Create app.py

app.py

@@ -0,0 +1,187 @@
+import os
+from typing import List, Optional, Union
+
+import gradio as gr
+import pandas as pd
+from dotenv import load_dotenv
+from pandas import DataFrame
+from smolagents import CodeAgent, LiteLLMModel, tool
+
+# Load environment variables
+load_dotenv()
+
+def create_agent():
+    """Create a CodeAgent instance with GPT-4 backend."""
+    model = LiteLLMModel(model_id="gpt-4o-mini")
+    
+    @tool
+    def read_csv(filepath: str) -> DataFrame:
+        """
+        Read a CSV file and return a pandas DataFrame.
+        
+        Args:
+            filepath: Path to the CSV file
+        """
+        return pd.read_csv(filepath)
+    
+    @tool
+    def read_excel(filepath: str) -> DataFrame:
+        """
+        Read an Excel file and return a pandas DataFrame.
+        
+        Args:
+            filepath: Path to the Excel file
+        """
+        return pd.read_excel(filepath)
+
+    agent = CodeAgent(
+        tools=[read_csv, read_excel],
+        model=model,
+        additional_authorized_imports=[
+            "pandas",
+            "numpy",
+            "matplotlib",
+            "seaborn",
+            "plotly",
+            "sklearn",
+            "scipy",
+        ],
+        max_steps=5,
+        verbosity_level=1
+    )
+    return agent
+
+def process_request(
+    files: Union[str, List[str]],
+    user_query: str,
+    api_key: str = "",
+    temperature: float = 0.7,
+    history: Optional[List[tuple]] = None
+) -> tuple:
+    """
+    Process user request with uploaded files and query.
+    
+    Args:
+        files: Path or list of paths to uploaded files
+        user_query: Natural language query from user
+        api_key: Optional API key for GPT-4
+        temperature: Model temperature
+        history: Chat history
+        
+    Returns:
+        Tuple of (output, error, new_history)
+    """
+    if api_key:
+        os.environ["OPENAI_API_KEY"] = api_key
+        
+    try:
+        # Create agent instance
+        agent = create_agent()
+        
+        # Build context from files
+        file_context = ""
+        if isinstance(files, str):
+            files = [files]
+            
+        for file in files:
+            filename = os.path.basename(file)
+            file_context += f"File uploaded: {filename}\n"
+            
+        # Build complete prompt
+        prompt = f"""
+        {file_context}
+        
+        User request: {user_query}
+        
+        Please analyze the data and provide:
+        1. Code to perform the analysis
+        2. Explanation of approach
+        3. Visualizations if relevant
+        4. Key insights and findings
+        """
+        
+        # Execute agent
+        result = agent.run(prompt)
+        
+        # Update history
+        new_history = history or []
+        new_history.append((user_query, result))
+        
+        return result, None, new_history
+        
+    except Exception as e:
+        return None, str(e), history
+
+# Create Gradio interface
+def create_interface():
+    """Create Gradio interface for the AI coding assistant."""
+    
+    with gr.Blocks(title="AI Coding Assistant") as interface:
+        gr.Markdown("""
+        # AI Coding Assistant
+        Upload data files and ask questions in natural language to get code, analysis and visualizations.
+        """)
+        
+        with gr.Row():
+            with gr.Column():
+                files = gr.File(
+                    label="Upload Data Files",
+                    file_types=[".csv", ".xlsx", ".xls"],
+                    multiple=True
+                )
+                query = gr.Textbox(
+                    label="What would you like to analyze?",
+                    placeholder="e.g., Create a scatter plot comparing column A vs B"
+                )
+                api_key = gr.Textbox(
+                    label="API Key (Optional)",
+                    placeholder="Your OpenAI API key",
+                    type="password"
+                )
+                temperature = gr.Slider(
+                    label="Temperature",
+                    minimum=0.0,
+                    maximum=1.0,
+                    value=0.7,
+                    step=0.1
+                )
+                submit = gr.Button("Analyze")
+                
+            with gr.Column():
+                output = gr.Markdown(label="Output")
+                error = gr.Markdown(label="Errors")
+        
+        # Hidden state for chat history
+        history = gr.State([])
+        
+        # Handle submissions
+        submit.click(
+            process_request,
+            inputs=[files, query, api_key, temperature, history],
+            outputs=[output, error, history]
+        )
+        
+        # Add examples
+        gr.Examples(
+            examples=[
+                [
+                    None,
+                    "Create a scatter plot showing the relationship between column A and B, with a trend line",
+                ],
+                [
+                    None,
+                    "Calculate summary statistics and identify any outliers in the numerical columns",
+                ],
+                [
+                    None,
+                    "Perform clustering analysis on the data and visualize the clusters",
+                ],
+            ],
+            inputs=[files, query],
+        )
+        
+    return interface
+
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch()