# import gradio as gr
# import os

# def greet(name):
#     return "Hello " + name + "!"

# # Create the simplest possible Gradio interface
# iface = gr.Interface(
#     fn=greet,
#     inputs="text",
#     outputs="text",
#     title="Test Gradio App",
#     description="This is a simple test app to check if Gradio launches.",
#     flagging_dir="/tmp/gradio_flagged_data" # <--- ADD THIS LINE BACK!
# )

# # Use a specific port for Gradio within the Docker container.
# # This matches the EXPOSE 7860 in your Dockerfile.
# # It also sets share=False for deployment contexts like Spaces.
# iface.launch(server_name="0.0.0.0", server_port=7860, share=False)

import gradio as gr
# from transformers import pipeline
# from langchain_community.llms import OpenAI
# from langchain.chains import LLMChain
# from langchain.prompts import PromptTemplate
# from langchain_community.document_loaders import PyPDFLoader

def load_document(file_path):
    """Loads a PDF document and returns its content."""
    loader = PyPDFLoader(file_path)
    pages = loader.load_and_split()
    return "".join([page.page_content for page in pages])

def summarize_text(text):
    """Summarizes the given text using a pre-trained model."""
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    summary = summarizer(text, max_length=500, min_length=100, do_sample=False)
    return summary[0]['summary_text']

def identify_future_research(text):
    """Uses a language model to identify future research scope."""
    llm = OpenAI(temperature=0.7)  # You can also use open-source models from Hugging Face Hub

    prompt_template = """
    Based on the following research paper, identify and suggest potential areas for future research.
    Be specific and provide actionable insights.

    Research Paper Content:
    {paper_content}

    Future Research Scope:
    """

    prompt = PromptTemplate(
        input_variables=["paper_content"],
        template=prompt_template
    )

    chain = LLMChain(llm=llm, prompt=prompt)
    future_scope = chain.run(paper_content=text)
    return future_scope



def analyze_paper(file):
    """The main function that orchestrates the analysis."""
    if file is not None:
        # paper_text = load_document(file.name)
        # summary = summarize_text(paper_text)
        # future_scope = identify_future_research(paper_text)
        # return summary, future_scope
        return "Dummy Summary Placeholder", "Dummy Future Scope Placeholder"
    return "Please upload a research paper.", ""

iface = gr.Interface(
    fn=analyze_paper,
    inputs=gr.File(label="Upload Research Paper (PDF)"),
    outputs=[
        gr.Textbox(label="Summary of the Paper"),
        gr.Textbox(label="Scope for Further Research")
    ],
    flagging_dir="/tmp/gradio_flagged_data",
    title="AI Research Assistant",
    description="Upload a research paper to get a summary and identify potential areas for future research.",
    theme="huggingface"
)

iface.launch(share=True, debug=True)