app.py

import os
import re
import json
import gradio as gr
import requests
from typing import List, Dict
from googlesearch import search
import google.generativeai as genai
from google.generativeai.types import HarmCategory, HarmBlockThreshold

def initialize_gemini(api_key: str):
    """Initialize the Google Gemini API with appropriate configurations"""
    genai.configure(api_key=api_key)
    generation_config = {
        "temperature": 0.2,
        "top_p": 0.8,
        "top_k": 40,
        "max_output_tokens": 1024,
    }
    safety_settings = {
        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
    }
    
    model = genai.GenerativeModel(
        model_name="gemini-1.5-flash",
        generation_config=generation_config,
        safety_settings=safety_settings
    )
    return model

def google_search_naics(company_name: str) -> List[str]:
    """Find potential NAICS codes for a company using Google search"""
    query = f"NAICS code 2022 for {company_name}"
    naics_codes = set()
    
    try:
        search_results = search(query, stop=5, pause=2)
        
        for result_url in search_results:
            try:
                response = requests.get(result_url, timeout=5)
                if response.status_code == 200:
                    # Extract 6-digit NAICS codes
                    found_codes = re.findall(r'\b\d{6}\b', response.text)
                    naics_codes.update(found_codes)
            except Exception as e:
                print(f"Error fetching {result_url}: {e}")
        
        return list(naics_codes)[:5]  # Return up to 5 extracted NAICS codes
    except Exception as e:
        print(f"Error performing Google search: {str(e)}")
        return []

def get_naics_classification(model, company_name: str, context: str, candidates: List[str]) -> dict:
    """
    Use Gemini AI to determine the most appropriate NAICS code from candidates
    First provides reasoning, then multiple possibilities with confidence levels
    """
    try:
        # If we have candidate codes from Google search
        if candidates:
            prompt = f"""
You are a NAICS code classification expert. Based on the company information provided and the NAICS code candidates found from Google search, determine the most appropriate NAICS code.

Company Name: {company_name}
Context Information: {context}

NAICS Code Candidates from Google Search: {candidates}

First, explain your reasoning for which industry this company belongs to.
Then list 3 potential NAICS classifications with confidence percentages (must add up to 100%).
Finally, provide your final conclusion.

Your response should be in this format:
REASONING: [Your detailed reasoning about the company's industry classification]

POSSIBILITY_1: [Industry name] - NAICS Code [6-digit code] - [XX]% confidence
POSSIBILITY_2: [Industry name] - NAICS Code [6-digit code] - [XX]% confidence  
POSSIBILITY_3: [Industry name] - NAICS Code [6-digit code] - [XX]% confidence

CONCLUSION: I am [XX]% confident this company is [industry description] which is NAICS code [6-digit code]
"""
        # If no candidates were found from Google search
        else:
            prompt = f"""
You are a NAICS code classification expert. Based on the company information provided, determine the most appropriate NAICS code.

Company Name: {company_name}
Context Information: {context}

First, explain your reasoning for which industry this company belongs to.
Then list 3 potential NAICS classifications with confidence percentages (must add up to 100%).
Finally, provide your final conclusion.

Your response should be in this format:
REASONING: [Your detailed reasoning about the company's industry classification]

POSSIBILITY_1: [Industry name] - NAICS Code [6-digit code] - [XX]% confidence
POSSIBILITY_2: [Industry name] - NAICS Code [6-digit code] - [XX]% confidence  
POSSIBILITY_3: [Industry name] - NAICS Code [6-digit code] - [XX]% confidence

CONCLUSION: I am [XX]% confident this company is [industry description] which is NAICS code [6-digit code]
"""
        response = model.generate_content(prompt)
        response_text = response.text.strip()
        
        # Extract reasoning
        reasoning_match = re.search(r'REASONING:(.*?)POSSIBILITY_1:', response_text, re.DOTALL | re.IGNORECASE)
        reasoning = reasoning_match.group(1).strip() if reasoning_match else "No reasoning provided."
        
        # Extract possibilities
        possibilities = []
        
        # Try to extract possibility 1
        poss1_match = re.search(r'POSSIBILITY_1:(.*?)POSSIBILITY_2:', response_text, re.DOTALL | re.IGNORECASE)
        if poss1_match:
            possibilities.append(poss1_match.group(1).strip())
            
        # Try to extract possibility 2  
        poss2_match = re.search(r'POSSIBILITY_2:(.*?)POSSIBILITY_3:', response_text, re.DOTALL | re.IGNORECASE)
        if poss2_match:
            possibilities.append(poss2_match.group(1).strip())
            
        # Try to extract possibility 3
        poss3_match = re.search(r'POSSIBILITY_3:(.*?)CONCLUSION:', response_text, re.DOTALL | re.IGNORECASE)
        if poss3_match:
            possibilities.append(poss3_match.group(1).strip())
        
        # Extract conclusion
        conclusion_match = re.search(r'CONCLUSION:(.*?)
    except Exception as e:
        print(f"Error getting NAICS classification: {str(e)}")
        return {
            "naics_code": "000000",
            "reasoning": f"Error analyzing company: {str(e)}"
        }

def find_naics_code(api_key, company_name, company_description):
    """Main function to find NAICS code that will be called by Gradio"""
    if not api_key or not company_name:
        return "Please provide both API key and company name."
    
    try:
        # Initialize Gemini API
        model = initialize_gemini(api_key)
        
        # Search for NAICS candidates
        naics_candidates = google_search_naics(company_name)
        
        # Get classification
        if not naics_candidates:
            result = get_naics_classification(model, company_name, company_description, [])
        else:
            result = get_naics_classification(model, company_name, company_description, naics_candidates)
        
        # Format the output
        output = f"## NAICS Code for {company_name}\n\n"
        output += f"**NAICS Code:** {result['naics_code']}\n\n"
        output += f"**Reasoning:**\n{result['reasoning']}\n\n"
        
        # Add possibilities section
        if 'possibilities' in result and result['possibilities']:
            output += f"**Possible Classifications:**\n\n"
            for i, possibility in enumerate(result['possibilities'], 1):
                output += f"{i}. {possibility}\n\n"
        
        # Add conclusion
        if 'conclusion' in result and result['conclusion']:
            output += f"**Conclusion:**\n{result['conclusion']}\n\n"
        
        if naics_candidates:
            output += f"**Candidate NAICS Codes Found from Google:**\n{', '.join(naics_candidates)}"
        
        return output
    
    except Exception as e:
        return f"Error: {str(e)}"

# Create Gradio Interface
with gr.Blocks(title="NAICS Code Finder") as app:
    gr.Markdown("# NAICS Code Finder")
    gr.Markdown("This app helps you find the appropriate NAICS code for a company based on its name and description.")
    
    with gr.Row():
        with gr.Column():
            api_key = gr.Textbox(label="Google Gemini API Key", placeholder="Enter your Gemini API key here", type="password")
            company_name = gr.Textbox(label="Company Name", placeholder="Enter the company name")
            company_description = gr.Textbox(label="Company Description", placeholder="Enter a brief description of the company", lines=5)
            
            submit_btn = gr.Button("Find NAICS Code")
        
        with gr.Column():
            output = gr.Markdown(label="Result")
    
    submit_btn.click(
        fn=find_naics_code,
        inputs=[api_key, company_name, company_description],
        outputs=output
    )

if __name__ == "__main__":
    app.launch()
, response_text, re.DOTALL | re.IGNORECASE)
        conclusion = conclusion_match.group(1).strip() if conclusion_match else "No conclusion provided."
        
        # Extract final NAICS code from conclusion
        naics_match = re.search(r'NAICS code (\d{6})', conclusion)
        if naics_match:
            naics_code = naics_match.group(1)
        else:
            # Try to find any 6-digit code in the conclusion
            code_match = re.search(r'\b(\d{6})\b', conclusion)
            naics_code = code_match.group(1) if code_match else "000000"
        
        return {
            "naics_code": naics_code,
            "reasoning": reasoning,
            "possibilities": possibilities,
            "conclusion": conclusion
        }
    except Exception as e:
        print(f"Error getting NAICS classification: {str(e)}")
        return {
            "naics_code": "000000",
            "reasoning": f"Error analyzing company: {str(e)}"
        }

def find_naics_code(api_key, company_name, company_description):
    """Main function to find NAICS code that will be called by Gradio"""
    if not api_key or not company_name:
        return "Please provide both API key and company name."
    
    try:
        # Initialize Gemini API
        model = initialize_gemini(api_key)
        
        # Search for NAICS candidates
        naics_candidates = google_search_naics(company_name)
        
        # Get classification
        if not naics_candidates:
            result = get_naics_classification(model, company_name, company_description, [])
        else:
            result = get_naics_classification(model, company_name, company_description, naics_candidates)
        
        # Format the output
        output = f"## NAICS Code for {company_name}\n\n"
        output += f"**NAICS Code:** {result['naics_code']}\n\n"
        output += f"**Reasoning:**\n{result['reasoning']}\n\n"
        
        if naics_candidates:
            output += f"**Candidate NAICS Codes Found:**\n{', '.join(naics_candidates)}"
        
        return output
    
    except Exception as e:
        return f"Error: {str(e)}"

# Create Gradio Interface
with gr.Blocks(title="NAICS Code Finder") as app:
    gr.Markdown("# NAICS Code Finder")
    gr.Markdown("This app helps you find the appropriate NAICS code for a company based on its name and description.")
    
    with gr.Row():
        with gr.Column():
            api_key = gr.Textbox(label="Google Gemini API Key", placeholder="Enter your Gemini API key here", type="password")
            company_name = gr.Textbox(label="Company Name", placeholder="Enter the company name")
            company_description = gr.Textbox(label="Company Description", placeholder="Enter a brief description of the company", lines=5)
            
            submit_btn = gr.Button("Find NAICS Code")
        
        with gr.Column():
            output = gr.Markdown(label="Result")
    
    submit_btn.click(
        fn=find_naics_code,
        inputs=[api_key, company_name, company_description],
        outputs=output
    )

if __name__ == "__main__":
    app.launch()