app changes

app.py

@@ -155,178 +155,180 @@ st.markdown("Link to the app - [PDF to extract loadn details app on 🤗 Spaces]
 #image uploader
 file_name = st.file_uploader(label = "Upload your PDF file here",type=['pdf','png','jpg','jpeg'])
 print(file_name)
-# Find the PDF path
-pdf_path = file_name # '/content/data/'+file_name+".pdf"
-#text_file_path = '/content/data/'+file_name+".txt"
-# Create a pdf file object
-pdfFileObj = open(pdf_path, 'rb')
-# Create a pdf reader object
-pdfReaded = PyPDF2.PdfReader(pdfFileObj)
-
-# Create the dictionary to extract text from each image
-text_per_page = {}
-# Create a boolean variable for image detection
-image_flag = False
-number_of_pages = len(list(extract_pages(pdfFileObj)))
-result = ''
-
-# We extract the pages from the PDF
-for pagenum, page in enumerate(extract_pages(pdf_path)):
-
-    # Initialize the variables needed for the text extraction from the page
-    pageObj = pdfReaded.pages[pagenum]
-    page_text = []
-    line_format = []
-    text_from_images = []
-    text_from_tables = []
-    page_content = []
-    # Initialize the number of the examined tables
-    table_in_page= -1
-    # Open the pdf file
-    pdf = pdfplumber.open(pdf_path)
-    # Find the examined page
-    page_tables = pdf.pages[pagenum]
-    # Find the number of tables in the page
-    tables = page_tables.find_tables()
-    if len(tables)!=0:
-        table_in_page = 0
-
-    # Extracting the tables of the page
-    for table_num in range(len(tables)):
-        # Extract the information of the table
-        table = extract_table(pdf_path, pagenum, table_num)
-        # Convert the table information in structured string format
-        table_string = table_converter(table)
-        # Append the table string into a list
-        text_from_tables.append(table_string)
-
-    # Find all the elements
-    page_elements = [(element.y1, element) for element in page._objs]
-    # Sort all the element as they appear in the page
-    page_elements.sort(key=lambda a: a[0], reverse=True)
-
-
-    # Find the elements that composed a page
-    for i,component in enumerate(page_elements):
-        # Extract the element of the page layout
-        element = component[1]
-
-        # Check the elements for tables
-        if table_in_page == -1:
-            pass
-        else:
-            if is_element_inside_any_table(element, page ,tables):
-                table_found = find_table_for_element(element,page ,tables)
-                if table_found == table_in_page and table_found != None:
-                    page_content.append(text_from_tables[table_in_page])
-                    #page_text.append('table')
-                    #line_format.append('table')
-                    table_in_page+=1
-                # Pass this iteration because the content of this element was extracted from the tables
-                continue
-
-        if not is_element_inside_any_table(element,page,tables):
-
-            # Check if the element is text element
-            if isinstance(element, LTTextContainer):
-                # Use the function to extract the text and format for each text element
-                (line_text, format_per_line) = text_extraction(element)
-                # Append the text of each line to the page text
-                page_text.append(line_text)
-                # Append the format for each line containing text
-                line_format.append(format_per_line)
-                page_content.append(line_text)
-
-
-            # Check the elements for images
-            if isinstance(element, LTFigure):
-                # Crop the image from PDF
-                crop_image(element, pageObj)
-                # Convert the croped pdf to image
-                convert_to_images('cropped_image.pdf')
-                # Extract the text from image
-                image_text = image_to_text('PDF_image.png')
-                image_text = "" # removed to remove the errors with image
-                text_from_images.append(image_text)
-                page_content.append(image_text)
-                # Add a placeholder in the text and format lists
-                #page_text.append('image')
-                #line_format.append('image')
-                # Update the flag for image detection
-                image_flag = True
-
-
-    # Create the key of the dictionary
-    dctkey = 'Page_'+str(pagenum)
-
-    # Add the list of list as value of the page key
-    #text_per_page[dctkey]= [page_text, line_format, text_from_images,text_from_tables, page_content]
-    text_per_page[dctkey]= [page_text, text_from_images,text_from_tables, page_content]
-    #result = result.join(page_text).join(line_format).join(text_from_images).join(text_from_tables).join(page_content)
-
-
-result = " "
-for t in range(number_of_pages):
-  page = 'Page_'+str(t)
-  #result = result.join(map(str, text_per_page[page]))
-  for q in range(len(text_per_page[page])):
-    #print(f"{''.join(map(str, text_per_page[page][q]))}")
-    result = result + f"{''.join(map(str, text_per_page[page][q]))}"
-
-#paid key
-os.environ["OPENAI_API_KEY"]="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"
-
-from dotenv import load_dotenv, find_dotenv
-_ = load_dotenv(find_dotenv()) # read local .env file
-openai.api_key = os.environ['OPENAI_API_KEY']
-
-
-template="You are a helpful assistant that annalyses a bank statement annd provides answers"
-system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template= "{text}"
-human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
-
-prompt_1 = """Loan status include details like Total Outstanding or Total Loan Amount,
-Start Month, Tenure in Months, Rate of interest and EMI.
-
-Extract the details from text from triple tick marks and return a JSON object ONLY with keys Total Loan Amount as Number, Start Month in format mmm-yyyy, Tenure in Months, ROI, EMI as Number.
-
-Only return the JSON.
-"""
-
-
-prompt_template_1 = PromptTemplate.from_template(
-  prompt_1 +  "```{loan_data} ```"
- )
-#prompt_template_1.format(loan_data=result.lower())
-response_1 = OpenAI().complete(prompt_template_1.format(loan_data=result.lower()))
-
-prompt_2 = """Loan transaction details are the information of transaction happened during a period and contains
-details like Month, EMI as monthly amount paid, Payment status as Paid or Unpaid, outstanding Balance after payment of EMI.
-
-Return a table of ALL transactions by
-
-1. COMBININNG monthly transactions for each month
-2. WITHOUT missing rows for ANY month
-3. with columns Month, EMI Paid, Payment Status, Interest Amount, Principal Amount, Balance Amount
-
-from text in triple tick marks.
-
-Just return the table"""
-
-prompt_template_2 = PromptTemplate.from_template(
-    prompt_2 + "```{response_1} {loan_data} ```"
- )
-#prompt_template_2.format(response_1 =response_1, loan_data=result.lower())
-
-
-response_2 = OpenAI().complete(prompt_template_2.format(response_1 =response_1, loan_data=result.lower()))
 
+if file_name is not None:
+    st.write(file_name.name)
+
+    file_details = {"FileName":file_name.name,"FileType":file_name.type}
+    st.write(file_details)
+    # Find the PDF path
+    pdf_path = file_name # '/content/data/'+file_name+".pdf"
+    st.write(pdf_path)
+    #text_file_path = '/content/data/'+file_name+".txt"
+    # Create a pdf file object
+    #pdfFileObj = open(+pdf_path, 'rb')
+    # Create a pdf reader object
+    pdfReaded = PyPDF2.PdfReader(file_name)
+
+    # Create the dictionary to extract text from each image
+    text_per_page = {}
+    # Create a boolean variable for image detection
+    image_flag = False
+
+    number_of_pages = len(list(extract_pages(file_name)))
+    result = ''
+
+    # We extract the pages from the PDF
+    for pagenum, page in enumerate(extract_pages(file_name)):
+
+        # Initialize the variables needed for the text extraction from the page
+        pageObj = pdfReaded.pages[pagenum]
+        page_text = []
+        line_format = []
+        text_from_images = []
+        text_from_tables = []
+        page_content = []
+        # Initialize the number of the examined tables
+        table_in_page= -1
+        # Open the pdf file
+        pdf = pdfplumber.open(pdf_path)
+        # Find the examined page
+        page_tables = pdf.pages[pagenum]
+        # Find the number of tables in the page
+        tables = page_tables.find_tables()
+        if len(tables)!=0:
+            table_in_page = 0
+
+        # Extracting the tables of the page
+        for table_num in range(len(tables)):
+            # Extract the information of the table
+            table = extract_table(pdf_path, pagenum, table_num)
+            # Convert the table information in structured string format
+            table_string = table_converter(table)
+            # Append the table string into a list
+            text_from_tables.append(table_string)
+
+        # Find all the elements
+        page_elements = [(element.y1, element) for element in page._objs]
+        # Sort all the element as they appear in the page
+        page_elements.sort(key=lambda a: a[0], reverse=True)
+
+
+        # Find the elements that composed a page
+        for i,component in enumerate(page_elements):
+            # Extract the element of the page layout
+            element = component[1]
+
+            # Check the elements for tables
+            if table_in_page == -1:
+                pass
+            else:
+                if is_element_inside_any_table(element, page ,tables):
+                    table_found = find_table_for_element(element,page ,tables)
+                    if table_found == table_in_page and table_found != None:
+                        page_content.append(text_from_tables[table_in_page])
+                        #page_text.append('table')
+                        #line_format.append('table')
+                        table_in_page+=1
+                    # Pass this iteration because the content of this element was extracted from the tables
+                    continue
+
+            if not is_element_inside_any_table(element,page,tables):
+
+                # Check if the element is text element
+                if isinstance(element, LTTextContainer):
+                    # Use the function to extract the text and format for each text element
+                    (line_text, format_per_line) = text_extraction(element)
+                    # Append the text of each line to the page text
+                    page_text.append(line_text)
+                    # Append the format for each line containing text
+                    line_format.append(format_per_line)
+                    page_content.append(line_text)
+
+
+                # Check the elements for images
+                if isinstance(element, LTFigure):
+                    # Crop the image from PDF
+                    crop_image(element, pageObj)
+                    # Convert the croped pdf to image
+                    convert_to_images('cropped_image.pdf')
+                    # Extract the text from image
+                    image_text = image_to_text('PDF_image.png')
+                    image_text = "" # removed to remove the errors with image
+                    text_from_images.append(image_text)
+                    page_content.append(image_text)
+                    # Add a placeholder in the text and format lists
+                    #page_text.append('image')
+                    #line_format.append('image')
+                    # Update the flag for image detection
+                    image_flag = True
+
+
+        # Create the key of the dictionary
+        dctkey = 'Page_'+str(pagenum)
+
+        # Add the list of list as value of the page key
+        #text_per_page[dctkey]= [page_text, line_format, text_from_images,text_from_tables, page_content]
+        text_per_page[dctkey]= [page_text, text_from_images,text_from_tables, page_content]
+        #result = result.join(page_text).join(line_format).join(text_from_images).join(text_from_tables).join(page_content)
+
+
+    result = " "
+    for t in range(number_of_pages):
+        page = 'Page_'+str(t)
+    #result = result.join(map(str, text_per_page[page]))
+    for q in range(len(text_per_page[page])):
+        #print(f"{''.join(map(str, text_per_page[page][q]))}")
+        result = result + f"{''.join(map(str, text_per_page[page][q]))}"
+
+    #paid key
+    os.environ["OPENAI_API_KEY"]="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"
+
+    from dotenv import load_dotenv, find_dotenv
+    _ = load_dotenv(find_dotenv()) # read local .env file
+    openai.api_key = os.environ['OPENAI_API_KEY']
+
+
+    template="You are a helpful assistant that annalyses a bank statement annd provides answers"
+    system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+    human_template= "{text}"
+    human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+
+    prompt_1 = """Loan status include details like Total Outstanding or Total Loan Amount,
+    Start Month, Tenure in Months, Rate of interest and EMI.
+
+    Extract the details from text from triple tick marks and return a JSON object ONLY with keys Total Loan Amount as Number, Start Month in format mmm-yyyy, Tenure in Months, ROI, EMI as Number.
+
+    Only return the JSON.
+    """
+
+
+    prompt_template_1 = PromptTemplate.from_template(
+    prompt_1 +  "```{loan_data} ```"
+    )
+    #prompt_template_1.format(loan_data=result.lower())
+    response_1 = OpenAI().complete(prompt_template_1.format(loan_data=result.lower()))
+
+    prompt_2 = """Loan transaction details are the information of transaction happened during a period and contains
+    details like Month, EMI as monthly amount paid, Payment status as Paid or Unpaid, outstanding Balance after payment of EMI.
 
+    Return a table of ALL transactions by
+
+    1. COMBININNG monthly transactions for each month
+    2. WITHOUT missing rows for ANY month
+    3. with columns Month, EMI Paid, Payment Status, Interest Amount, Principal Amount, Balance Amount
+
+    from text in triple tick marks.
+
+    Just return the table"""
+
+    prompt_template_2 = PromptTemplate.from_template(
+        prompt_2 + "```{response_1} {loan_data} ```"
+    )
+    #prompt_template_2.format(response_1 =response_1, loan_data=result.lower())
 
-reader = load_model() #load model
 
-if file_name is not None:
+    response_2 = OpenAI().complete(prompt_template_2.format(response_1 =response_1, loan_data=result.lower()))
 
     with st.spinner("🤖 AI is at Work! "):
         st.write(response_2)