Update app.py

app.py

@@ -1,9 +1,6 @@
 import gradio as gr
 from google import genai
 import os
-import sys
-import io
-import pandas as pd  # We will use Pandas to create a DataFrame
 
 # Load API key from environment variable
 GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
@@ -11,7 +8,7 @@ GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
 # Initialize the Google GenAI client
 client = genai.Client(api_key=GEMINI_API_KEY)
 
-# Function to interact with the Gemini API and generate content or code
+# Function to interact with the Gemini API
 def generate_content(prompt):
     # Generate content using the Gemini API
     response = client.models.generate_content(
@@ -19,71 +16,37 @@ def generate_content(prompt):
         contents=[prompt]
     )
     
-    result_text = response.text.strip()  # Clean response text
-    return result_text
-
-# Function to execute the code generated by Gemini
-def execute_code(code):
-    try:
-        # Redirect stdout to capture print statements from the code
-        output = io.StringIO()
-        sys.stdout = output
-        exec(code)  # Execute the Python code
-        sys.stdout = sys.__stdout__  # Reset stdout
-        return output.getvalue()  # Return captured output
-    except Exception as e:
-        return f"Error: {str(e)}"  # Return error message if execution fails
-
-# Function to format result as a table
-def format_result_as_table(result):
-    try:
-        # Split result into key-value pairs (assuming each line is a pair)
-        rows = [line.split(':') for line in result.split('\n') if ':' in line]
-        # Create DataFrame
-        df = pd.DataFrame(rows, columns=["Key", "Value"])
-        return df
-    except Exception as e:
-        return pd.DataFrame([["Error", str(e)]], columns=["Key", "Value"])
+    # Format the result for better readability
+    result_text = response.text.strip()  # Remove extra spaces and line breaks
+    formatted_response = f"**AI Generated Content:**\n\n{result_text}"
+    return formatted_response
 
 # Create Gradio interface using Blocks for better control
 with gr.Blocks() as interface:
     # Title and Description
-    gr.Markdown("# Gemini AI Content Generator & Code Executor")
-    gr.Markdown("Provide a prompt to generate content or code. The code will be executed if it's valid Python.")
+    gr.Markdown("# Gemini AI Content Generator")
+    gr.Markdown("Provide a prompt and get an explanation from Gemini AI.\n\nThe AI will return a well-formatted response.")
     
-    # Text input field for prompt and submit button
-    prompt_input = gr.Textbox(label="Enter your prompt (e.g., Python code):", placeholder="print('Hello, World!')", lines=3)
-    submit_button = gr.Button("Generate and Execute Code")
+    # Text input field and submit button
+    prompt_input = gr.Textbox(label="Enter your prompt:", placeholder="How does AI work?", lines=2)
+    submit_button = gr.Button("Generate Content")
     
-    # Output areas for the results
-    code_section = gr.Markdown(label="Generated Code or Content")
-    execution_section = gr.DataFrame(headers=["Key", "Value"], datatype=["str", "str"], interactive=False)
+    # Output area for the result
+    output_area = gr.Markdown()
     
-    # Loading spinner
+    # Loading spinner (show loading during API call)
     loading_spinner = gr.HTML("<p style='color: #4CAF50;'>Loading... Please wait...</p>")
     loading_spinner.visible = False  # Initially hidden
     
     # Define button click interaction
     def on_submit(prompt):
-        loading_spinner.visible = True  # Show loading message
-        result = generate_content(prompt)  # Get content or code from Gemini
-        
-        code_section.value = f"### Generated Code/Content:\n\n{result}"  # Display the code/content
-        
-        if "```python" in result:  # If code is returned, extract Python code and execute it
-            code = result.split("```python")[1].split("```")[0].strip()
-            execution_result = execute_code(code)
-            result = f"**Code Executed Result:**\n\n{execution_result}"
-        else:
-            result = f"**Generated Content:**\n\n{result}"
-
-        # Convert the result into a table
-        table_data = format_result_as_table(result)
-        loading_spinner.visible = False  # Hide loading message
-        return code_section, table_data, loading_spinner  # Return the sections with content and execution result
+        loading_spinner.visible = True
+        result = generate_content(prompt)
+        loading_spinner.visible = False
+        return result, loading_spinner
 
     # Link the button with the function
-    submit_button.click(on_submit, inputs=prompt_input, outputs=[code_section, execution_section, loading_spinner])
+    submit_button.click(on_submit, inputs=prompt_input, outputs=[output_area, loading_spinner])
 
 # Launch Gradio interface
-interface.launch()
+interface.launch()