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| import streamlit as st | |
| import pandas as pd | |
| import json | |
| import tempfile | |
| import os | |
| # ============================== | |
| # App Configuration | |
| # ============================== | |
| st.set_page_config( | |
| page_title="MVP", | |
| page_icon="", | |
| layout="centered" | |
| ) | |
| # initialize session state | |
| if 'example_mgf' not in st.session_state: | |
| st.session_state['example_mgf'] = None | |
| if 'example_json' not in st.session_state: | |
| st.session_state['example_json'] = None | |
| # ============================== | |
| # Introductory Section | |
| # ============================== | |
| st.title("MVP Playground") | |
| st.markdown(""" | |
| This web app lets you test our trained model on your own data. | |
| ### 📚 References | |
| 🔗 **Paper:** [Read the publication here](https://github.com/HassounLab/MVP) | |
| 📦 **Source Code:** [GitHub Repository](https://github.com/HassounLab/MVP) | |
| --- | |
| ### 🧠 Available Models | |
| We have two models trained on the [MassSpecGym](https://github.com/pluskal-lab/MassSpecGym) training dataset: | |
| - **binnedSpec** – trained on binned spectra and does not require formula information. | |
| - **formSpec** – our main model trained on spectra with subformula annotations. Requires formula and adduct information. | |
| --- | |
| ### ⚙️ Instructions | |
| 1. **Prepare two input files:** | |
| - **Spectra file (.mgf)** – your experimental spectra data. | |
| - **Candidates file (.json)** – candidate molecules for each spectrum. | |
| 2. **Select a model** from the dropdown. | |
| 3. **Click “Run Prediction”** to start processing. | |
| ⚠️ **Note:** For fair usage, the web app limits computation to **1,000 pairs**. Each pair consists of one spectrum and one candidate molecule. | |
| 4. After processing, you’ll receive a downloadable **CSV file** with your results. | |
| --- | |
| ### 📁 Example Input Files | |
| You can download example files to understand the required format: | |
| - [Download sample spectra (MGF)](data/app/data.mgf) | |
| - [Download sample candidates (JSON)](data/app/identifier_to_candidates.json) | |
| Here's an example of the spectra file format (.mgf): | |
| ``` | |
| BEGIN IONS | |
| TITLE=example_spectrum | |
| PEPMASS=100.0 | |
| CHARGE=1+ | |
| FORMULA=C10H12O2 # optional, required for formSpec model | |
| ADDUCT=[M+H]+ # optional, required for formSpec model | |
| 100.0 1000 | |
| 101.0 1500 | |
| 102.0 2000 | |
| END IONS | |
| ``` | |
| --- | |
| ### 💡 Tip | |
| If you want to process **more than 1,000 pairs**, | |
| please **clone the repository** and run it locally with GPU support for faster computation. | |
| """) | |
| # ============================== | |
| # File Upload Section | |
| # ============================== | |
| st.subheader("📤 Upload Your Files") | |
| # --- File uploaders --- | |
| mgf_file = st.file_uploader("Upload spectra file (.mgf)", type=["mgf"]) | |
| json_file = st.file_uploader("Upload candidates file (.json)", type=["json"]) | |
| # --- Example files button --- | |
| if st.button("Use Example Files"): | |
| with open("data/app/data.mgf", "rb") as f: | |
| st.session_state["example_mgf"] = f.read() | |
| with open("data/app/identifier_to_candidates.json", "rb") as f: | |
| st.session_state["example_json"] = f.read() | |
| st.success("✅ Example files loaded!") | |
| # --- Determine which files to use --- | |
| if mgf_file is not None: | |
| mgf_bytes = mgf_file.read() | |
| elif "example_mgf" in st.session_state: | |
| mgf_bytes = st.session_state["example_mgf"] | |
| else: | |
| mgf_bytes = None | |
| if json_file is not None: | |
| json_bytes = json_file.read() | |
| elif "example_json" in st.session_state: | |
| json_bytes = st.session_state["example_json"] | |
| else: | |
| json_bytes = None | |
| # --- Display results --- | |
| if mgf_bytes and json_bytes: | |
| st.success("Files are ready to use!") | |
| else: | |
| st.info("Please upload your files or 'Use Example Files'.") | |
| # ============================== | |
| # Model Selection and Run Button | |
| # ============================== | |
| model_choice = st.selectbox( | |
| "Select model to use:", | |
| options=["binnedSpec", "formSpec"] | |
| ) | |
| run_button = st.button("🚀 Run Prediction") | |
| # ============================== | |
| # Run Prediction | |
| # ============================== | |
| if run_button: | |
| if not mgf_bytes or not json_bytes: | |
| st.error("Please upload both a spectra (.mgf) and candidates (.json) file.") | |
| else: | |
| with st.spinner("Running predictions... please wait ⏳", show_time=True): | |
| # Save uploaded files to temporary paths | |
| st.write("Saving files to temporary paths...") | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".mgf") as tmp_mgf: | |
| tmp_mgf.write(mgf_bytes) | |
| mgf_path = tmp_mgf.name | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".json") as tmp_json: | |
| tmp_json.write(json_bytes) | |
| candidates_pth = tmp_json.name | |
| # Check number of pairs in candidates file | |
| st.write("Checking number of pairs in candidates file...") | |
| with open(candidates_pth, 'r') as f: | |
| candidates_data = json.load(f) | |
| total_pairs = sum(len(cands) for cands in candidates_data.values()) | |
| if total_pairs > 1000: | |
| st.error(f"⚠️ Too many pairs ({total_pairs})! Please limit to 1,000 pairs for the web app.") | |
| st.stop() | |
| # preprocess spectra | |
| st.write("Preprocessing spectra...") | |
| from utils_app import preprocess_spectra, setup_config, run_inference | |
| dataset_pth, subformula_dir = preprocess_spectra(mgf_path, model_choice, mass_diff_thresh=20) | |
| if dataset_pth is None: | |
| st.error("Error in preprocessing spectra. Please check your input files.") | |
| if model_choice == "formSpec": | |
| st.info("Make sure that for 'formSpec' model, each spectrum has 'formula' and 'adduct' metadata.") | |
| st.stop() | |
| # Prepare model config paths | |
| st.write("Preparing model config paths...") | |
| params = setup_config(model_choice, dataset_pth, candidates_pth, subformula_dir) | |
| try: | |
| st.write("Running inference...") | |
| run_inference(params) | |
| except Exception as e: | |
| st.error(f"Error running model inference: {e}") | |
| st.stop() | |
| # Convert to CSV | |
| st.write("Converting to CSV...") | |
| df = pd.read_pickle(params['df_test_path']) | |
| csv_path = params['df_test_path'].replace(".pkl", ".csv") | |
| df.to_csv(csv_path, index=False) | |
| st.success(f"✅ Done! Model: {model_choice}") | |
| st.download_button( | |
| label="📥 Download Results CSV", | |
| data=open(csv_path, "rb").read(), | |
| file_name=os.path.basename(csv_path), | |
| mime="text/csv" | |
| ) | |
| st.info("To run larger datasets or enable GPU acceleration, please clone the repo and run locally.") | |
| # ============================== | |
| # Footer | |
| # ============================== | |
| st.markdown("---") | |