import gradio as gr
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, progress=gr.Progress()):
    """Process files and generate comprehensive report with progress tracking"""
    progress(0, desc="Starting analysis...")
    full_content = []
    
    # Read files with progress
    for i, path in enumerate(file_paths):
        progress(i/len(file_paths), f"Reading {path.split('/')[-1]}...")
        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:
            return f"Error processing {path}: {str(e)}"

    # Generate report with progress
    progress(0.8, "Analyzing content with AI...")
    report = agent.run(f"""
    Analyze these documents and create a detailed report:
    
    {"".join(full_content)[:10000]}
    
    Report structure:
    1. Executive Summary
    2. Key Findings
    3. Important Patterns
    4. Recommendations
    
    Use professional markdown formatting with headings and bullet points.
    """)
    
    progress(1.0, "Analysis complete!")
    return report

with gr.Blocks() as demo:
    gr.Markdown("# Professional Document Analyzer")
    
    with gr.Row():
        with gr.Column(scale=1):
            file_input = gr.File(
                file_count="multiple",
                file_types=[".txt"],
                label="Upload Documents"
            )
            process_btn = gr.Button("Generate Report", variant="primary")
            status = gr.Textbox(label="Processing Status", interactive=False)
        
        with gr.Column(scale=2, variant="panel"):
            gr.Markdown("## Analysis Report")
            report_output = gr.Markdown(
                elem_classes="report-box",
                label="",
                show_label=False
            )

    @process_btn.click(
        inputs=file_input,
        outputs=[status, report_output]
    )
    def process_files(files):
        if not files:
            return "No files uploaded", ""
        
        file_paths = [f.name for f in files]
        
        def update_progress(progress):
            return lambda p: progress(p)
        
        return (
            gr.Progress().track() |  # Progress indicator
            analyze_content(file_paths)
        )

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