huggingface.co/arifdogan' target='_blank'>🤗 Hugging Face</a></p>
+    </div>
+    """,
+    unsafe_allow_html=True,
+)

requirements.txt

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+asgiref==3.8.1
+attrs==24.3.0
+certifi==2024.12.14
+cffi==1.17.1
+charset-normalizer==3.4.0
+cryptography==44.0.1
+Deprecated==1.2.18
+Django==5.1.4
+djangorestframework==3.15.2
+filelock==3.16.1
+fsspec==2024.12.0
+git-filter-repo==2.45.0
+h11==0.14.0
+huggingface-hub==0.27.0
+idna==3.10
+inquirerpy==0.3.4
+isodate==0.7.2
+lxml==5.3.0
+outcome==1.3.0.post0
+packaging==24.2
+pfzy==0.3.4
+prompt_toolkit==3.0.48
+pycountry==24.6.1
+pycparser==2.22
+pycryptodome==3.21.0
+PyGithub==2.5.0
+PyJWT==2.10.1
+PyNaCl==1.5.0
+PySocks==1.7.1
+python-magic==0.4.27
+PyYAML==6.0.2
+requests==2.32.3
+selenium==4.27.1
+sniffio==1.3.1
+socks==0
+sortedcontainers==2.4.0
+sqlparse==0.5.2
+streamlink==7.0.0
+tqdm==4.67.1
+trio==0.27.0
+trio-websocket==0.11.1
+typing_extensions==4.12.2
+undetected-chromedriver==3.5.5
+urllib3==2.2.3
+wcwidth==0.2.13
+websocket-client==1.8.0
+websockets==14.1
+wrapt==1.17.2
+wsproto==1.2.0
+