aquibmoin commited on
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4b8acc1
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1 Parent(s): c24f2ac

Update app.py

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  1. app.py +1 -82
app.py CHANGED
@@ -5,14 +5,7 @@ from openai import OpenAI
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  import os
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  import numpy as np
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  from sklearn.metrics.pairwise import cosine_similarity
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- from docx import Document
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- from docx.shared import Pt
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- from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
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- from docx.oxml.ns import nsdecls
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- from docx.oxml import parse_xml
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  import io
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- import tempfile
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- #import pyvo as vo
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  import pandas as pd
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  from pinecone import Pinecone
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  import logging
@@ -20,6 +13,7 @@ import re
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  from utils.ads_references import extract_keywords_with_gpt, fetch_nasa_ads_references
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  from utils.data_insights import fetch_exoplanet_data, generate_data_insights
 
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  from langchain_openai import ChatOpenAI
@@ -174,81 +168,6 @@ def generate_response(user_input, science_objectives="", relevant_context="", re
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  # Return two clearly separated responses
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  return full_response, response_only
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- def export_to_word(response_content, subdomain_definition, science_goal, context, max_tokens, temperature, top_p, frequency_penalty, presence_penalty):
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- doc = Document()
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-
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- # Add a title (optional, you can remove this if not needed)
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- doc.add_heading('AI Generated SCDD', 0)
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-
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- # Insert the Subdomain Definition at the top
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- doc.add_heading('Subdomain Definition:', level=1)
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- doc.add_paragraph(subdomain_definition)
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-
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- # Insert the Science Goal at the top
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- doc.add_heading('Science Goal:', level=1)
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- doc.add_paragraph(science_goal)
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-
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- # Insert the User-defined Context
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- doc.add_heading('User-defined Context:', level=1)
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- doc.add_paragraph(context)
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-
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- # Insert Model Parameters
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- doc.add_heading('Model Parameters:', level=1)
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- doc.add_paragraph(f"Max Tokens: {max_tokens}")
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- doc.add_paragraph(f"Temperature: {temperature}")
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- doc.add_paragraph(f"Top-p: {top_p}")
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- doc.add_paragraph(f"Frequency Penalty: {frequency_penalty}")
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- doc.add_paragraph(f"Presence Penalty: {presence_penalty}")
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-
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- # Split the response into sections based on ### headings
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- sections = response_content.split('### ')
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-
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- for section in sections:
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- if section.strip():
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- # Handle the "Observations Requirements Table" separately with proper formatting
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- if 'Observations Requirements Table' in section:
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- doc.add_heading('Observations Requirements Table', level=1)
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-
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- # Extract table lines
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- table_lines = section.split('\n')[2:] # Start after the heading line
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-
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- # Check if it's an actual table (split lines by '|' symbol)
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- table_data = [line.split('|')[1:-1] for line in table_lines if '|' in line]
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-
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- if table_data:
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- # Add table to the document
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- table = doc.add_table(rows=len(table_data), cols=len(table_data[0]))
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- table.style = 'Table Grid'
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- for i, row in enumerate(table_data):
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- for j, cell_text in enumerate(row):
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- cell = table.cell(i, j)
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- cell.text = cell_text.strip()
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- # Apply text wrapping for each cell
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- cell._element.get_or_add_tcPr().append(parse_xml(r'<w:tcW w:w="2500" w:type="pct" ' + nsdecls('w') + '/>'))
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-
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- # Process any paragraphs that follow the table
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- paragraph_after_table = '\n'.join([line for line in table_lines if '|' not in line and line.strip()])
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- if paragraph_after_table:
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- doc.add_paragraph(paragraph_after_table.strip())
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-
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- # Handle the "ADS References" section
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- elif section.startswith('ADS References'):
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- doc.add_heading('ADS References', level=1)
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- references = section.split('\n')[1:] # Skip the heading
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- for reference in references:
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- if reference.strip():
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- doc.add_paragraph(reference.strip())
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-
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- # Add all other sections as plain paragraphs
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- else:
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- doc.add_paragraph(section.strip())
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-
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- # Save the document to a temporary file
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- temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".docx")
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- doc.save(temp_file.name)
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-
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- return temp_file.name
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-
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  def extract_table_from_response(gpt_response):
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  # Split the response into lines
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  lines = gpt_response.strip().split("\n")
 
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  import os
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  import numpy as np
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  from sklearn.metrics.pairwise import cosine_similarity
 
 
 
 
 
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  import io
 
 
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  import pandas as pd
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  from pinecone import Pinecone
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  import logging
 
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  from utils.ads_references import extract_keywords_with_gpt, fetch_nasa_ads_references
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  from utils.data_insights import fetch_exoplanet_data, generate_data_insights
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+ from utils.gen_doc import export_to_word
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  from langchain_openai import ChatOpenAI
 
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  # Return two clearly separated responses
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  return full_response, response_only
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  def extract_table_from_response(gpt_response):
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  # Split the response into lines
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  lines = gpt_response.strip().split("\n")