Create app.py

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+import io
+import google.generativeai as genai
+
+# Function to process the file and generate visualizations
+def process_file(api_key, file, instructions):
+    # Set up Gemini API
+    genai.configure(api_key=api_key)
+    model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
+
+    # Read the file
+    if file.name.endswith('.csv'):
+        df = pd.read_csv(file.name)
+    else:
+        df = pd.read_excel(file.name)
+
+    # Analyze data and get visualization suggestions from Gemini
+    data_description = df.describe().to_string()
+    prompt = f"Given this data: {data_description}\n"
+    if instructions:
+        prompt += f"And these instructions: {instructions}\n"
+    prompt += "Suggest 3 ways to visualize this data."
+
+    response = model.generate_content(prompt)
+    suggestions = response.text.split('\n')
+
+    # Generate visualizations (placeholder - you'll need to implement actual visualization logic)
+    visualizations = []
+    for i, suggestion in enumerate(suggestions[:3]):
+        plt.figure()
+        plt.title(f"Visualization {i+1}")
+        plt.text(0.5, 0.5, suggestion, ha='center', va='center')
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png')
+        buf.seek(0)
+        visualizations.append(buf)
+
+    return visualizations
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("Data Visualization with Gemini")
+    api_key = gr.Textbox(label="Enter Gemini API Key")
+    file = gr.File(label="Upload Excel or CSV file")
+    instructions = gr.Textbox(label="Optional visualization instructions")
+    submit = gr.Button("Generate Visualizations")
+    progress = gr.Progress()
+    outputs = [gr.Image(label=f"Visualization {i+1}") for i in range(3)]
+    
+    submit.click(
+        fn=process_file,
+        inputs=[api_key, file, instructions],
+        outputs=outputs
+    )
+
+demo.launch()