Upload prediction.py

prediction.py

@@ -0,0 +1,73 @@
+import streamlit as st
+import requests
+from rdkit import Chem
+import datetime
+from db import get_database
+
+# Function to validate SMILES string
+def is_valid_smiles(smiles):
+    """ Validate if the input is a valid SMILES string using RDKit """
+    mol = Chem.MolFromSmiles(smiles)
+    return mol is not None
+
+# Function to save prediction to MongoDB
+def save_to_db(smiles_input, predictions):
+    db = get_database()
+    collection = db["polymers"]  # your collection
+    doc = {
+        "smiles": smiles_input,
+        "predictions": predictions,
+        "timestamp": datetime.datetime.utcnow()
+    }
+    collection.insert_one(doc)
+
+# Streamlit page for Polymer Property Prediction
+def show():
+    st.markdown("<h1 style='text-align: center; color: #4CAF50;'>🔬 Polymer Property Prediction</h1>", unsafe_allow_html=True)
+    st.markdown("<hr style='border: 1px solid #ccc;'>", unsafe_allow_html=True)
+
+    # Input box with placeholder inside
+    input_text = st.text_input(
+        label="",
+        placeholder="Enter SMILES ",
+        key="smiles_input"
+    )
+
+    # Show Predict button always
+    predict_clicked = st.button("🚀 Predict", use_container_width=True)
+
+    # Predict on button click OR on pressing Enter with input
+    if (predict_clicked or input_text) and input_text.strip():
+        with st.spinner("Predicting..."):
+            if is_valid_smiles(input_text.strip()):
+                try:
+                    input_data = {
+                        "smiles": input_text.strip()  # Only sending smiles
+                    }
+                    response = requests.post("http://127.0.0.1:8000/predict", json=input_data)
+                    if response.status_code == 200:
+                        result = response.json()
+                        renamed_properties = {
+                            "property1": "Tensile_strength (Mpa)",
+                            "property2": "Ionization_Energy (eV)",
+                            "property3": "Electron_Affinity (eV)",
+                            "property4": "LogP",
+                            "property5": "Refractive_Index",
+                            "property6": "Molecular_Weight (g/mol)"
+                        }
+                        predictions = {}
+                        for key, name in renamed_properties.items():
+                            value = result.get(key, 'N/A')
+                            st.markdown(f"<div style='font-size:18px; padding: 6px 0;'><strong>{name}:</strong> {value}</div>", unsafe_allow_html=True)
+                            predictions[name] = value
+
+                        # Save prediction to MongoDB
+                        save_to_db(input_text.strip(), predictions)
+
+                        st.success("Prediction saved successfully!")
+                    else:
+                        st.error("Prediction failed. Please try again.")
+                except Exception as e:
+                    st.error(f"Error: {e}")
+            else:
+                st.error("❌ Invalid SMILES input. Please enter a correct SMILES string.")