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app.py

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+"""
+International Trade Flow Predictor - Full Application for Hugging Face Spaces
+"""
+from flask import Flask, render_template, request, jsonify
+import os
+import json
+import requests
+import pandas as pd
+import numpy as np
+import time
+from dotenv import load_dotenv
+import sys
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load environment variables from .env file if it exists
+load_dotenv()
+
+# Log environment for debugging
+logger.info(f"Environment variables: HUGGINGFACE_API_TOKEN exists: {'HUGGINGFACE_API_TOKEN' in os.environ}")
+logger.info(f"Python version: {sys.version}")
+logger.info(f"Working directory: {os.getcwd()}")
+logger.info(f"Directory contents: {os.listdir('.')}")
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Import the llm_assistant module
+try:
+    from llm_assistant import TradeAssistant
+    logger.info("Successfully imported TradeAssistant")
+except Exception as e:
+    logger.error(f"Error importing TradeAssistant: {str(e)}")
+    
+    # Create a fallback class if import fails
+    class TradeAssistant:
+        def __init__(self, api_token=None):
+            self.api_token = api_token
+            
+        def query(self, user_question, chat_history=None, include_app_context=True):
+            return {
+                "success": False,
+                "response": "The AI assistant is temporarily unavailable. Please check the application logs for details.",
+                "message": "Import error"
+            }
+            
+        def format_chat_history(self, chat_history_raw):
+            return []
+            
+        def enhance_query_with_context(self, query):
+            return query
+            
+        def explain_hs_code(self, code):
+            return {
+                "success": False,
+                "response": "HS code explanation is temporarily unavailable.",
+                "message": "Import error"
+            }
+            
+        def get_trade_recommendation(self, country=None, product=None, year=None):
+            return {
+                "success": False,
+                "response": "Trade recommendations are temporarily unavailable.",
+                "message": "Import error"
+            }
+
+# Initialize the AI Assistant
+trade_assistant = TradeAssistant(api_token=os.environ.get("HUGGINGFACE_API_TOKEN"))
+
+# Import the primary app functionality
+# This avoids having to duplicate all the code
+from app import (get_countries, get_product_codes, query_comtrade, 
+                clean_comtrade_data, predict_trade, export_data, 
+                get_ml_models, train_ml_model, get_cached_data, 
+                get_trade_rankings, get_top_trade_partners)
+
+# Home page
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+# AI Assistant endpoints
+@app.route('/api/assistant/query', methods=['POST'])
+def assistant_query():
+    data = request.json
+    
+    # Get the user question from request
+    user_question = data.get('question', '')
+    
+    # Validate input
+    if not user_question:
+        return jsonify({
+            'success': False,
+            'response': '',
+            'message': 'No question provided'
+        })
+        
+    # Get chat history if provided
+    chat_history_raw = data.get('chatHistory', [])
+    
+    # Format chat history for the LLM
+    chat_history = trade_assistant.format_chat_history(chat_history_raw)
+    
+    # Enhance query with additional context if applicable
+    enhanced_question = trade_assistant.enhance_query_with_context(user_question)
+    
+    # Send query to the LLM
+    response = trade_assistant.query(enhanced_question, chat_history)
+    
+    # Return the response
+    return jsonify(response)
+
+# API endpoint for HS code explanation
+@app.route('/api/assistant/explain-hs-code', methods=['POST'])
+def explain_hs_code():
+    data = request.json
+    
+    # Get the HS code from request
+    hs_code = data.get('code', '')
+    
+    # Validate input
+    if not hs_code:
+        return jsonify({
+            'success': False,
+            'response': '',
+            'message': 'No HS code provided'
+        })
+        
+    # Send to specific HS code explanation function
+    response = trade_assistant.explain_hs_code(hs_code)
+    
+    # Return the response
+    return jsonify(response)
+
+# API endpoint for trade recommendations
+@app.route('/api/assistant/get-recommendation', methods=['POST'])
+def get_recommendation():
+    data = request.json
+    
+    # Get parameters
+    country = data.get('country', None)
+    product = data.get('product', None)
+    year = data.get('year', None)
+    
+    # At least one parameter should be provided
+    if not country and not product and not year:
+        return jsonify({
+            'success': False,
+            'response': '',
+            'message': 'Please provide at least one parameter (country, product, or year)'
+        })
+        
+    # Get recommendation
+    response = trade_assistant.get_trade_recommendation(country, product, year)
+    
+    # Return the response
+    return jsonify(response)
+
+if __name__ == "__main__":
+    # Hugging Face Spaces uses port 7860 by default
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host="0.0.0.0", port=port)