verifier.py

import streamlit as st
import firebase_admin
from firebase_admin import credentials, firestore
import pandas as pd
import PyPDF2
import google.generativeai as genai

def verifier_page():
    # Initialize Firebase
    cred = credentials.Certificate('serviceAccountKey.json')
    if not firebase_admin._apps:
        firebase_admin.initialize_app(cred)
    
    # Create a Firestore client
    db = firestore.client()

    # Fetch and display submissions
    def fetch_data(selected_fields):
        docs = db.collection('pdf_uploads').stream()
        data = []
        for doc in docs:
            doc_dict = doc.to_dict()
            filtered_data = {field: doc_dict.get(field) for field in selected_fields if field in doc_dict}
            data.append(filtered_data)
        return data
    
    fields_to_fetch = ['filename', 'pdf_url', 'text']

    # Initialize session state variables if not present
    if 'selected_file' not in st.session_state:
        st.session_state.selected_file = ""
    #if 'selected_text' not in st.session_state:
    #   st.session_state.selected_text = ""
    #if 'selected_ai' not in st.session_state:
    #   st.session_state.selected_ai = ""



    if st.session_state.selected_file:
            # Process the selected file and generate AI response
            pdf_path = 'VCS-Standard.pdf'
            start_page = 0  # Start extracting from the first page (0-based index)
            end_page = 93  # Extract up to the third page (0-based index)
            vcs_text = extract_text_from_pdf(pdf_path, start_page, end_page)

            pdf_path = 'VCS-Methodology-Requirements.pdf'
            start_page = 0  # Start extracting from the first page (0-based index)
            end_page = 89  # Extract up to the third page (0-based index)
            methodology_text = extract_text_from_pdf(pdf_path, start_page, end_page)

            pdf_path = 'VCS-Project-Description-Template-v4.4-FINAL2.docx.pdf'
            start_page = 0  # Start extracting from the first page (0-based index)
            end_page = 34  # Extract up to the third page (0-based index)
            template_text = extract_text_from_pdf(pdf_path, start_page, end_page)

            # Configure AI (this could be dynamic depending on how your setup works)
            GOOGLE_API_KEY = "AIzaSyC7TpzrIH_3-dppWE8exqdZX3DAdE6cy8w"
            genai.configure(api_key=GOOGLE_API_KEY)
            
            # Example of working with LLM models (Gemini 1.5)
            model = genai.GenerativeModel('gemini-1.5-flash-latest')

            # Generate the AI response based on the text of the selected file
            response = model.generate_content("You are a project verifier officer at Verra, the leading registry for projects used to generate carbon credits. Your job is to look into project submissions from project developers who create an implement nature-based solutions in order to generate carbon credits. You go through the content of the project submissions to investigate whether the submission fits into the vcs standards, methodology requirements, and touches everything on the project description template. A verifier has to compare the submission to these 3 main criteria.  As a verifier, I want you to evaluate the project submission below based on the resources listed below. The output should be in the format of summary of the project submission, the level of adherence to the standards, what needs to be fixed, and notes for improvement for project developers. The output needs to have project-specific feedback. You can bolster your feedback with quotes from the submission or referencing numbers mentioned in the submission. Here is the project submission:" + st.session_state.selected_file + "Here is the vcs standards:" + vcs_text + "Here is the methodology requirement:" + methodology_text + "Here is the project description template:" + template_text)
                
            # Save the response and filename in session state
            st.session_state.selected_file = response.text

            # Clear everything and show file details and AI response
            st.empty()
            st.write(f"Selected File: {st.session_state.selected_file}")
            #st.write(f"AI Response: {st.session_state.selected_ai}")

        
    else:
        # Automatically load and display the data table on page load
            data = fetch_data(fields_to_fetch)
            if data:
                df = pd.DataFrame(data)
        
    
                # Add buttons for each row in the DataFrame
            for index, row in df.iterrows():
                col1, col2 = st.columns([4, 1])
                with col1:
                    st.write(row['filename'])
        
                with col2:
                    # Button to view details of each row
                    button_key = f"view_{index}"
                    if st.button("See details", key=button_key):
                        # Update the session state with the selected file details
                        st.session_state.selected_file = row['text']
                        #st.session_state.selected_text = row['text']
    
                        # Clear existing content on button click
                        st.rerun()
                            

# Helper function to extract text from PDF
def extract_text_from_pdf(uploaded_file, start_page, end_page):
    if uploaded_file is None:
        return ""  # Return an empty string if no file is uploaded
    
    reader = PyPDF2.PdfReader(uploaded_file)
    num_pages = len(reader.pages)
    
    if start_page < 0 or start_page >= num_pages:
        start_page = 0
    if end_page < start_page or end_page >= num_pages:
        end_page = num_pages - 1

    text = ''
    for page_num in range(start_page, end_page + 1):
        page = reader.pages[page_num]
        text += page.extract_text()

    return text