185 lines
6.5 KiB
Python
185 lines
6.5 KiB
Python
import streamlit as st
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import requests
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import pandas as pd
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from functions import cosing_download
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from functions_ui import download_pdf
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API_BASE = "https://api.cosmoguard.it/api/v1"
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st.set_page_config(page_title="Ricerca Ingredienti", layout="wide")
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st.title("Ricerca Ingredienti per CAS")
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if "ingredient_data" not in st.session_state:
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st.session_state.ingredient_data = None
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if "ingredient_cas" not in st.session_state:
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st.session_state.ingredient_cas = ""
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cas_input = st.session_state.selected_cas
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force_refresh = st.session_state.force_refresh if "force_refresh" in st.session_state else False
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if cas_input:
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with st.spinner(f"Ricerca in corso per {cas_input}..."):
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try:
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resp = requests.post(
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f"{API_BASE}/ingredients/search",
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json={"cas": cas_input, "force": force_refresh},
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timeout=120,
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)
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result = resp.json()
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if result.get("success") and result.get("data"):
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st.session_state.ingredient_data = result["data"]
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st.session_state.ingredient_cas = cas_input
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st.success(f"Ingrediente {cas_input} trovato")
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else:
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st.session_state.ingredient_data = None
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st.error(result.get("error", f"Nessun dato per CAS {cas_input}"))
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except requests.ConnectionError:
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st.error("Impossibile connettersi all'API. Verifica che il server sia attivo.")
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except Exception as e:
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st.error(f"Errore: {e}")
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data = st.session_state.ingredient_data
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if data is None:
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st.info("Effettua una ricerca per visualizzare i risultati.")
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else:
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cas = data.get("cas", "")
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st.subheader(f"Ingrediente: {cas}")
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# --- Header con INCI e data ---
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col_h1, col_h2 = st.columns(2)
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with col_h1:
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inci = data.get("inci") or []
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st.markdown(f"**INCI:** {', '.join(inci) if inci else 'N/A'}")
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with col_h2:
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st.markdown(f"**Data creazione:** {data.get('creation_date', 'N/A')}")
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st.markdown("---")
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# --- DAP Info ---
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st.subheader("DAP Info")
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dap = data.get("dap_info")
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if dap:
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col_d1, col_d2, col_d3 = st.columns(3)
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with col_d1:
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st.metric("Peso Molecolare (Da)", dap.get("molecular_weight", "N/A"))
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st.metric("Log Pow", dap.get("log_pow", "N/A"))
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with col_d2:
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st.metric("TPSA", dap.get("tpsa", "N/A"))
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st.metric("Punto Fusione (C)", dap.get("melting_point") or "N/A")
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with col_d3:
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dap_val = dap.get("dap_value", 0.5)
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st.metric("DAP Value", f"{dap_val * 100:.0f}%")
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st.metric("Alta Ionizzazione", dap.get("high_ionization") or "N/A")
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else:
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st.warning("Dati DAP non disponibili")
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st.markdown("---")
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# --- COSING Info ---
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st.subheader("COSING Info")
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cosing_list = data.get("cosing_info")
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if cosing_list:
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for i, cosing in enumerate(cosing_list):
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if len(cosing_list) > 1:
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st.markdown(f"**Voce {i + 1}**")
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col_c1, col_c2 = st.columns(2)
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with col_c1:
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names = cosing.get("common_names", [])
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st.markdown(f"**Nomi comuni:** {', '.join(names) if names else 'N/A'}")
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st.markdown(f"**INCI:** {', '.join(cosing.get('inci', [])) or 'N/A'}")
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st.markdown(f"**CAS:** {', '.join(cosing.get('cas', [])) or 'N/A'}")
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funcs = cosing.get("functionName", [])
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if funcs:
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st.markdown("**Funzioni:**")
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for f in funcs:
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st.markdown(f"- {f}")
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ref_no = cosing.get("reference", "")
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if ref_no:
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pdf_bytes = cosing_download(ref_no)
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if isinstance(pdf_bytes, bytes):
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st.download_button(
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label="Download CosIng PDF",
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data=pdf_bytes,
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file_name=f"{cas_input}_cosing.pdf",
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mime="application/pdf"
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)
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else:
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st.error(pdf_bytes)
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with col_c2:
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annex = cosing.get("annex", [])
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if annex:
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st.markdown("**Annex/Restrizioni:**")
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for a in annex:
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st.markdown(f"- {a}")
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else:
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st.markdown("**Annex:** Nessuna restrizione")
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other = cosing.get("otherRestrictions", [])
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if other:
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st.markdown("**Altre restrizioni:**")
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for o in other:
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st.markdown(f"- {o}")
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restriction = cosing.get("cosmeticRestriction", "")
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if restriction:
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st.warning(f"Restrizione cosmetica: {restriction}")
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link_opinions = cosing.get("sccsOpinionUrls", [])
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if link_opinions:
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st.markdown("**SCCS Opinions:**")
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for url in link_opinions:
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st.link_button("View SCCS Opinion", url)
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if i < len(cosing_list) - 1:
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st.divider()
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else:
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st.warning("Dati COSING non disponibili")
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st.markdown("---")
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# --- Toxicity ---
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st.subheader("Tossicologia")
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tox = data.get("toxicity")
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if tox:
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# Best case highlight
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best = tox.get("best_case")
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if best:
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st.success(
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f"Miglior indicatore: **{best['indicator']}** = {best['value']} {best['unit']} "
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f"({best['route']}) — Fattore: {tox.get('factor', 'N/A')}"
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)
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download_pdf(
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casNo=cas_input,
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origin=best.get("source"),
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link=best.get("ref")
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)
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# Indicators table
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indicators = tox.get("indicators", [])
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if indicators:
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df = pd.DataFrame(indicators)
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col_order = ["indicator", "value", "unit", "route", "toxicity_type"]
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existing = [c for c in col_order if c in df.columns]
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df = df[existing]
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df.columns = ["Indicatore", "Valore", "Unita", "Via", "Tipo"][:len(existing)]
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st.dataframe(df, width="stretch", hide_index=True)
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else:
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st.warning("Dati tossicologici non disponibili")
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st.markdown("---")
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# --- JSON completo ---
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with st.expander("JSON completo"):
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st.json(data)
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