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Dockerfile.txt

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+
+
+
+# Start with CUDA base image
+FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04
+
+# Avoid prompts from apt
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa \
+    && apt-get update \
+    && apt-get install -y \
+    python3.9 \
+    python3.9-distutils \
+    python3.9-dev \
+    python3-pip \
+    build-essential \
+    wget \
+    ffmpeg \
+    libsm6 \
+    libxext6 \
+    libgl1-mesa-glx \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set Python 3.9 as the default python version
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
+RUN update-alternatives --set python3 /usr/bin/python3.9
+
+# Install pip for Python 3.9
+RUN wget https://bootstrap.pypa.io/get-pip.py && \
+    python3 get-pip.py && \
+    rm get-pip.py
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the requirements file with correct ownership
+COPY --chown=user requirements.txt .
+
+# Install any needed packages specified in requirements.txt
+RUN pip3 install --no-cache-dir --user -r requirements.txt
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Run app.py when the container launches
+CMD ["python3", "-m", "streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app-3.py

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+# import streamlit as st
+# from doctr.io import DocumentFile
+# from doctr.models import ocr_predictor
+# import json
+# from doctr.models import ocr_predictor
+# import openai
+# import re
+
+# import os
+# model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)
+
+# # import the OpenAI Python library for calling the OpenAI API
+# from openai import OpenAI
+# import os
+
+# # Function to extract text from PDF using OCR
+# def extract_text_from_pdf(pdf_file):
+#     doc = DocumentFile.from_pdf(pdf_file)
+#     result = model(doc)  # Assuming 'model' is your OCR predictor
+#     return result
+
+# # Function to clean extracted text
+# def clean_extracted_text(result):
+#     extracted_text = []
+#     for page in result.pages:
+#         for block in page.blocks:
+#             for line in block.lines:
+#                 line_text = ' '.join(word.value for word in line.words)
+#                 extracted_text.append(line_text)
+#     return '\n'.join(extracted_text)
+
+# # Function to extract critical information using a language model
+# def extract_critical_information(text):
+#     # Here you would integrate your OpenAI language model (LLM) or similar
+#     # For demonstration, let's assume it returns a mock JSON
+#     MODEL = "gpt-3.5-turbo"
+#     #response = client.chat.completions.create(
+#     response = client.chat.completions.create(
+#     model=MODEL,
+#     messages=[
+#         {"role": "system", "content": "You are a property deed expert for the US."},
+#         {"role": "user", "content": f"Given the raw information extracted using OCR from a PDF, you should extract the most important parts of the deed such as the owner's name, property number, address, and any other important factors. The OCR results are stored in the variable `result`.\n\nOCR Result:\n{text}\n\nPlease provide the extracted information in JSON format.\n\nExample JSON output:\n{{\n  \"owner_name\": \"\",\n  \"property_address\": \"\",\n  \"legal_description\": \"\",\n  \"grantor_name\": \"\",\n  \"grantee_name\": \"\",\n  \"deed_type\": \"\",\n  \"liens_and_encumbrances\": \"\",\n  \"signatures\": \"\",\n  \"notarization_details\": \"\",\n  \"recording_information\": \"\",\n  \"consideration\": \"\",\n  \"habendum_clause\": \"\",\n  \"warranty_clauses\": \"\",\n  \"tax_information\": \"\",\n  \"title_insurance_details\": \"\"\n}}Extracted Information JSON:, Warning: Do not make up fake information"}],
+#     temperature=0,
+#     )
+#     extracted_info= (response.choices[0].message.content)
+#     return extracted_info
+
+
+# def clean_and_convert_to_json(input_string):
+#     # Remove the markdown code block indicators and any leading/trailing whitespace
+#     cleaned_string = input_string.strip()
+#     cleaned_string = re.sub(r'^```json\s*|\s*```$', '', cleaned_string, flags=re.MULTILINE)
+    
+#     # Remove any non-printable characters except newlines
+#     cleaned_string = ''.join(char for char in cleaned_string if char.isprintable() or char in '\n\r')
+    
+#     # Ensure the string starts with { and ends with }
+#     cleaned_string = cleaned_string.strip()
+#     if not cleaned_string.startswith('{'):
+#         cleaned_string = '{' + cleaned_string
+#     if not cleaned_string.endswith('}'):
+#         cleaned_string = cleaned_string + '}'
+    
+#     try:
+#         # Parse the string as JSON
+#         data = json.loads(cleaned_string)
+#         # Convert back to a JSON string with proper formatting
+#         cleaned_json = json.dumps(data, indent=2)
+#         return cleaned_json
+#     except json.JSONDecodeError as e:
+#         print(f"JSON Decode Error: {e}")
+#         print(f"Problematic string:\n{cleaned_string}")
+#         return None  
+
+
+# def main():
+#     st.title("Property Deed Information Extraction App")
+
+#     uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+
+#     if uploaded_file is not None:
+#         st.write("File uploaded successfully!")
+
+#         # Process the uploaded PDF file
+#         result = extract_text_from_pdf(uploaded_file)
+#         cleaned_text = clean_extracted_text(result)
+#         extracted_info = extract_critical_information(cleaned_text)
+#         extracted_info = clean_and_convert_to_json(extracted_info)
+
+#         # Parse the JSON result
+#         try:
+#             extracted_info_json = json.loads(extracted_info)
+#         except json.JSONDecodeError as e:
+#             st.error(f"An error occurred while parsing the JSON response: {e}")
+#             extracted_info_json = {}
+
+#         # Display extracted information
+#         st.subheader("Extracted Information:")
+#         st.text_input("Owner Name", value=extracted_info_json.get("owner_name", ""))
+#         st.text_input("Property Address", value=extracted_info_json.get("property_address", ""))
+#         st.text_input("Legal Description", value=extracted_info_json.get("legal_description", ""))
+#         st.text_input("Grantor Name", value=extracted_info_json.get("grantor_name", ""))
+#         st.text_input("Grantee Name", value=extracted_info_json.get("grantee_name", ""))
+#         st.text_input("Deed Type", value=extracted_info_json.get("deed_type", ""))
+#         st.text_input("Liens and Encumbrances", value=extracted_info_json.get("liens_and_encumbrances", ""))
+#         st.text_input("Signatures", value=extracted_info_json.get("signatures", ""))
+#         st.text_input("Notarization Details", value=extracted_info_json.get("notarization_details", ""))
+#         st.text_input("Recording Information", value=extracted_info_json.get("recording_information", ""))
+#         st.text_input("Consideration", value=extracted_info_json.get("consideration", ""))
+#         st.text_input("Habendum Clause", value=extracted_info_json.get("habendum_clause", ""))
+#         st.text_input("Warranty Clauses", value=extracted_info_json.get("warranty_clauses", ""))
+#         st.text_input("Tax Information", value=extracted_info_json.get("tax_information", ""))
+#         st.text_input("Title Insurance Details", value=extracted_info_json.get("title_insurance_details", ""))
+
+# if __name__ == "__main__":
+#     main()
+
+import streamlit as st
+from doctr.io import DocumentFile
+from doctr.models import ocr_predictor
+import json
+import openai
+import re
+import os
+from openai import OpenAI
+model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True).to('cpu')
+
+# Set up OpenAI client
+
+
+client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "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"))
+
+# if not api_key:
+#     st.error("API Key is missing. Please set the OPENAI_API_KEY environment variable.")
+# else:
+#     client = openai.OpenAI(api_key=api_key)
+#     st.write(f"API Key Loaded: {api_key[:4]}...")
+
+# Function to extract text from PDF using OCR
+def extract_text_from_pdf(pdf_file):
+    doc = DocumentFile.from_pdf(pdf_file)
+    result = model(doc)  # Assuming 'model' is your OCR predictor
+    return result
+
+# Function to clean extracted text
+def clean_extracted_text(result):
+    extracted_text = []
+    for page in result.pages:
+        for block in page.blocks:
+            for line in block.lines:
+                line_text = ' '.join(word.value for word in line.words)
+                extracted_text.append(line_text)
+    return '\n'.join(extracted_text)
+
+# Function to extract critical information using a language model
+# def extract_critical_information(text):
+#     MODEL = "gpt-3.5-turbo"
+#     try:
+#         response = client.ChatCompletion.create(
+#             model=MODEL,
+#             messages=[
+#                 {"role": "system", "content": "You are a property deed expert for the US."},
+#                 {"role": "user", "content": f"Given the raw information extracted using OCR from a PDF, you should extract the most important parts of the deed such as the owner's name, property number, address, and any other important factors. The OCR results are stored in the variable `result`.\n\nOCR Result:\n{text}\n\nPlease provide the extracted information in JSON format.\n\nExample JSON output:\n{{\n  \"owner_name\": \"\",\n  \"property_address\": \"\",\n  \"legal_description\": \"\",\n  \"grantor_name\": \"\",\n  \"grantee_name\": \"\",\n  \"deed_type\": \"\",\n  \"liens_and_encumbrances\": \"\",\n  \"signatures\": \"\",\n  \"notarization_details\": \"\",\n  \"recording_information\": \"\",\n  \"consideration\": \"\",\n  \"habendum_clause\": \"\",\n  \"warranty_clauses\": \"\",\n  \"tax_information\": \"\",\n  \"title_insurance_details\": \"\"\n}}Extracted Information JSON:, Warning: Do not make up fake information"}],
+#             temperature=0,
+#         )
+#         extracted_info = response['choices'][0]['message']['content']
+#         return extracted_info
+#     except openai.error.OpenAIError as e:
+#         st.error(f"OpenAI API Error: {e}")
+#         return None
+
+def extract_critical_information(text):
+    # Here you would integrate your OpenAI language model (LLM) or similar
+    # For demonstration, let's assume it returns a mock JSON
+    MODEL = "gpt-4o-mini"
+    #response = client.chat.completions.create(
+    response = client.chat.completions.create(
+    model=MODEL,
+    messages=[
+        {"role": "system", "content": "You are a property deed expert for the US."},
+        {"role": "user", "content": f"Given the raw information extracted using OCR from a PDF, you should extract the most important parts of the deed such as the owner's name, property number, address, and any other important factors. The OCR results are stored in the variable `result`.\n\nOCR Result:\n{text}\n\nPlease provide the extracted information in JSON format.\n\nExample JSON output:\n{{\n  \"owner_name\": \"\",\n  \"property_address\": \"\",\n  \"legal_description\": \"\",\n  \"grantor_name\": \"\",\n  \"grantee_name\": \"\",\n  \"deed_type\": \"\",\n  \"liens_and_encumbrances\": \"\",\n  \"signatures\": \"\",\n  \"notarization_details\": \"\",\n  \"recording_information\": \"\",\n  \"consideration\": \"\",\n  \"habendum_clause\": \"\",\n  \"warranty_clauses\": \"\",\n  \"tax_information\": \"\",\n  \"title_insurance_details\": \"\"\n}}Extracted Information JSON:, Warning: Do not make up fake information"}],
+    temperature=0,
+    )
+    extracted_info= (response.choices[0].message.content)
+    return extracted_info
+
+
+# Function to clean and convert text to JSON
+def clean_and_convert_to_json(input_string):
+    cleaned_string = input_string.strip()
+    cleaned_string = re.sub(r'^```json\s*|\s*```$', '', cleaned_string, flags=re.MULTILINE)
+    cleaned_string = ''.join(char for char in cleaned_string if char.isprintable() or char in '\n\r')
+    cleaned_string = cleaned_string.strip()
+    if not cleaned_string.startswith('{'):
+        cleaned_string = '{' + cleaned_string
+    if not cleaned_string.endswith('}'):
+        cleaned_string = cleaned_string + '}'
+    try:
+        data = json.loads(cleaned_string)
+        cleaned_json = json.dumps(data, indent=2)
+        return cleaned_json
+    except json.JSONDecodeError as e:
+        st.error(f"JSON Decode Error: {e}")
+        return None
+
+# Streamlit app
+def main():
+    st.title("Property Deed Information Extraction App")
+
+    uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+
+    if uploaded_file is not None:
+        st.write("File uploaded successfully!")
+
+        result = extract_text_from_pdf(uploaded_file)
+        cleaned_text = clean_extracted_text(result)
+        extracted_info = extract_critical_information(cleaned_text)
+        if extracted_info:
+            extracted_info = clean_and_convert_to_json(extracted_info)
+            if extracted_info:
+                try:
+                    extracted_info_json = json.loads(extracted_info)
+                except json.JSONDecodeError as e:
+                    st.error(f"An error occurred while parsing the JSON response: {e}")
+                    extracted_info_json = {}
+
+                st.subheader("Extracted Information:")
+                st.text_input("Owner Name", value=extracted_info_json.get("owner_name", ""))
+                st.text_input("Property Address", value=extracted_info_json.get("property_address", ""))
+                st.text_input("Legal Description", value=extracted_info_json.get("legal_description", ""))
+                st.text_input("Grantor Name", value=extracted_info_json.get("grantor_name", ""))
+                st.text_input("Grantee Name", value=extracted_info_json.get("grantee_name", ""))
+                st.text_input("Deed Type", value=extracted_info_json.get("deed_type", ""))
+                st.text_input("Liens and Encumbrances", value=extracted_info_json.get("liens_and_encumbrances", ""))
+                st.text_input("Signatures", value=extracted_info_json.get("signatures", ""))
+                st.text_input("Notarization Details", value=extracted_info_json.get("notarization_details", ""))
+                st.text_input("Recording Information", value=extracted_info_json.get("recording_information", ""))
+                st.text_input("Consideration", value=extracted_info_json.get("consideration", ""))
+                st.text_input("Habendum Clause", value=extracted_info_json.get("habendum_clause", ""))
+                st.text_input("Warranty Clauses", value=extracted_info_json.get("warranty_clauses", ""))
+                st.text_input("Tax Information", value=extracted_info_json.get("tax_information", ""))
+                st.text_input("Title Insurance Details", value=extracted_info_json.get("title_insurance_details", ""))
+            else:
+                st.error("Failed to clean and convert extracted information to JSON.")
+        else:
+            st.error("Failed to extract critical information from text.")
+
+if __name__ == "__main__":
+    main()
+
+

requirements-2.txt

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+streamlit>=1.0.0
+openai
+python-doctr[torch]