Update app.py

app.py

@@ -60,7 +60,7 @@ def preprocess_dataframe(df):
         return None
 
 # Analysis Function
-def analyze_data(data, visualization_type, class_size=10):
+def analyze_data(data, visualization_type):
     st.subheader("Basic Analysis")
     st.write("Shape of Data:", data.shape)
     st.write("Data Types:")
@@ -80,11 +80,7 @@ def analyze_data(data, visualization_type, class_size=10):
         ax.set_ylabel(y_col)
         st.pyplot(fig)
     # Visualization logic
-    elif visualization_type == "Heatmap" and not numeric_data.empty:
-        st.subheader("Correlation Heatmap")
-        fig, ax = plt.subplots(figsize=(8, 6))
-        sns.heatmap(numeric_data.corr(), annot=True, ax=ax, cmap="coolwarm", fmt=".2f")
-        st.pyplot(fig)
+
 
 
 
@@ -115,11 +111,11 @@ def analyze_data(data, visualization_type, class_size=10):
         st.warning("No valid visualization option selected or data available.")
 
     # Automatically generate a prompt for Groq based on the analysis
-    prompt = generate_groq_prompt(data, visualization_type, class_size)
+    prompt = generate_groq_prompt(data, visualization_type)
     return prompt
 
 # Function to generate a prompt based on the data analysis
-def generate_groq_prompt(data, visualization_type, class_size):
+def generate_groq_prompt(data, visualization_type):
     # Convert DataFrame to a string without the index
     data_without_index = data.to_string(index=False)
     
@@ -127,7 +123,7 @@ def generate_groq_prompt(data, visualization_type, class_size):
     Here is the summary statistics for the dataset:
     {data_without_index}
 
-    The user has selected the '{visualization_type}' visualization type with a class size of {class_size}.
+    The user has selected the '{visualization_type}' visualization type .
     Please generate Python code that does this and for any data, please don't use any file input. Write the data in the code.
     """
 
@@ -150,15 +146,15 @@ if uploaded_file is not None:
             # Visualization Selection
             visualization_type = st.selectbox(
                 "Select a visualization type:",
-                ["Heatmap", "Bar Chart", "Line Graph", "Scatter Plot", "Histogram", "Area Chart"]
+                [ "Bar Chart", "Line Graph",  "Area Chart"]
             )
 
             # User input for class size customization
-            class_size = st.slider("Select the class size for certain plots (e.g., Histogram)", 5, 50, 10)
+  
 
             # Perform Analysis and Visualization
-            prompt = analyze_data(data, visualization_type, class_size)
-            st.text(f"Prompt sent to Groq:\n{prompt}")
+            prompt = analyze_data(data, visualization_type)
+
 
             # Chat with Groq Section
             st.subheader("Chat with Groq")