Create app.py

app.py

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+import streamlit as st
+import requests
+import pandas as pd
+import json
+import plotly.express as px
+import matplotlib.pyplot as plt
+from config import NREL_API_KEY, IEA_API_KEY, IRENA_API_KEY, DOE_API_KEY, GROQ_API_KEY
+from data_analysis import fetch_hydrogen_data, analyze_hydrogen_data, groq_ai_analysis
+
+# Streamlit UI
+st.set_page_config(page_title="AI Hydrogen Electrolysis Dashboard", layout="wide")
+
+# Sidebar - API selection
+st.sidebar.header("🔍 Data Sources")
+api_options = ["NREL", "IEA", "IRENA", "DOE"]
+selected_api = st.sidebar.radio("Select API for Hydrogen Data:", api_options)
+
+# Fetch Data
+hydrogen_data = fetch_hydrogen_data(selected_api)
+
+# If no data, try alternative APIs
+if hydrogen_data.empty:
+    st.warning(f"No data found from {selected_api}. Fetching from alternative sources...")
+    for api in api_options:
+        if api != selected_api:
+            hydrogen_data = fetch_hydrogen_data(api)
+            if not hydrogen_data.empty:
+                st.success(f"Data successfully retrieved from {api}")
+                break
+
+# Display Data
+st.title("🚀 AI-Powered Hydrogen Electrolysis Dashboard")
+st.subheader(f"Real-Time Data from {selected_api}")
+
+if not hydrogen_data.empty:
+    st.dataframe(hydrogen_data)
+
+    # Plot Electrolysis Efficiency vs. Cost
+    st.subheader("📊 Hydrogen Production Trends")
+    fig = px.scatter(hydrogen_data, x="Efficiency (%)", y="Cost ($/kg)", color="Technology",
+                     title="Hydrogen Production Efficiency vs. Cost")
+    st.plotly_chart(fig)
+
+    # Bar Chart - Hydrogen Production Rates by Source
+    st.subheader("⚡ Hydrogen Production Rate by Technology")
+    fig2 = px.bar(hydrogen_data, x="Technology", y="Hydrogen Production Rate (kg/h)",
+                  title="Hydrogen Production Rate by Technology")
+    st.plotly_chart(fig2)
+
+    # AI Prediction & Analysis using Groq API
+    st.subheader("🤖 AI-Powered Electrolysis Analysis")
+    ai_prediction = groq_ai_analysis(hydrogen_data)
+    st.write(ai_prediction)
+
+else:
+    st.error("No data available from all sources. Please try again later.")