app.py

# --- Imports ---
import spaces
import gradio as gr
from transformers import pipeline
import pandas as pd
import os

# --- Load Model ---
pipe = pipeline(model="InstaDeepAI/ChatNT", trust_remote_code=True)

# --- Logs ---
log_file = "logs.txt"

class Log:
    def __init__(self, log_file):
        self.log_file = log_file

    def __call__(self):
        if not os.path.exists(self.log_file):
            return ""
        with open(self.log_file, "r") as f:
            return f.read()

# --- Main Function ---
@spaces.GPU
def run_chatnt(input_file, custom_question):
    with open(log_file, "a") as log:
        log.write("Request started\n")

    if not custom_question or custom_question.strip() == "":
        return pd.DataFrame(), None

    # Read DNA sequences
    dna_sequences = []
    if input_file is not None:
        with open(input_file.name, "r") as f:
            lines = f.readlines()
            for line in lines:
                if line.startswith(">"):
                    continue
                dna_sequences.append(line.strip())

    if not dna_sequences:
        return pd.DataFrame(), None

    # Build prompt
    english_sequence = custom_question + " <DNA>"

    # Call model
    output = pipe(
        inputs={
            "english_sequence": english_sequence,
            "dna_sequences": dna_sequences
        }
    )

    # Wrap output
    results = []
    if isinstance(output, list):
        for item in output:
            results.append({"Result": item})
    else:
        results.append({"Result": output})

    df = pd.DataFrame(results)
    output_file = "output.csv"
    df.to_csv(output_file, index=False)

    with open(log_file, "a") as log:
        log.write("Request finished\n")

    return df, output_file

# --- Gradio Interface ---
css = """
.gradio-container { font-family: sans-serif; }
.gr-button { color: white; border-color: black; background: black; }
footer { display: none !important; }
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("# 🧬 ChatNT — DNA Sequence Query Assistant")

    with gr.Row():
        with gr.Column(scale=1):
            input_file = gr.File(
                label="Upload DNA Sequence File (.fasta or .txt)",
                file_types=[".fasta", ".fa", ".txt"]
            )
            custom_question = gr.Textbox(
                label="English Question (required)",
                placeholder="e.g., Does this sequence contain a donor splice site?"
            )

            submit_btn = gr.Button("Run Query", variant="primary")

        with gr.Column(scale=2):
            output_df = gr.DataFrame(
                label="Results",
                headers=["Result"]
            )
            output_file = gr.File(label="Download Results (CSV)")

    submit_btn.click(
        run_chatnt,
        inputs=[input_file, custom_question],
        outputs=[output_df, output_file],
    )

    gr.Markdown("""
**Note:** Your question **must** include the `<DNA>` token if needed for multiple sequences.
    """)

    with gr.Accordion("Logs", open=True):
        log_display = Log(log_file)
        gr.Markdown(log_display)

# --- Launch ---
if __name__ == "__main__":
    demo.queue()
    demo.launch(debug=True, show_error=True)