app.py

import gradio as gr
import os
import tempfile
from openai import OpenAI

def generate_systematic_review(api_key, pdf_files):
    """
    Generate a systematic review of the uploaded PDF files using OpenAI's API.
    
    Args:
        api_key (str): OpenAI API key provided by the user
        pdf_files (list): List of uploaded PDF files
        
    Returns:
        str: Generated systematic review text
    """
    if not api_key.strip():
        return "Please provide a valid OpenAI API key."
    
    if not pdf_files:
        return "Please upload at least one PDF file."
    
    try:
        # Initialize OpenAI client with the provided API key
        client = OpenAI(api_key=api_key)
        
        # System prompt defining systematic review steps
        system_prompt = """Step 1: Identify a Research Field
The first step in writing a systematic review paper is to identify a research field. This involves selecting a specific area of study that you are interested in and want to explore further.
Step 2: Generate a Research Question
Once you have identified your research field, the next step is to generate a research question. This question should be specific, measurable, achievable, relevant, and time-bound (SMART).
Step 3: Create a Protocol
After generating your research question, the next step is to create a protocol. A protocol is a detailed plan of how you will conduct your research, including the methods you will use, the data you will collect, and the analysis you will perform.
Step 4: Evaluate Relevant Literature
The fourth step is to evaluate relevant literature. This involves searching for and reviewing existing studies related to your research question. You should critically evaluate the quality of these studies and identify any gaps or limitations in the current literature.
Step 5: Investigate Sources for Answers
The fifth step is to investigate sources for answers. This involves searching for and accessing relevant data and information that will help you answer your research question. This may include conducting interviews, surveys, or experiments, or analyzing existing data.
Step 6: Collect Data as per Protocol
The sixth step is to collect data as per protocol. This involves implementing the methods outlined in your protocol and collecting the data specified. You should ensure that your data collection methods are rigorous and reliable.
Step 7: Data Extraction
The seventh step is to extract the data. This involves organizing and analyzing the data you have collected, and extracting the relevant information that will help you answer your research question.
Step 8: Critical Analysis of Results
The eighth step is to conduct a critical analysis of your results. This involves interpreting your findings, identifying patterns and trends, and drawing conclusions based on your data.
Step 9: Interpreting Derivations
The ninth step is to interpret the derivations. This involves taking the conclusions you have drawn from your data and interpreting them in the context of your research question.
Step 10: Concluding Statements
The final step is to make concluding statements. This involves summarizing your findings and drawing conclusions based on your research. You should also provide recommendations for future research and implications for practice.
By following these steps, you can ensure that your systematic review paper is well-written, well-organized, and provides valuable insights into your research question.
"""
        
        # First, upload all files to get file IDs
        file_ids = []
        for pdf_file in pdf_files:
            with open(pdf_file.name, "rb") as f:
                file_response = client.files.create(
                    file=f,
                    purpose="assistants"
                )
                file_ids.append(file_response.id)
        
        # Create an assistant
        assistant = client.beta.assistants.create(
            name="Systematic Review Generator",
            instructions=system_prompt,
            model="gpt-4.1",
            tools=[{"type": "file_search"}],
            file_ids=file_ids
        )
        
        # Create a thread
        thread = client.beta.threads.create()
        
        # Add a message to the thread
        message = client.beta.threads.messages.create(
            thread_id=thread.id,
            role="user",
            content="Please generate the systematic review of these papers (include also important new generated tables)"
        )
        
        # Run the assistant on the thread
        run = client.beta.threads.runs.create(
            thread_id=thread.id,
            assistant_id=assistant.id
        )
        
        # Poll for the run completion
        import time
        while True:
            run_status = client.beta.threads.runs.retrieve(
                thread_id=thread.id,
                run_id=run.id
            )
            if run_status.status == "completed":
                break
            elif run_status.status in ["failed", "cancelled", "expired"]:
                return f"Run failed with status: {run_status.status}"
            time.sleep(2)
        
        # Get the messages from the thread
        messages = client.beta.threads.messages.list(thread_id=thread.id)
        
        # Get the latest assistant message
        assistant_messages = [msg for msg in messages.data if msg.role == "assistant"]
        if assistant_messages:
            latest_message = assistant_messages[0]  # Most recent message first
            
            # Extract text content from the message
            result_text = ""
            for content_item in latest_message.content:
                if content_item.type == "text":
                    result_text += content_item.text.value
            
            # Clean up: delete assistant, thread, and files
            client.beta.assistants.delete(assistant_id=assistant.id)
            client.beta.threads.delete(thread_id=thread.id)
            for file_id in file_ids:
                client.files.delete(file_id=file_id)
                
            return result_text
        
        # Clean up resources even if we didn't get a response
        client.beta.assistants.delete(assistant_id=assistant.id)
        client.beta.threads.delete(thread_id=thread.id)
        for file_id in file_ids:
            client.files.delete(file_id=file_id)
            
        return "Failed to generate a systematic review. Please try again."
        
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Create the Gradio interface
with gr.Blocks(title="Systematic Review Generator") as app:
    gr.Markdown("# Systematic Review Generator")
    gr.Markdown("Upload PDF files and generate a systematic review using OpenAI's GPT-4 model.")
    
    with gr.Row():
        with gr.Column():
            # Input components
            api_key = gr.Textbox(
                label="OpenAI API Key", 
                placeholder="Enter your OpenAI API key...",
                type="password"
            )
            
            pdf_files = gr.File(
                label="Upload PDF Files", 
                file_count="multiple",
                file_types=[".pdf"]
            )
            
            submit_btn = gr.Button("Generate Systematic Review", variant="primary")
            
        with gr.Column():
            # Output component
            output = gr.Markdown(label="Generated Systematic Review")
    
    # Set up the event handler
    submit_btn.click(
        fn=generate_systematic_review,
        inputs=[api_key, pdf_files],
        outputs=output
    )
    
    gr.Markdown("""
    ## How to Use
    
    1. Enter your OpenAI API key
    2. Upload one or more PDF research papers
    3. Click "Generate Systematic Review"
    4. The systematic review will be displayed in the output area
    
    ## Note
    
    This application requires a valid OpenAI API key with access to the GPT-4 model. 
    Your API key is not stored and is only used to make the API call to OpenAI.
    
    The review generation may take a few minutes depending on the number and size of PDF files.
    """)

if __name__ == "__main__":
    app.launch(share=True)