Update app.py

app.py

@@ -29,59 +29,55 @@ client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 # Initialize HuggingFace embeddings for FAISS
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
-# Custom CSS (aligned with previous apps)
+# Custom CSS with Silver, Blue, and Gold Theme + Responsiveness
 st.markdown("""
     <style>
     :root {
-        --primary-blue: #3B82F6;
-        --dark-blue: #1E40AF;
-        --light-blue: #DBEAFE;
-        --medium-grey: #6B7280;
-        --light-grey: #F3F4F6;
-        --white: #FFFFFF;
-        --border-grey: #E5E7EB;
+        --silver: #D8D8D8;
+        --blue: #5C89BC;
+        --gold: #A87E01;
+        --text-color: #333333;
     }
     .stApp {
-        background-color: var(--light-grey);
+        background-color: var(--silver);
         font-family: 'Inter', sans-serif;
         max-width: 900px;
         margin: 0 auto;
+        padding: 10px;
     }
     .header {
-        background-color: var(--white);
-        border-bottom: 2px solid var(--border-grey);
+        background-color: var(--blue);
+        color: white;
         padding: 15px;
-        border-radius: 12px 12px 0 0;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+        border-radius: 5px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
         text-align: center;
     }
     .header-title {
-        color: var(--dark-blue);
         font-size: 1.5rem;
         font-weight: 700;
         margin: 0;
     }
     .header-subtitle {
-        color: var(--medium-grey);
         font-size: 0.9rem;
         margin-top: 5px;
     }
     .sidebar .sidebar-content {
-        background-color: var(--white);
-        border-radius: 12px;
-        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        background-color: white;
+        border-radius: 5px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
         padding: 15px;
     }
     .chat-container {
-        background-color: var(--white);
-        border-radius: 12px;
-        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        background-color: white;
+        border-radius: 5px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
         padding: 15px;
         margin-top: 20px;
     }
     .user-message {
-        background-color: var(--primary-blue);
-        color: var(--white);
+        background-color: var(--blue);
+        color: white;
         border-radius: 18px 18px 4px 18px;
         padding: 12px 16px;
         margin-left: auto;
@@ -89,8 +85,8 @@ st.markdown("""
         margin-bottom: 10px;
     }
     .bot-message {
-        background-color: var(--light-grey);
-        color: var(--medium-grey);
+        background-color: #F0F0F0;
+        color: var(--text-color);
         border-radius: 18px 18px 18px 4px;
         padding: 12px 16px;
         margin-right: auto;
@@ -100,24 +96,57 @@ st.markdown("""
     .footer {
         text-align: center;
         margin-top: 20px;
-        color: var(--medium-grey);
+        color: var(--text-color);
         font-size: 0.8rem;
     }
     .tech-badge {
         display: inline-block;
-        background-color: var(--light-blue);
-        color: var(--dark-blue);
+        background-color: #E6ECEF;
+        color: var(--blue);
         padding: 4px 8px;
         border-radius: 12px;
         font-size: 0.7rem;
         margin: 0 4px;
     }
+    h2 {
+        color: var(--blue);
+        border-bottom: 2px solid var(--gold);
+        padding-bottom: 5px;
+    }
+    .stButton > button {
+        background-color: var(--gold);
+        color: white;
+        border-radius: 5px;
+        padding: 8px 16px;
+        border: none;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    .stButton > button:hover {
+        background-color: #8C6B01;
+    }
+    @media (max-width: 768px) {
+        .header-title {
+            font-size: 1.2rem;
+        }
+        .header-subtitle {
+            font-size: 0.8rem;
+        }
+        .chat-container, .sidebar .sidebar-content {
+            padding: 10px;
+        }
+        .stApp {
+            padding: 5px;
+        }
+        h2 {
+            font-size: 1.2rem;
+        }
+    }
     </style>
 """, unsafe_allow_html=True)
 
-# Helper Functions
+# Helper Functions (unchanged)
 def enhance_section_title(title):
-    st.markdown(f"<h2 style='border-bottom: 2px solid var(--border-grey); padding-bottom: 5px; color: var(--dark-blue);'>{title}</h2>", unsafe_allow_html=True)
+    st.markdown(f"<h2 style='border-bottom: 2px solid var(--gold); padding-bottom: 5px; color: var(--blue);'>{title}</h2>", unsafe_allow_html=True)
 
 def update_cleaned_data(df):
     st.session_state.cleaned_data = df
@@ -129,7 +158,6 @@ def update_cleaned_data(df):
     st.rerun()
 
 def convert_df_to_text(df):
-    """Convert DataFrame to text for vector store and context"""
     text = f"Dataset Summary: {df.shape[0]} rows, {df.shape[1]} columns\n"
     text += f"Missing Values: {df.isna().sum().sum()}\n"
     text += "Columns:\n"
@@ -143,11 +171,9 @@ def convert_df_to_text(df):
     return text
 
 def create_vector_store(df_text):
-    """Create a FAISS vector store from dataset text"""
     with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as temp_file:
         temp_file.write(df_text)
         temp_path = temp_file.name
-    
     loader = TextLoader(temp_path)
     documents = loader.load()
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
@@ -157,36 +183,29 @@ def create_vector_store(df_text):
     return vector_store
 
 def update_vector_store_with_plot(plot_text, existing_vector_store):
-    """Update the FAISS vector store with plot-derived text"""
     with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as temp_file:
         temp_file.write(plot_text)
         temp_path = temp_file.name
-    
     loader = TextLoader(temp_path)
     documents = loader.load()
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
     texts = text_splitter.split_documents(documents)
-    
     if existing_vector_store:
         existing_vector_store.add_documents(texts)
     else:
         existing_vector_store = FAISS.from_documents(texts, embeddings)
-    
     os.unlink(temp_path)
     return existing_vector_store
 
 def extract_plot_data(plot_info, df):
-    """Extract numerical data from the last generated plot and convert to text"""
     plot_type = plot_info["type"]
     x_col = plot_info["x"]
     y_col = plot_info["y"] if "y" in plot_info else None
     data = pd.read_json(plot_info["data"])
-    
     plot_text = f"Plot Type: {plot_type}\n"
     plot_text += f"X-Axis: {x_col}\n"
     if y_col:
         plot_text += f"Y-Axis: {y_col}\n"
-    
     if plot_type == "Scatter Plot" and y_col:
         correlation = data[x_col].corr(data[y_col])
         slope, intercept, r_value, p_value, std_err = stats.linregress(data[x_col].dropna(), data[y_col].dropna())
@@ -212,13 +231,11 @@ def extract_plot_data(plot_info, df):
         plot_text += "Correlation Matrix:\n"
         for col1 in corr.columns:
             for col2 in corr.index:
-                if col1 < col2:  # Avoid duplicates
+                if col1 < col2:
                     plot_text += f"{col1} vs {col2}: {corr.loc[col2, col1]:.2f}\n"
-    
     return plot_text
 
 def get_chatbot_response(user_input, app_mode, vector_store=None, model="llama3-70b-8192"):
-    """Get response from Groq with vector store context including plot data"""
     system_prompt = (
         "You are an AI assistant in Data-Vision Pro, a data analysis app with RAG capabilities. "
         f"The user is on the '{app_mode}' page:\n"
@@ -227,7 +244,6 @@ def get_chatbot_response(user_input, app_mode, vector_store=None, model="llama3-
         "- **EDA**: Visualize data (e.g., scatter plots, histograms) and analyze plots.\n"
         "When analyzing plots, provide detailed insights based on numerical data extracted from them."
     )
-    
     context = ""
     if vector_store:
         docs = vector_store.similarity_search(user_input, k=3)
@@ -236,7 +252,6 @@ def get_chatbot_response(user_input, app_mode, vector_store=None, model="llama3-
             system_prompt += f"Use this dataset and plot context to augment your response:\n{context}"
     else:
         system_prompt += "No dataset or plot data is loaded. Assist based on app functionality."
-    
     try:
         response = client.chat.completions.create(
             model=model,
@@ -410,9 +425,11 @@ def main():
         if 'raw_data' not in st.session_state:
             st.warning("Please upload data first in the Data Upload section.")
             st.stop()
-        if 'cleaned_data' not in st.session_state:
+        if 'cleaned_data' in st.session_state:
+            df = st.session_state.cleaned_data.copy()
+        else:
             st.session_state.cleaned_data = st.session_state.raw_data.copy()
-        df = st.session_state.cleaned_data.copy()
+            df = st.session_state.cleaned_data.copy()
 
         enhance_section_title("📊 Data Health Dashboard")
         with st.expander("Explore Data Health Metrics", expanded=True):
@@ -579,7 +596,6 @@ def main():
                             "y": y_axis,
                             "data": df[[x_axis, y_axis]].to_json() if y_axis else df[[x_axis]].to_json()
                         }
-                        # Extract numerical data and update vector store
                         plot_text = extract_plot_data(st.session_state.last_plot, df)
                         st.session_state.vector_store = update_vector_store_with_plot(plot_text, st.session_state.vector_store)
                         with st.expander("Extracted Plot Data"):