Update app.py

app.py

@@ -591,21 +591,130 @@ if "sites" not in st.session_state:
         }
     ]
 
+# -------------------
+# Session state defaults
+# -------------------
 if "active_site" not in st.session_state:
     st.session_state["active_site"] = 0
 
 if "llm_model" not in st.session_state:
     st.session_state["llm_model"] = "groq/compound"
 
-MAX_SITES = 10  # optional: maximum number of sites
 
 # -------------------
-# 2) Sidebar: Site management
+# API Keys
+# -------------------
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+GEM_API_KEY = None
+if "GEM_API" in st.secrets:
+    GEM_API_KEY = st.secrets["GEM_API"]
+
+
+# -------------------
+# Universal LLM Call
+# -------------------
+def llm_generate(prompt: str, model: str = None, max_tokens: int = 512) -> str:
+    """Universal LLM call for Groq, Gemini, DeepSeek."""
+    model_name = model or st.session_state["llm_model"]
+
+    try:
+        # ------------------- GEMINI -------------------
+        if model_name.lower().startswith("gemini"):
+            import requests
+            if not GEM_API_KEY:
+                return "[LLM error: No GEM_API key found in secrets]"
+
+            url = (
+                f"https://generativelanguage.googleapis.com/v1beta/models/"
+                f"{model_name}:generateContent?key={GEM_API_KEY}"
+            )
+            headers = {"Content-Type": "application/json"}
+            payload = {
+                "contents": [{"role": "user", "parts": [{"text": prompt}]}],
+                "generationConfig": {
+                    "temperature": 0.2,
+                    "maxOutputTokens": max_tokens
+                }
+            }
+            resp = requests.post(url, headers=headers, json=payload, timeout=60)
+            resp.raise_for_status()
+            data = resp.json()
+            return data["candidates"][0]["content"]["parts"][0]["text"].strip()
+
+        # ------------------- GROQ / DEEPSEEK / LLAMA -------------------
+        elif "groq" in model_name or "llama" in model_name or "deepseek" in model_name:
+            from groq import Groq
+            if not GROQ_API_KEY:
+                return "[LLM error: No GROQ_API key found in secrets/env]"
+
+            client = Groq(api_key=GROQ_API_KEY)
+            completion = client.chat.completions.create(
+                model=model_name,
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.2,
+                max_tokens=max_tokens
+            )
+            return completion.choices[0].message.content
+
+        else:
+            return f"[LLM error: Unknown model {model_name}]"
+
+    except Exception as e:
+        return f"[LLM error: {e}]"
+
+
+# -------------------
+# UI helper: CSS
 # -------------------
+st.markdown("""
+<style>
+/* Background and card styling */
+body { background: #0b0b0b; color: #e9eef6; }
+.stApp > .main > .block-container { padding-top: 18px; }
+
+/* Landing and cards */
+.gm-card { background: linear-gradient(180deg, rgba(255,122,0,0.04), rgba(255,122,0,0.02));
+           border-radius:12px; padding:14px; border:1px solid rgba(255,122,0,0.06);}
+.gm-cta { background: linear-gradient(90deg,#ff7a00,#ff3a3a); color:white;
+          padding:10px 14px; border-radius:10px; font-weight:700; }
+
+/* Chat bubbles */
+.chat-bot { background: #0f1720; border-left:4px solid #FF7A00;
+            padding:10px 12px; border-radius:12px; margin:6px 0; color:#e9eef6; }
+.chat-user { background: #1a1f27; padding:10px 12px; border-radius:12px;
+             margin:6px 0; color:#cfe6ff; text-align:right;}
+.small-muted { color:#9aa7bf; font-size:12px; }
+</style>
+""", unsafe_allow_html=True)
+
+
+# -------------------
+# Sidebar: Navigation + Model + Site Management
+# -------------------
+from streamlit_option_menu import option_menu
+
 with st.sidebar:
     st.markdown("<h2 style='color:#FF8C00;margin:8px 0'>GeoMate V2</h2>", unsafe_allow_html=True)
 
-    # Site management controls
+    # -------------------
+    # LLM model selector
+    # -------------------
+    st.session_state["llm_model"] = st.selectbox("Select LLM model", options=[
+        "meta-llama/llama-4-maverick-17b-128e-instruct",
+        "llama-3.1-8b-instant",
+        "meta-llama/llama-guard-4-12b",
+        "llama-3.3-70b-versatile",
+        "groq/compound",
+        "deepseek-r1-distill-llama-70b",   # ✅ DeepSeek
+        "gemini-1.5-pro",                  # ✅ Gemini Pro
+        "gemini-1.5-flash"                 # ✅ Gemini Flash
+    ], index=0)
+
+    st.markdown("---")
+
+    # -------------------
+    # Site management
+    # -------------------
     st.markdown("### Project Sites")
 
     site_names = [s.get("Site Name", f"Site {i}") for i, s in enumerate(st.session_state["sites"])]
@@ -1314,66 +1423,65 @@ def locator_page():
                     st.session_state["sites"][active]["map_snapshot"] = map_bytes
                 st.image(map_bytes, caption="Map Snapshot", use_column_width=True)
 
-            import matplotlib.pyplot as plt
-
-            # Custom theme colors (orange/black/gray)
-            COLOR_SCHEME = ["#FF6600", "#333333", "#FF9933", "#666666", "#FFCC99"]
+            import plotly.express as px
+            import plotly.graph_objects as go
             
-            # --- Display results
+            # -------------------------------
+            # 📊 Display Summaries (Locator)
+            # -------------------------------
             st.subheader("📊 Summary")
             
-            st.write(f"**Soil:** {soil_val}")
-            st.write(f"**Elevation:** {elev_val} m")
-            st.write(f"**Seismic:** {seismic_val}")
-            st.write(f"**Flood:** {flood_val}")
-            st.write(f"**PM2.5 Index:** {pm25_val}")
+            # --- Metric Cards
+            c1, c2, c3 = st.columns(3)
+            c1.metric("🟤 Soil (%)", f"{soil_val:.2f}" if soil_val else "N/A", help="Soil clay content")
+            c2.metric("⛰️ Elevation (m)", f"{elev_val:.1f}" if elev_val else "N/A", help="Mean elevation")
+            c3.metric("🌪️ Seismic PGA", f"{seismic_val:.3f}" if seismic_val else "N/A", help="Seismic hazard index")
             
-            # --- Landcover pie chart
+            c4, c5, c6 = st.columns(3)
+            c4.metric("🌊 Flood Occurrence", f"{flood_val:.2f}" if flood_val else "N/A")
+            c5.metric("💨 PM2.5 Index", f"{pm25_val:.2f}" if pm25_val else "N/A")
+            c6.metric("🟢 NDVI Count", f"{len(ndvi_ts)} pts" if ndvi_ts else "0")
+            
+            # --- Pie Chart for Landcover
+            # --- Donut Chart for Landcover
             if lc_stats:
-                st.markdown("#### 🌱 Landcover Distribution")
-                labels = list(lc_stats.keys())
+                labels = list(map(str, lc_stats.keys()))
                 values = list(lc_stats.values())
-                fig, ax = plt.subplots()
-                ax.pie(values, labels=labels, autopct="%1.1f%%", startangle=90,
-                       colors=COLOR_SCHEME, wedgeprops={'edgecolor': 'white'})
-                ax.set_aspect("equal")
-                st.pyplot(fig)
-            
-            # --- NDVI time series
-            if ndvi_ts:
-                st.markdown("#### 🌿 NDVI Trend (2 years)")
-                d, v = zip(*ndvi_ts)
-                fig, ax = plt.subplots()
-                ax.plot(d, v, marker="o", color=COLOR_SCHEME[0])
-                ax.set_title("NDVI", fontsize=12, color=COLOR_SCHEME[1])
-                ax.set_xlabel("Date")
-                ax.set_ylabel("NDVI")
-                ax.grid(True, linestyle="--", alpha=0.6)
-                st.pyplot(fig)
+                fig = go.Figure(data=[go.Pie(
+                    labels=labels,
+                    values=values,
+                    hole=0.5,  # donut
+                    textinfo="percent+label",  # show % and class
+                    insidetextorientation="radial",
+                    marker=dict(colors=px.colors.sequential.Oranges_r)
+                )])
+                fig.update_layout(
+                    title="🌍 Landcover Distribution",
+                    template="plotly_dark",
+                    margin=dict(l=20, r=20, t=40, b=20)
+                )
+                st.plotly_chart(fig, use_container_width=True)
+
             
-            # --- Precipitation
-            if precip_ts:
-                st.markdown("#### 🌧️ Precipitation Trend (1 year)")
-                d, v = zip(*precip_ts)
-                fig, ax = plt.subplots()
-                ax.plot(d, v, marker="o", color=COLOR_SCHEME[2])
-                ax.set_title("Precipitation (mm)", fontsize=12, color=COLOR_SCHEME[1])
-                ax.set_xlabel("Date")
-                ax.set_ylabel("Rainfall (mm)")
-                ax.grid(True, linestyle="--", alpha=0.6)
-                st.pyplot(fig)
+            # --- Time Series with Plotly
+            def plot_timeseries(ts, title, ylab, color):
+                if ts:
+                    dates, vals = zip(*ts)
+                    fig = go.Figure()
+                    fig.add_trace(go.Scatter(x=dates, y=vals, mode="lines+markers", line=dict(color=color)))
+                    fig.update_layout(
+                        title=title,
+                        xaxis_title="Date",
+                        yaxis_title=ylab,
+                        template="plotly_dark",
+                        hovermode="x unified",
+                        margin=dict(l=20, r=20, t=40, b=20)
+                    )
+                    st.plotly_chart(fig, use_container_width=True)
             
-            # --- Temperature
-            if temp_ts:
-                st.markdown("#### 🌡️ Land Surface Temp (1 year)")
-                d, v = zip(*temp_ts)
-                fig, ax = plt.subplots()
-                ax.plot(d, v, marker="o", color=COLOR_SCHEME[3])
-                ax.set_title("LST Temperature (K)", fontsize=12, color=COLOR_SCHEME[1])
-                ax.set_xlabel("Date")
-                ax.set_ylabel("Kelvin (K)")
-                ax.grid(True, linestyle="--", alpha=0.6)
-                st.pyplot(fig)
+            plot_timeseries(ndvi_ts, "🌿 NDVI Trend (2 years)", "NDVI", "#FF7A00")
+            plot_timeseries(precip_ts, "🌧️ Precipitation Trend (1 year)", "mm/day", "#00BFFF")
+            plot_timeseries(temp_ts, "🌡️ Land Surface Temp (1 year)", "K", "#FF3333")
 
 
 # GeoMate Ask (RAG) — simple chat with memory per site and auto-extract numeric values
@@ -1904,6 +2012,27 @@ if "page" not in st.session_state:
     st.session_state["page"] = "Home"
 
 # Build horizontal option menu
+# ===============================
+# Sidebar or Top-Bar Model Selector
+# ===============================
+with st.sidebar:
+    st.markdown("### ⚙️ LLM Model Selector")
+    st.session_state["llm_model"] = st.selectbox(
+        "Choose AI Model",
+        [
+            "groq/compound",                  # Groq general model
+            "deepseek-r1-distill-llama-70b",  # DeepSeek
+            "gemini-1.5-pro",                 # Gemini
+        ],
+        index=0 if "llm_model" not in st.session_state else
+        ["groq/compound", "deepseek-r1-distill-llama-70b", "gemini-1.5-pro"].index(
+            st.session_state.get("llm_model", "groq/compound")
+        )
+    )
+
+# ===============================
+# Page Menu (Horizontal)
+# ===============================
 selected = option_menu(
     None,
     PAGES,
@@ -1930,6 +2059,7 @@ st.session_state["page"] = selected
 if selected == "Home":
     st.title("🏠 Welcome to GeoMate")
     st.write("Your geotechnical AI copilot.")
+    st.info(f"Currently using **{st.session_state['llm_model']}** for analysis.")
 
 elif selected == "Soil recognizer":
     st.title("🔎 Soil Recognizer")
@@ -1954,10 +2084,12 @@ elif selected == "Locator":
 elif selected == "RAG":
     st.title("🤖 Knowledge Assistant")
     st.write("Query soil and geotechnical references with AI.")
+    st.caption(f"Model in use: {st.session_state['llm_model']}")
 
 elif selected == "Reports":
     st.title("📑 Reports")
     st.write("Generate classification and full reports.")
+    st.caption(f"Analysis will run with: {st.session_state['llm_model']}")
 
 
 # Display page content