Update verifier.py

verifier.py

@@ -6,15 +6,14 @@ import PyPDF2
 import google.generativeai as genai
 
 def verifier_page():
-    # Initialize Firebase
+    #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
+    #display submissions
     def fetch_data(selected_fields):
         docs = db.collection('pdf_uploads').stream()
         data = []
@@ -26,81 +25,72 @@ def verifier_page():
     
     fields_to_fetch = ['filename', 'pdf_url', 'text']
 
-    # Initialize session state variables if not present
+    #initialize session state variables if not 
     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)
+            start_page = 0  
+            end_page = 93 
             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)
+            start_page = 0  
+            end_page = 89   
             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)
+            start_page = 0  
+            end_page = 34  
             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 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 level of adherence should have grading on selected criteria mentioned in the documentation. The goal here is to help other verifiers understand what do you think about this project submission and how much more improvement this work needs. What needs to be fixed should be detailed feedback and give action items. 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
+            #save the response in session state
             st.session_state.selected_file = response.text
 
-            # Clear everything and show file details and AI response
+            #remove everything and show file details and 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
+            #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
+                        #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
+                        #clear existing content on button click
                         st.rerun()
                             
 
-# Helper function to extract text from PDF
+#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
+        return ""  
     
     reader = PyPDF2.PdfReader(uploaded_file)
     num_pages = len(reader.pages)