Create app.py

app.py

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+import streamlit as st
+import os
+from groq import Groq
+
+# Get API Key
+api_key = os.getenv("GROQ_API_KEY")
+
+# Initialize Groq client
+client = Groq(api_key=api_key) if api_key else None
+
+# Streamlit UI
+st.set_page_config(page_title="🧪 ChemEng AI Assistant", page_icon="🧪")
+st.title("🧪 Chemical Engineering Equipment Sizing Assistant")
+
+st.markdown("""
+Ask any question about chemical process equipment sizing and get AI-powered answers based on domain knowledge.
+
+**Try examples like:**
+- `What size of distillation column is used for 5000 kg/hr ethanol separation?`
+- `Size a shell-and-tube heat exchanger for 1 MW heat duty.`
+- `Suggest the volume of a CSTR with 1-hour residence time at 2000 L/hr.`
+""")
+
+# User Input
+query = st.text_area("💬 Enter your question:", height=150)
+
+# Model Selector (Only supported by Groq)
+model = st.selectbox("🔍 Choose a model:", [
+    "llama-3.1-8b-instant",
+    "llama-3.3-70b-versatile",
+    "openai/gpt-oss-120b"
+])
+
+# Process the query
+if st.button("🚀 Get Response"):
+    if not api_key:
+        st.error("🚨 Groq API Key not found. Set the GROQ_API_KEY in your environment.")
+    elif not query.strip():
+        st.warning("⚠️ Please enter a question.")
+    else:
+        with st.spinner("Generating response..."):
+            try:
+                chat_completion = client.chat.completions.create(
+                    model=model,
+                    messages=[
+                        {
+                            "role": "system",
+                            "content": (
+                                "You are a senior chemical engineer. Answer user questions about equipment sizing, "
+                                "design rules, and heuristics clearly and concisely. Provide real-world estimates based on "
+                                "chemical engineering practices."
+                            )
+                        },
+                        {
+                            "role": "user",
+                            "content": query
+                        }
+                    ]
+                )
+                response = chat_completion.choices[0].message.content
+                st.success("✅ Answer:")
+                st.markdown(response)
+
+            except Exception as e:
+                st.error(f"❌ Error: {str(e)}")