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| import os | |
| import requests | |
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.prompts import PromptTemplate | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| import re | |
| from langchain_groq import ChatGroq | |
| from langchain_google_genai.chat_models import ChatGoogleGenerativeAI | |
| api_key = os.getenv("Gemini_api_key") | |
| llm_1 = ChatGoogleGenerativeAI(model="gemini-pro", api_key=api_key) | |
| api_key2=os.getenv("Llama_api_key") | |
| MODEL_ID = "llama3-groq-70b-8192-tool-use-preview" #"llama3-groq-70b-8192-tool-use-preview" "llama-3.2-90b-text-preview" | |
| llm_2=ChatGroq(model=MODEL_ID, temperature=0, groq_api_key=api_key2) | |
| def download_pdf_from_url(url, local_file_path): | |
| response = requests.get(url) | |
| with open(local_file_path, 'wb') as f: | |
| f.write(response.content) | |
| def extract_text_from_pdf(pdf_file_path): | |
| reader = PdfReader(pdf_file_path) | |
| text = "" | |
| for page in reader.pages: | |
| extracted_text = page.extract_text() | |
| if extracted_text: | |
| text += extracted_text + "\n" | |
| return text.strip() | |
| def clean_html_tags(text): | |
| clean_text = re.sub(r"<ul>|</ul>|<li>|</li>", "", text) | |
| clean_text = re.sub(r"<.*?>", "", clean_text) | |
| return clean_text.strip() | |
| # Define the template for summarization | |
| template = """ | |
| Based on the following document: | |
| {document_text} | |
| Please provide the summary in a **table format**. Each point should be in its own row, with the following columns: | |
| | **Aspect** | **Details** | | |
| |--------------------------|---------------------------------------------------------------------| | |
| | What did they do? | Briefly describe the main task, objective, or experiment. | | |
| | Contributions | Highlight the main contributions of the paper. | | |
| | Hardware | Name, model, price (if available), link (if available), function. | | |
| | Software | Type (commercial/free/custom-developed), version, availability, features. | | |
| | Dataset | Type (public/private), type of data (image, text, video, log), duration, size. | | |
| | Algorithms | List the algorithms or models used. | | |
| | Place of Experiment | Where was the experiment conducted (institution/lab)? | | |
| | Claimed Results | Summarize the key results and findings. | | |
| | Limitations | Identify limitations or shortcomings. | | |
| | Solutions | Suggest possible solutions for overcoming limitations. | | |
| | Improvements | Suggest potential improvements or additions. | | |
| Ensure each section is concise but informative. | |
| """ | |
| # Prompt Template | |
| prompt = PromptTemplate(template=template, input_variables=["document_text"]) | |
| template2 = """ | |
| Paraphrase the following paragraph in academic research format: | |
| #NO PREAMBLE # | |
| #DONT INCLUDE ANY BULLET POINTS WRITE IN SINGLE PARAGRAPH# | |
| {paragraph} | |
| """ | |
| # Prompt Template | |
| prompt2 = PromptTemplate(template=template2, input_variables=["paragraph"]) | |