app.py

import os
import streamlit as st
from paddleocr import PaddleOCR
from langchain_groq import ChatGroq
from langchain.output_parsers import PydanticOutputParser
from langchain_core.prompts import PromptTemplate
from pydantic import BaseModel, Field
import fitz  
import json
from PIL import Image

ocr = PaddleOCR(use_angle_cls=True, lang='es')

st.set_page_config(layout="wide")

class CarInfoEntity(BaseModel):
    dealer_name: str = Field(description="Nombre del concesionario o empresa.")
    dealer_address: str = Field(description="Dirección física del concesionario.")
    tax_id: str = Field(description="Número de identificación fiscal del concesionario.")
    contact_phone: str = Field(description="Número de teléfono principal para contactar con el concesionario.")
    contact_fax: str = Field(description="Número de fax del concesionario.")
    contact_email: str = Field(description="Dirección de correo electrónico para consultas.")
    website_url: str = Field(description="Sitio web oficial del concesionario.")
    operating_hours: str = Field(description="Horario habitual de atención del concesionario.")
    saturday_hours: str = Field(description="Horario de atención específico para los sábados.")
    order_date: str = Field(description="Fecha en que se realizó el pedido.")
    order_number: str = Field(description="Identificador único del pedido.")
    sales_rep: str = Field(description="Nombre del vendedor que maneja la transacción.")
    customer_full_name: str = Field(description="Nombre completo del comprador.")
    customer_address: str = Field(description="Dirección del comprador.")
    customer_city: str = Field(description="Ciudad donde reside el comprador.")
    customer_postal_code: str = Field(description="Código postal de la dirección del comprador.")
    customer_province: str = Field(description="Provincia donde se encuentra el comprador.")
    customer_id: str = Field(description="Número de identificación del comprador (NIF).")
    customer_phone: str = Field(description="Número de teléfono del comprador.")
    vehicle_description: str = Field(description="Descripción del vehículo que se está comprando, incluyendo marca, modelo y año.")
    vehicle_color: str = Field(description="Color del vehículo.")
    vehicle_price: str = Field(description="Precio total del vehículo, incluyendo impuestos.")

model = ChatGroq(
    model="llama-3.3-70b-versatile",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2
)



entity = ['dealer_name', 'dealer_address', 'tax_id', 'contact_phone', 'contact_fax', 'contact_email', 'website_url', 
          'operating_hours', 'saturday_hours', 'order_date', 'order_number', 'sales_rep', 
          'customer_full_name', 'customer_address', 'customer_city', 'customer_postal_code', 
          'customer_province', 'customer_id','customer_phone', 'vehicle_description','vehicle_color','vehicle_price']

# Streamlit App
st.title("Vehicle Information Extractor")
st.write("Upload a PDF file to extract vehicle information.")

uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
use_default = st.checkbox("Use Default Pdf")

doc = None

if use_default:
    default_pdf_path = "pedido V.O.pdf"
    if os.path.exists(default_pdf_path):
        print("Present")
        doc = fitz.open(default_pdf_path)
        st.write("Using default PDF:")
    else:
        st.error("Default PDF not found.")

else:
    if uploaded_file is not None:
        with open("temp.pdf", "wb") as f:
            f.write(uploaded_file.read())
        doc = fitz.open("temp.pdf")
        st.write("Uploaded PDF:")

if doc:
    col1, col2 = st.columns(2)

    with col1:
        for page_num in range(len(doc)):
            page = doc.load_page(page_num)
            pix = page.get_pixmap()
            img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
            st.image(img, caption=f"Page {page_num + 1}", use_column_width=True)

    # Perform OCR
    ocr_result = ocr.ocr(default_pdf_path if use_default else "temp.pdf")

    extracted_text = []
    for page in ocr_result:
        for result in page:
            text = result[1][0]
            extracted_text.append(text)

    all_text = " ".join(extracted_text)

    prompt_text = """Task: Analyze the {all_text} and find out given entity value:{entity} from the {all_text}:

    Output Format: A table with the entity and value. First column contains the {entity} and second column contains the value fetched from the {all_text}.
                   
    Do not include any additional explanations or unnecessary details. 
    {format_instructions}"""

    parser = PydanticOutputParser(pydantic_object=CarInfoEntity)

    prompt = PromptTemplate(
        template=prompt_text,
        input_variables=["all_text", "entity"],
        partial_variables={"format_instructions": parser.get_format_instructions()},
    )

    chain = prompt | model | parser

    output = chain.invoke({"all_text": all_text, "entity": entity})

    with col2:
        st.write("Extracted Vehicle Information (Table):")
        st.table(output)