Update app.py

app.py

@@ -1,60 +1,92 @@
-from fastapi import FastAPI, Query
-from fastapi.middleware.cors import CORSMiddleware
+import streamlit as st
 import pandas as pd
-from typing import List, Optional
-import uvicorn
-
-app = FastAPI()
-
-# Enable CORS for frontend
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Or restrict to your frontend origin
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Load Excel into memory
-df = pd.read_excel("data.xlsx", engine="openpyxl")
-
-# Smart URL resolver
-def generate_smart_url(entity: str, name: str) -> str:
-    base_urls = {
-        "ISO": "https://www.iso.org/search.html?q=",
-        "IEEE": "https://ieeexplore.ieee.org/search/searchresult.jsp?newsearch=true&queryText=",
-        "IEC": "https://webstore.iec.ch/searchform?q=",
-        "API": "https://www.api.org/products-and-services/standards"
+import requests
+import os
+
+# ----------------------------
+# Configuration
+# ----------------------------
+st.set_page_config(page_title="Instrumentation Standards AI Assistant", layout="wide")
+
+# ----------------------------
+# Load Data
+# ----------------------------
+@st.cache_data
+def load_data():
+    df = pd.read_excel("data.xlsx", engine='openpyxl')
+    return df
+
+df = load_data()
+
+# ----------------------------
+# GROQ API Summarizer
+# ----------------------------
+GROQ_API_KEY = os.getenv("Standardssearch")  # Set this in your environment or .streamlit/secrets.toml
+
+def summarize_with_groq(text, model="llama3-70b-8192"):
+    url = "https://api.groq.com/openai/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": model,
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant summarizing industrial standards for instrumentation and control engineers."},
+            {"role": "user", "content": f"Summarize the following technical standard for a professional audience:\n\n{text}"}
+        ],
+        "temperature": 0.5
     }
-    for key in base_urls:
-        if key.lower() in entity.lower():
-            return base_urls[key] + name.replace(" ", "+")
-    return "https://www.google.com/search?q=" + entity + "+" + name.replace(" ", "+")
-
-@app.get("/search")
-def search_standards(entity: Optional[str] = Query(None), name: Optional[str] = Query(None)):
-    results = df.copy()
-
-    if entity:
-        results = results[results["Standards Entity"].str.contains(entity, case=False, na=False)]
-    if name:
-        results = results[results["Standard Name"].str.contains(name, case=False, na=False)]
-
-    if results.empty:
-        return []
-
-    standards = []
-    for _, row in results.iterrows():
-        standard = {
-            "Standards Entity": row["Standards Entity"],
-            "Standard Name": row["Standard Name"],
-            "Description": row["Description"],
-            "URL": row["URL"] if pd.notna(row.get("URL")) else generate_smart_url(row["Standards Entity"], row["Standard Name"])
-        }
-        standards.append(standard)
-
-    return standards
-
-# Optional: for local testing
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+    response = requests.post(url, headers=headers, json=payload)
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"]
+    else:
+        return f"Error: {response.status_code} - {response.text}"
+
+# ----------------------------
+# UI Layout
+# ----------------------------
+
+st.title("📘 Instrumentation & Control Engineering Standards")
+st.markdown("This app helps Instrumentation & Control Engineers search and understand global engineering standards.")
+st.markdown("**Standards included:** ANSI, API, ASME, ASTM, BS, IEC, ISA, ISO, MSS, NACE, NAMUR, NFPA, PIP, EN, and more.")
+
+# Search Filters
+st.subheader("🔍 Search for Industrial Standards")
+col1, col2 = st.columns(2)
+
+with col1:
+    entity_input = st.text_input("Enter Standard Entity").strip()
+
+with col2:
+    name_input = st.text_input("Enter Standard Name").strip()
+
+# Filtered Results
+if entity_input or name_input:
+    filtered_df = df[
+        df["Standards Entity"].str.contains(entity_input, case=False, na=False) &
+        df["Standard Name"].str.contains(name_input, case=False, na=False)
+    ]
+    
+    if not filtered_df.empty:
+        st.success(f"✅ Found {len(filtered_df)} matching standard(s). Click to expand.")
+
+        for index, row in filtered_df.iterrows():
+            with st.expander(f"📘 {row['Standard Name']}"):
+                st.markdown(f"**Entity:** {row['Standards Entity']}")
+                st.markdown(f"**Description:** {row['Description']}")
+                
+                if st.button("Summarize with AI", key=f"summarize_{index}"):
+                    with st.spinner("Querying GROQ model for summary..."):
+                        summary = summarize_with_groq(row["Description"])
+                        st.markdown("### ✨ AI Summary")
+                        st.write(summary)
+    else:
+        st.warning("No matching standards found.")
+else:
+    st.info("Enter at least one search term to begin.")
+
+# Footer
+st.markdown("---")
+st.caption("Built with ❤️ for Control & Instrumentation Engineers | Powered by GROQ + Streamlit")