Update app.py

app.py

@@ -24,7 +24,7 @@ embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-
 # Set page config
 st.set_page_config(page_title="Neural-Vision Enhanced", layout="wide")
 
-# Custom CSS for Responsive Silver-Blue-Gold Theme
+# Custom CSS for Responsive Silver-Blue-Gold Theme with Top Nav
 st.markdown("""
     <style>
     :root {
@@ -57,6 +57,27 @@ st.markdown("""
         font-size: 1rem;
         margin-top: 5px;
     }
+    .nav-bar {
+        background-color: white;
+        border-radius: 5px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        padding: 15px;
+        margin-bottom: 20px;
+        display: flex;
+        justify-content: space-around;
+        align-items: center;
+    }
+    .nav-item {
+        color: var(--blue);
+        font-weight: 500;
+        cursor: pointer;
+        padding: 5px 10px;
+        border-radius: 5px;
+    }
+    .nav-item:hover {
+        background-color: var(--gold);
+        color: white;
+    }
     .card {
         background-color: white;
         border-radius: 5px;
@@ -100,12 +121,6 @@ st.markdown("""
     .stButton > button:hover {
         background-color: #8C6B01;
     }
-    .sidebar .sidebar-content {
-        background-color: white;
-        border-radius: 5px;
-        padding: 15px;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-    }
     @media (max-width: 768px) {
         .header-title {
             font-size: 1.4rem;
@@ -113,6 +128,15 @@ st.markdown("""
         .header-subtitle {
             font-size: 0.9rem;
         }
+        .nav-bar {
+            flex-direction: column;
+            padding: 10px;
+        }
+        .nav-item {
+            margin: 5px 0;
+            width: 100%;
+            text-align: center;
+        }
         .card, .chat-container {
             padding: 10px;
         }
@@ -160,7 +184,7 @@ PREBUILT_MODELS = {
     }
 }
 
-# Helper Functions (unchanged from previous)
+# Helper Functions (unchanged)
 def convert_df_to_text(df):
     text = f"Dataset Summary: {df.shape[0]} rows, {df.shape[1]} columns\n"
     text += f"Missing Values: {df.isna().sum().sum()}\n"
@@ -211,16 +235,26 @@ def build_model_from_config(config, X, y=None):
 def main():
     st.markdown('<div class="header"><h1 class="header-title">Neural-Vision Enhanced</h1><p class="header-subtitle">Build & Train Neural Networks</p></div>', unsafe_allow_html=True)
 
-    with st.sidebar:
-        st.markdown('<div class="card"><h3>Data Input</h3></div>', unsafe_allow_html=True)
+    # Top Navigation Bar
+    st.markdown('<div class="nav-bar">', unsafe_allow_html=True)
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col1:
+        st.markdown('<div class="nav-item">Data Input</div>', unsafe_allow_html=True)
         uploaded_file = st.file_uploader("Upload CSV Dataset", type=["csv"])
         if uploaded_file:
             df = pd.read_csv(uploaded_file)
             st.session_state.vector_store = create_vector_store(convert_df_to_text(df))
             st.success("Dataset uploaded!")
+    with col2:
+        st.markdown('<div class="nav-item">Navigation</div>', unsafe_allow_html=True)
+        nav_option = st.selectbox("Navigate", ["Model Builder", "Chat", "Train Model"], label_visibility="collapsed")
+    with col3:
+        st.markdown('<div class="nav-item">Info</div>', unsafe_allow_html=True)
+        st.write("Built with Streamlit & Groq")
+    st.markdown('</div>', unsafe_allow_html=True)
 
-    col1, col2 = st.columns([2, 1])
-    with col1:
+    # Main Content
+    if nav_option == "Model Builder":
         st.markdown('<div class="card"><h2>Model Builder</h2></div>', unsafe_allow_html=True)
         mode = st.selectbox("Domain", ["Legal", "Financial", "Marketing"])
         model_builder_mode = st.radio("Mode", ["Prebuilt", "Custom"])
@@ -248,8 +282,9 @@ def main():
                 st.session_state.model_config = {"type": "clustering", "n_clusters": st.number_input("Clusters", min_value=2, value=3)}
             if st.button("Finalize"): st.json(st.session_state.model_config)
 
-    with col2:
+    elif nav_option == "Chat":
         st.markdown('<div class="chat-container"><h3>Chat with Grok</h3></div>', unsafe_allow_html=True)
+        mode = st.selectbox("Domain", ["Legal", "Financial", "Marketing"])
         prompt = st.text_input("Ask a question:")
         if prompt:
             response = get_groq_response(prompt, mode)
@@ -258,24 +293,27 @@ def main():
         for msg in st.session_state.chat_history:
             st.markdown(f'<div class={"user-message" if msg["role"] == "user" else "bot-message"}>{msg["content"]}</div>', unsafe_allow_html=True)
 
-    if uploaded_file and st.session_state.model_config:
-        st.markdown('<div class="card"><h2>Train Model</h2></div>', unsafe_allow_html=True)
-        df = pd.read_csv(uploaded_file)
-        X = df.drop(columns=[df.columns[-1]]) if st.session_state.model_config["type"] != "clustering" else df
-        y = df[df.columns[-1]] if st.session_state.model_config["type"] != "clustering" else None
-        if st.button("Train"):
-            scaler = StandardScaler()
-            X_scaled = scaler.fit_transform(X)
-            model = build_model_from_config(st.session_state.model_config, X_scaled, y)
-            if st.session_state.model_config["type"] != "clustering":
-                X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
-                model.fit(X_train, y_train)
-                y_pred = model.predict(X_test)
-                st.session_state.metrics = {"accuracy" if st.session_state.model_config["type"] == "classification" else "r2_score": accuracy_score(y_test, y_pred) if st.session_state.model_config["type"] == "classification" else r2_score(y_test, y_pred)}
-            else:
-                model.fit(X_scaled)
-                st.session_state.metrics = {"silhouette_score": silhouette_score(X_scaled, model.labels_)}
-            st.json(st.session_state.metrics)
+    elif nav_option == "Train Model":
+        if uploaded_file and st.session_state.model_config:
+            st.markdown('<div class="card"><h2>Train Model</h2></div>', unsafe_allow_html=True)
+            df = pd.read_csv(uploaded_file)
+            X = df.drop(columns=[df.columns[-1]]) if st.session_state.model_config["type"] != "clustering" else df
+            y = df[df.columns[-1]] if st.session_state.model_config["type"] != "clustering" else None
+            if st.button("Train"):
+                scaler = StandardScaler()
+                X_scaled = scaler.fit_transform(X)
+                model = build_model_from_config(st.session_state.model_config, X_scaled, y)
+                if st.session_state.model_config["type"] != "clustering":
+                    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+                    model.fit(X_train, y_train)
+                    y_pred = model.predict(X_test)
+                    st.session_state.metrics = {"accuracy" if st.session_state.model_config["type"] == "classification" else "r2_score": accuracy_score(y_test, y_pred) if st.session_state.model_config["type"] == "classification" else r2_score(y_test, y_pred)}
+                else:
+                    model.fit(X_scaled)
+                    st.session_state.metrics = {"silhouette_score": silhouette_score(X_scaled, model.labels_)}
+                st.json(st.session_state.metrics)
+        else:
+            st.warning("Upload a dataset and configure a model first!")
 
 if __name__ == "__main__":
     main()