Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load the model and tokenizer
+def load_model():
+    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+    model = GPT2LMHeadModel.from_pretrained("gpt2")
+    return pipeline('text-generation', model=model, tokenizer=tokenizer)
+
+text_generator = load_model()
+
+def generate_sql_query(prompt):
+    response = text_generator(prompt, max_length=100, num_return_sequences=1)
+    return response[0]['generated_text'].strip()
+
+def execute_sql_query(sql_query, data):
+    # In this case, we'll directly use the pandas DataFrame as our data source
+    result = pd.read_csv('Movies.csv')
+    return result
+
+def analyze_and_visualize(data, prompt):
+    if 'trend' in prompt.lower() or 'time' in data.columns:
+        vis_type = 'line'
+    elif data.select_dtypes(include=['object']).shape[1] > 0:
+        vis_type = 'bar'
+    else:
+        vis_type = 'scatter'
+    
+    plt.figure(figsize=(10, 6))
+    if vis_type == 'line':
+        sns.lineplot(data=data)
+        plt.title('Trend Analysis')
+    elif vis_type == 'bar':
+        categorical_column = data.select_dtypes(include=['object']).columns[0]
+        numerical_column = data.select_dtypes(include=['number']).columns[0]
+        sns.barplot(x=categorical_column, y=numerical_column, data=data)
+        plt.title('Categorical Data Analysis')
+    else:
+        sns.scatterplot(data=data)
+        plt.title('Scatter Plot')
+    
+    plt.xlabel('X-axis Label')
+    plt.ylabel('Y-axis Label')
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    return plt  # Return the plot object instead of showing it
+
+# Streamlit app
+def main():
+    st.title("Conversational BI Bot")
+    prompt = st.text_input("Enter your query:", "")
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    
+    if st.button("Generate and Visualize") and uploaded_file is not None:
+        if prompt:
+            with st.spinner('Generating SQL query...'):
+                sql_query = generate_sql_query(prompt)
+                st.write(f"Generated SQL Query: `{sql_query}`")
+                
+                with st.spinner('Executing SQL query...'):
+                    data = execute_sql_query(sql_query, uploaded_file)
+                    st.write("Query Results:", data)
+                    
+                    with st.spinner('Generating Visualization...'):
+                        plot = analyze_and_visualize(data, prompt)
+                        st.pyplot(plot)  # Display the plot using st.pyplot()
+
+if __name__ == "__main__":
+    main()