Update verifier.py

verifier.py

@@ -31,15 +31,15 @@ def verifier_page():
         st.session_state.selected_file = None
     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 'selected_ai' not in st.session_state:
+    #   st.session_state.selected_ai = ""
 
     # Check if a file has already been selected and displayed
     if st.session_state.selected_file:
         # When a file is selected, clear everything and display the details only
         st.empty()  # Clear all content before displaying details
-        st.title(f"Selected File: {st.session_state.selected_file}")
-        st.write(f"AI Response: {st.session_state.selected_ai}")
+        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)
@@ -65,7 +65,7 @@ def verifier_page():
                         #st.session_state.selected_text = row['text']
 
                         # Clear existing content on button click
-                        st.rerun()
+                        st.experimental_rerun()
 
         if st.session_state.selected_file:
             # Process the selected file and generate AI response
@@ -84,15 +84,6 @@ def verifier_page():
             end_page = 34  # Extract up to the third page (0-based index)
             template_text = extract_text_from_pdf(pdf_path, start_page, end_page)
 
-            # Check if the extracted text is not empty
-            if not st.session_state.selected_text:
-                st.error("No text extracted from the selected file.")
-                return
-
-            # Debug print to check if the selected text is correct
-            st.write("Extracted Text from Selected File:")
-            st.write(st.session_state.selected_text)
-
             # 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)
@@ -100,20 +91,9 @@ def verifier_page():
             # Example of working with LLM models (Gemini 1.5)
             model = genai.GenerativeModel('gemini-1.5-flash-latest')
 
-            # Debug log for checking the text before passing it to the AI model
-            request_text = "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
-
-            st.write("Request Text for AI Model:")
-            st.write(request_text)
-
             # Generate the AI response based on the text of the selected file
-            response = model.generate_content(request_text)
+            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_text + "Here is the vcs standards:" + vcs_text + "Here is the methodology requirement:" + methodology_text + "Here is the project description template:" + template_text)
                 
-            # Check if response is empty
-            if not response.text:
-                st.error("AI response is empty.")
-                return
-
             # Save the response and filename in session state
             st.session_state.selected_file = response.text