Create app.py

app.py

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+import os
+from typing import Optional
+
+import gradio as gr
+import pandas as pd
+from litellm import completion
+
+from components.analysis import DataAnalyzer
+from components.statistical import StatisticalAnalyzer
+from components.visualization import D3Visualizer
+
+def analyze_data(
+    file: gr.File,
+    query: str,
+    api_key: str,
+    temperature: float = 0.7,
+) -> str:
+    """Process user request and generate analysis"""
+    
+    if not api_key:
+        return "Error: Please provide an API key."
+        
+    if not file:
+        return "Error: Please upload a file."
+        
+    try:
+        # Set up environment
+        os.environ["OPENAI_API_KEY"] = api_key
+        
+        # Load data
+        if file.name.endswith('.csv'):
+            df = pd.read_csv(file.name)
+        elif file.name.endswith(('.xlsx', '.xls')):
+            df = pd.read_excel(file.name)
+        else:
+            return "Error: Unsupported file type."
+        
+        # Initialize analyzers
+        analyzer = DataAnalyzer()
+        
+        # Build context
+        file_info = f"""
+        File: {file.name}
+        Shape: {df.shape}
+        Columns: {', '.join(df.columns)}
+        
+        Column Types:
+        {chr(10).join([f'- {col}: {dtype}' for col, dtype in df.dtypes.items()])}
+        """
+        
+        # Get analysis request from GPT-4
+        messages = [
+            {
+                "role": "system",
+                "content": """You are a data analysis assistant. 
+                Interpret the user's query and determine which analysis to perform.
+                Return a JSON with:
+                - analysis_type: distribution, forecast, or correlation
+                - params: dictionary of parameters needed
+                - explanation: why this analysis is appropriate"""
+            },
+            {
+                "role": "user",
+                "content": f"{file_info}\n\nUser request: {query}"
+            }
+        ]
+        
+        response = completion(
+            model="gpt-4o-mini",
+            messages=messages,
+            temperature=temperature
+        )
+        
+        # Parse response and perform analysis
+        analysis_request = eval(response.choices[0].message.content)
+        result = analyzer.analyze_data(
+            df,
+            analysis_request["analysis_type"],
+            analysis_request["params"]
+        )
+        
+        # Combine results into HTML
+        html_output = f"""
+        <div class="analysis-container">
+            <div class="explanation">
+                <h2>Analysis Explanation</h2>
+                <p>{analysis_request['explanation']}</p>
+            </div>
+            
+            <div class="results">
+                <h2>Statistical Results</h2>
+                <pre>{str(result.get('statistics', ''))}</pre>
+            </div>
+            
+            <div class="visualization">
+                <h2>Interactive Visualization</h2>
+                {result['visualization']}
+            </div>
+        </div>
+        """
+        
+        return html_output
+        
+    except Exception as e:
+        return f"Error occurred: {str(e)}"
+
+def create_interface():
+    """Create Gradio interface"""
+    
+    with gr.Blocks(title="Interactive Data Analysis") as interface:
+        gr.Markdown("""
+        # Interactive Data Analysis Assistant
+        
+        Upload your data and get interactive visualizations with statistical analysis.
+        
+        **Features:**
+        - Interactive D3 visualizations
+        - Statistical analysis
+        - Probability distributions
+        - Time series forecasting
+        - Correlation analysis
+        
+        **Note**: Requires your own OpenAI API key.
+        """)
+        
+        with gr.Row():
+            with gr.Column():
+                file = gr.File(
+                    label="Upload Data File",
+                    file_types=[".csv", ".xlsx", ".xls"]
+                )
+                query = gr.Textbox(
+                    label="What would you like to analyze?",
+                    placeholder="e.g., Show distribution of values with statistics",
+                    lines=3
+                )
+                api_key = gr.Textbox(
+                    label="API Key (Required)",
+                    placeholder="Your OpenAI API key",
+                    type="password"
+                )
+                temperature = gr.Slider(
+                    label="Temperature",
+                    minimum=0.0,
+                    maximum=1.0,
+                    value=0.7,
+                    step=0.1
+                )
+                analyze_btn = gr.Button("Analyze")
+            
+            with gr.Column():
+                output = gr.HTML(label="Output")
+        
+        analyze_btn.click(
+            analyze_data,
+            inputs=[file, query, api_key, temperature],
+            outputs=output
+        )
+        
+        gr.Examples(
+            examples=[
+                [None, "Show me the distribution of values and calculate statistics"],
+                [None, "Create a 10-period probability cone forecast"],
+                [None, "Analyze correlations between variables"],
+                [None, "Test if the data follows a normal distribution"],
+                [None, "Show the data distribution with confidence intervals"],
+            ],
+            inputs=[file, query]
+        )
+    
+    return interface
+
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch()