Update app.py

app.py

@@ -5,11 +5,8 @@ from pandasai.llm.starcoder import Starcoder
 import matplotlib.pyplot as plt
 import gradio as gr
 
-def plot_chey():
-    a,b = plt.subplots()
-    
-    plt.xlabel('two rs')
-    plt.title('hada')
+def plot_chey(X):
+    a,b = X.figure, X.axes
     return a
     
 title='Plot'
@@ -35,34 +32,14 @@ prompt = 'Plot the histogram of countries showing for each the gdp, using differ
 response = pandas_ai.run(df, prompt=prompt,
                           is_conversational_answer=False) 
 
-heading = '🤗🧡🤍💙 Plot different SVM Classifiers on Iris Dataset'
+heading = 'Pandas AI : Dataframe analytics using text prompt'
 with gr.Blocks(title = heading, theme= 'snehilsanyal/scikit-learn') as demo:
     gr.Markdown("# {}".format(heading))
-    gr.Markdown(
-        """
-        ### This demo visualizes different SVM Classifiers on a 2D projection of the Iris dataset. 
-        
-        <b>The features to be considered are:</b>\
-        \
-        
-        1. Sepal length (cm) \
-        
-        2. Sepal width (cm) \
-        
-        <b>The SVM Classifiers used for this demo are:</b>\
-        \
-        1. SVC with linear kernel \
-        
-        2. Linear SVC \
-        
-        3. SVC with RBF kernel\
-        
-        4. SVC with Polynomial (degree 3) kernel
-        """
-    )
-    gr.Markdown('**[Demo is based on this script from scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py)**')
-    button = gr.Button(value = 'Visualize different SVM Classifiers on Iris Dataset')
-    button.click(plot_chey, outputs = gr.Plot())
+   
+ 
+    
+    button = gr.Button(value = 'submit')
+    button.click(plot_chey(response), outputs = gr.Plot())
 
 demo.launch()
 """