import gradio as gr
import time
from smolagents import CodeAgent, HfApiModel

# Initialize the AI agent
agent = CodeAgent(
    tools=[],
    model=HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
)

def analyze_content(file_paths):
    """Process files and generate report with status updates"""
    status = "Starting analysis..."
    yield "", status
    
    full_content = []
    for path in file_paths:
        status = f"Reading {path.split('/')[-1]}..."
        yield "", status
        
        try:
            with open(path, 'r', encoding='utf-8') as f:
                content = f.read()
                full_content.append(f"## {path.split('/')[-1]}\n{content}\n")
        except Exception as e:
            yield f"Error processing {path}: {str(e)}", ""
            return

    status = "Analyzing content with AI..."
    yield "", status
    
    report = agent.run(f"""
    Analyze these documents:
    {"".join(full_content)[:10000]}
    
    Create report with:
    1. Key insights
    2. Important patterns
    3. Actionable recommendations
    
    Use markdown formatting with headers.
    """)
    
    status = "Analysis complete!"
    yield report, status

with gr.Blocks() as demo:
    gr.Markdown("# Document Analysis System")
    
    with gr.Row():
        file_input = gr.File(
            file_count="multiple",
            file_types=[".txt"],
            label="Upload Documents"
        )
        process_btn = gr.Button("Generate Report", variant="primary")
    
    report_output = gr.Markdown(label="Analysis Report")
    status = gr.Textbox(label="Processing Status", interactive=False)

    process_btn.click(
        fn=analyze_content,
        inputs=file_input,
        outputs=[report_output, status],
        show_progress="hidden"
    )

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True
    )