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| import streamlit as st | |
| import pandas as pd | |
| import PyPDF2 | |
| import io | |
| import os | |
| from dotenv import load_dotenv | |
| import requests | |
| import time | |
| # Load environment variables | |
| load_dotenv() | |
| PERPLEXITY_API_KEY = os.getenv("PERPLEXITY_API_KEY") | |
| PERPLEXITY_API_URL = "https://api.perplexity.ai/chat/completions" | |
| def call_perplexity_api(prompt: str) -> str: | |
| """Call Perplexity AI with a prompt, return the text response if successful.""" | |
| headers = { | |
| "Authorization": f"Bearer {PERPLEXITY_API_KEY}", | |
| "Content-Type": "application/json", | |
| } | |
| payload = { | |
| "model": "llama-3.1-sonar-small-128k-chat", | |
| "messages": [{"role": "user", "content": prompt}], | |
| "temperature": 0.3, | |
| } | |
| try: | |
| response = requests.post(PERPLEXITY_API_URL, headers=headers, json=payload) | |
| response.raise_for_status() | |
| return response.json()["choices"][0]["message"]["content"] | |
| except Exception as e: | |
| st.error(f"API Error: {str(e)}") | |
| return "" | |
| def extract_text_from_pdf(pdf_file): | |
| """Extract text content from a PDF file.""" | |
| pdf_reader = PyPDF2.PdfReader(pdf_file) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() + "\n" | |
| return text | |
| def analyze_paper(text: str, category: str) -> str: | |
| """Generate a prompt and get analysis for a specific category.""" | |
| prompts = { | |
| "Summarized Abstract": "Extract and summarize the abstract from this research paper:", | |
| "Results": "What are the main results and findings from this research paper:", | |
| "Summarized Introduction": "Summarize the introduction section of this research paper:", | |
| "Methods Used": "What are the main methods and methodologies used in this research:", | |
| "Literature Survey": "Summarize the literature review or related work from this paper:", | |
| "Limitations": "What are the limitations mentioned in this research:", | |
| "Contributions": "What are the main contributions of this research:", | |
| "Practical Implications": "What are the practical implications of this research:", | |
| "Objectives": "What are the main objectives of this research:", | |
| "Findings": "What are the key findings from this research:", | |
| "Future Research": "What future research directions are suggested in this paper:", | |
| "Dependent Variables": "What are the dependent variables studied in this research:", | |
| "Independent Variables": "What are the independent variables studied in this research:", | |
| "Dataset": "What dataset(s) were used in this research:", | |
| "Problem Statement": "What is the main problem statement or research question:", | |
| "Challenges": "What challenges were faced or addressed in this research:", | |
| "Applications": "What are the potential applications of this research:" | |
| } | |
| prompt = f"{prompts[category]}\n\nPaper text: {text[:5000]}" # Limit text to avoid token limits | |
| return call_perplexity_api(prompt) | |
| def main(): | |
| st.title("Research Paper Analysis Tool") | |
| # File uploader | |
| uploaded_files = st.file_uploader("Upload PDF files", type="pdf", accept_multiple_files=True) | |
| if uploaded_files: | |
| if st.button("Process Papers"): | |
| # Initialize progress bar | |
| progress_bar = st.progress(0) | |
| status_text = st.empty() | |
| # Initialize results dictionary | |
| results = [] | |
| # Define categories | |
| categories = [ | |
| "Summarized Abstract", "Results", "Summarized Introduction", | |
| "Methods Used", "Literature Survey", "Limitations", | |
| "Contributions", "Practical Implications", "Objectives", | |
| "Findings", "Future Research", "Dependent Variables", | |
| "Independent Variables", "Dataset", "Problem Statement", | |
| "Challenges", "Applications" | |
| ] | |
| # Process each file | |
| for i, file in enumerate(uploaded_files): | |
| status_text.text(f"Processing {file.name}...") | |
| # Extract text from PDF | |
| text = extract_text_from_pdf(file) | |
| # Initialize paper results | |
| paper_results = {"Filename": file.name} | |
| # Analyze each category | |
| for j, category in enumerate(categories): | |
| status_text.text(f"Processing {file.name} - {category}") | |
| paper_results[category] = analyze_paper(text, category) | |
| # Update progress | |
| progress = (i * len(categories) + j + 1) / (len(uploaded_files) * len(categories)) | |
| progress_bar.progress(progress) | |
| # Add small delay to avoid API rate limits | |
| time.sleep(1) | |
| results.append(paper_results) | |
| # Create DataFrame | |
| df = pd.DataFrame(results) | |
| # Convert DataFrame to CSV | |
| csv = df.to_csv(index=False) | |
| # Create download button | |
| st.download_button( | |
| label="Download Results as CSV", | |
| data=csv, | |
| file_name="research_papers_analysis.csv", | |
| mime="text/csv" | |
| ) | |
| # Display results in the app | |
| st.subheader("Analysis Results") | |
| st.dataframe(df) | |
| status_text.text("Processing complete!") | |
| progress_bar.progress(1.0) | |
| if __name__ == "__main__": | |
| main() |