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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| import json | |
| import os | |
| from dotenv import load_dotenv | |
| # 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 generate_research_paper(df: pd.DataFrame) -> dict: | |
| """ | |
| For each column in the DataFrame, generate a research paper section (200-500 words) | |
| that addresses the data in that column. Return a dict mapping column -> text. | |
| """ | |
| paper_sections = {} | |
| for col in df.columns: | |
| # Convert all non-null rows in the column to strings and join them for context | |
| col_values = df[col].dropna().astype(str).tolist() | |
| # We'll truncate if this is huge | |
| sample_text = " | ".join(col_values[:50]) # limit to first 50 rows for brevity | |
| prompt = f""" | |
| Topic: {col} | |
| Data Sample: {sample_text} | |
| Generate a professional research paper section for the above column. | |
| The section should be at least 100 words and at most 150 words, | |
| focusing on key insights, challenges, and potential research angles. | |
| Integrate the data samples as context for the content. | |
| """ | |
| section_text = call_perplexity_api(prompt) | |
| paper_sections[col] = section_text.strip() if section_text else "" | |
| return paper_sections | |
| def format_paper(paper_dict: dict) -> str: | |
| """ | |
| Format the generated paper into a Markdown string. | |
| Each column name is used as a heading, and the text is placed under it. | |
| """ | |
| md_text = "# Generated Research Paper\n\n" | |
| for col, content in paper_dict.items(): | |
| md_text += f"## {col}\n{content}\n\n" | |
| return md_text | |
| def main(): | |
| st.title("Corpus-based Research Paper Generator") | |
| uploaded_file = st.file_uploader("Upload CSV corpus file", type="csv") | |
| if uploaded_file: | |
| df = pd.read_csv(uploaded_file) | |
| st.write("### Preview of Uploaded Data") | |
| st.dataframe(df.head()) | |
| if st.button("Generate Research Paper"): | |
| st.info("Generating paper based on the columns of your corpus...") | |
| with st.spinner("Calling Perplexity AI..."): | |
| paper = generate_research_paper(df) | |
| if paper: | |
| formatted_paper = format_paper(paper) | |
| st.success("Research Paper Generated Successfully!") | |
| st.write(formatted_paper) | |
| st.download_button( | |
| label="Download Paper as Markdown", | |
| data=formatted_paper, | |
| file_name="research_paper.md", | |
| mime="text/markdown", | |
| ) | |
| else: | |
| st.error( | |
| "Paper generation failed. Please check Perplexity API key." | |
| ) | |
| if __name__ == "__main__": | |
| main() | |