Update app.py

app.py

@@ -44,7 +44,7 @@ def get_chatgpt_response(messages, selected_model):
     return response['choices'][0]['message']['content']
 
 # Define pages in sidebar
-page = st.sidebar.radio('Select a page:', ('Review Analysis', 'review summary', 'Feature Benefits', 'Identify Avatars', 'Tone of Voice Manual'))
+page = st.sidebar.radio('Select a page:', ('Review Analysis', 'review summary', 'Feature Benefits', 'Identify Avatars', 'Tone of Voice Manual', 'question_answers'))
 
 
 if page == 'Review Analysis':
@@ -989,3 +989,195 @@ if page == 'review summary':
             st.error(f"An error occurred while writing to Google Sheets: {e}")
 
 
+if page == 'question_answers':
+    st.title('question answers')
+
+    gc = gspread.service_account(filename='arctic-rite-381810-e8bee8664772.json')
+
+    # Ask user for Google Sheet URL
+    sheet_url = st.text_input('Enter the URL of your Google Sheet')
+
+    # Add this in the part of your Streamlit app where the user can select the model
+    models = ["gpt-3.5-turbo", "gpt-4"]
+    selected_model = st.selectbox("Choose a model:", models)
+
+    # Extract sheet ID from URL
+    sheet_id = sheet_url.split('/')[5]
+    # Open the Google spreadsheet
+    sheet = gc.open_by_key(sheet_id)
+
+    # Select sheet named 'Listing'
+    worksheet = sheet.worksheet('Listing')
+    # Get all records of the data
+    df = get_as_dataframe(worksheet)
+
+    # Convert DataFrame to strings
+    df_str = df.astype(str)
+
+    title = df[df.eq('Title').any(axis=1)].iloc[0, 2]
+    bullets = [df[df.eq(f"Bullet #{i}").any(axis=1)].iloc[0, 2] for i in range(1, 6)]
+
+    backend_search_terms = df[df.astype(str).apply(lambda x: 'Backend Search Terms' in ' '.join(x), axis=1)].iloc[0, 2] if len(df[df.astype(str).apply(lambda x: 'Backend Search Terms' in ' '.join(x), axis=1)]) > 0 else None
+
+    image_text_row = df[df.eq('Image Text').any(axis=1)]
+    image_text = list(image_text_row.dropna(axis=1).iloc[0, :])
+
+    a_plus_desc_mask = df.astype(str).apply(lambda x: 'A+ Description' in ' '.join(x), axis=1)
+    if a_plus_desc_mask.any():
+        a_plus_desc_row = df[a_plus_desc_mask].index[0]
+        a_plus_desc = df.iloc[a_plus_desc_row:, :].fillna('').values.flatten()
+        a_plus_desc = ' '.join(a_plus_desc).strip()
+    else:
+        a_plus_desc = None
+
+    legacy_desc = df[df.astype(str).apply(lambda x: 'Legacy Product Description' in ' '.join(x), axis=1)].iloc[0, 2] if len(df[df.astype(str).apply(lambda x: 'Legacy Product Description' in ' '.join(x), axis=1)]) > 0 else None
+
+    image_text_string = ' '.join(image_text)
+
+    bullet_str = ""
+    for i, bullet in enumerate(bullets, 1):
+        bullet_str += f"Bullet #{i}: {bullet}\n"
+
+    # Display default prompt to user
+    default_prompt = """Below is data of our products for amazon listing:
+    ------------------------------------
+    Title: {}\n 
+    bullet_str: {}\n
+    Legacy Product Description: {}\n
+    A+ Description: {}\n
+    Image_text: {}\n
+    backend_search_terms: {}\n
+    -------------------------------------    
+
+    Please create question-answers pairs for amazon listing using this data. 
+    Please segregate them theme wise. themes names should not be features or benefits and create 1 or more questions answer pairs for each theme.
+    questions generated should be potential questions in customer mind before buying product.
+    please create at least 20 question-answers pairs.
+            """.format(title, bullet_str, legacy_desc, a_plus_desc, image_text_string, backend_search_terms)
+
+    prompt = st.text_area("Edit the prompt:", value=default_prompt, height=200)
+
+
+
+    # If the user has input a URL, fetch the data from Google Sheets
+    if st.button('generate'):
+
+        messages = [
+                    {"role": "system", "content": "You are helpful assistant"},
+                    {"role": "user", "content": prompt}
+                ]
+
+        
+
+        model_response = get_chatgpt_response(messages, selected_model)
+
+        question_answers= model_response
+
+        st.session_state.question_answers = question_answers
+
+        st.write(question_answers)
+
+    def parse_qa(text):
+        try:
+            # Split the text into themes
+            themes = re.split(r'\nTheme: ', text)[1:]
+            
+            output = []
+            for theme in themes:
+                theme_lines = theme.split('\n')
+                theme_name = theme_lines[0]
+                qa_pairs = theme_lines[1:]
+                
+                # Extract question and answer pairs
+                for qa in qa_pairs:
+                    if qa.startswith('Q'):
+                        question = qa.split(': ', 1)[1]
+                    elif qa.startswith('A'):
+                        answer = qa.split(': ', 1)[1]
+                        output.append((theme_name, question, answer))
+            return output, True
+        except Exception as e:
+            return text, False  # Return the full text and a flag indicating the parsing failed
+
+        
+
+    # # Create a button that will trigger writing to Google Sheets
+    # if st.button('Write output to sheet'):
+
+    #     final_output= st.session_state.question_answers
+
+    #     st.write("Button clicked, processing data...")
+
+    #     sheet_id = sheet_url.split('/')[5]  # Open the Google spreadsheet
+    #     sheet = gc.open_by_key(sheet_id)
+
+
+
+       
+
+    #     try:
+    #         # Check if the "tone_of_voice_manual" worksheet already exists
+    #         worksheet_output = None
+    #         for wks in sheet.worksheets():
+    #             if wks.title == 'question_answers':
+    #                 worksheet_output = wks
+    #                 break
+
+    #         # If the worksheet does not exist, create a new one
+    #         if worksheet_output is None:
+    #             worksheet_output = sheet.add_worksheet(title="question_answers", rows="100", cols="20")
+
+    #         # Read the existing data from the worksheet
+    #         existing_data = get_as_dataframe(worksheet_output)
+
+    #         # Remove empty columns from the existing data
+    #         existing_data = existing_data.dropna(how='all', axis=1)
+
+    #         # Prepare a list to store the tone of voice manual data
+    #         question_answers_data = [final_output]
+
+    #         # Convert the list of tone of voice manual data into a DataFrame
+    #         new_data = pd.DataFrame(question_answers_data, columns=['question_answers'])
+
+    #         # Append the new data to the existing data
+    #         updated_data = pd.concat([existing_data, new_data], axis=1)
+
+    #         # Clear the worksheet before writing the updated data
+    #         worksheet_output.clear()
+
+    #         # Write the updated data to the "tone_of_voice_manual" worksheet
+    #         set_with_dataframe(worksheet_output, updated_data)
+    #         st.write("Data written successfully to Google Sheets.")
+    #     except Exception as e:
+    #         st.error(f"An error occurred while writing to Google Sheets: {e}")
+
+    if st.button('Write output to sheet'):
+        final_output, parsed_successfully = parse_qa(st.session_state.question_answers)
+
+        st.write("Button clicked, processing data...")
+
+        sheet_id = sheet_url.split('/')[5]  # Open the Google spreadsheet
+        sheet = gc.open_by_key(sheet_id)
+
+        try:
+            worksheet_output = None
+            for wks in sheet.worksheets():
+                if wks.title == 'question_answers':
+                    worksheet_output = wks
+                    break
+
+            if worksheet_output is None:
+                worksheet_output = sheet.add_worksheet(title="question_answers", rows="100", cols="20")
+
+            if parsed_successfully:
+                new_data = pd.DataFrame(final_output, columns=['Theme', 'Question', 'Answer'])
+            else:
+                new_data = pd.DataFrame([final_output], columns=['Output'])
+
+            worksheet_output.clear()
+            set_with_dataframe(worksheet_output, new_data)
+            st.write("Data written successfully to Google Sheets.")
+        except Exception as e:
+            st.error(f"An error occurred while writing to Google Sheets: {e}")
+
+