Create app.py

app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import openai
+import os 
+from dotenv import load_dotenv
+
+api_key = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=api_key)
+def query_llm(prompt):
+    """Query OpenAI's GPT model."""
+    try:
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": "You are an expert in MOF material science."},
+                {"role": "user", "content": prompt},
+            ],
+        )
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        st.error(f"Error querying LLM: {e}")
+        return None
+
+
+st.title("MOF Prediction & Functionalization")
+
+
+st.header("MOF Description")
+mof_description = st.text_area("Describe the MOF (e.g., metal, pore size, functional groups)", height=150)  # Adjust height as needed
+
+task = st.selectbox(
+    "Select a task:",
+    ("Predict Properties", "Functionalization Suggestions", "Synthesis Plan")
+)
+
+if task == "Predict Properties":
+    if st.button("Predict Properties"):
+        st.text("Processing...")
+        prompt = f"Predict the CO₂ adsorption capacity and selectivity of a MOF with the following description:\n\n{mof_description}" 
+        prediction = query_llm(prompt)
+        if prediction:
+            st.subheader("Predicted MOF Properties")
+            st.write(prediction)
+
+elif task == "Functionalization Suggestions":
+    st.header("Functionalization Details")
+    if st.button("Get Functionalization Suggestions"):
+        st.text("Processing...")
+        prompt = f"Suggest functionalization strategies for a MOF with the following description to enhance CO₂ capture:\n\n{mof_description}" 
+        func_suggestions = query_llm(prompt)
+        if func_suggestions:
+            st.subheader("Suggested Functionalization Strategies")
+            st.write(func_suggestions)
+
+elif task == "Synthesis Plan":
+    st.header("Synthesis Details")
+    if st.button("Generate Synthesis Plan"):
+        st.text("Processing...")
+        prompt = f"Provide a synthesis plan for a MOF with the following description:\n\n{mof_description}"
+        synthesis_plan = query_llm(prompt)
+        if synthesis_plan:
+            st.subheader("AI-Generated Synthesis Plan")
+            st.write(synthesis_plan)
+
+st.info("Combine with DFT and ML models for high-throughput screening.")