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| import os | |
| import shutil | |
| import tkinter as tk # Not used in Gradio app, but kept for context if user reverts | |
| from tkinter import filedialog, messagebox # Not used in Gradio app | |
| import requests # Still imported, but not used for model inference | |
| import time | |
| # --- Hugging Face Transformers Imports --- | |
| # You will need to install this library: pip install transformers torch gradio | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
| # --- Gradio Imports --- | |
| import gradio as gr | |
| # CHANGED: Switched to yeniguno/bert-uncased-intent-classification for intent-based classification | |
| print("Loading local intent classification model (yeniguno/bert-uncased-intent-classification)...") | |
| tokenizer_intent = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification") | |
| model_intent = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification") | |
| # Use a text-classification pipeline as the model is for text classification | |
| classification_pipeline = pipeline("text-classification", model=model_intent, tokenizer=tokenizer_intent) | |
| print("Intent classification model loaded successfully.") | |
| def classify_message_with_ai(message_content): | |
| """ | |
| Classifies a message as 'buyer' or 'seller' using the loaded local intent classification model. | |
| Args: | |
| message_content (str): The text content of the message to classify. | |
| Returns: | |
| str: 'buyer' or 'seller' based on intent mapping, or 'unclassified'. | |
| """ | |
| if not message_content: | |
| return "Please provide some text to classify." | |
| # Use the loaded intent classification pipeline | |
| results = classification_pipeline(message_content) | |
| # The result is a list of dictionaries, e.g., [{'label': 'product_inquiry', 'score': 0.99}] | |
| intent = results[0]['label'].lower() | |
| score = results[0]['score'] | |
| # --- Intent to Buyer/Seller Mapping Logic (CRITICAL: REVIEW AND ADJUST THIS) --- | |
| # This is a **placeholder** mapping. You MUST define how intents map to buyer/seller roles | |
| # based on the characteristics of your actual messages and the specific labels | |
| # output by the 'yeniguno/bert-uncased-intent-classification' model. | |
| # Example common intents and their likely roles (as defined in previous version): | |
| buyer_intents = [ | |
| 'information_seeking', 'product_inquiry', 'complaint', 'order_status', | |
| 'price_inquiry', 'availability_check', 'making_offer', 'general_query' # Added general_query as common buyer intent | |
| ] | |
| seller_intents = [ | |
| 'product_offering', 'promotion', 'transaction_confirmation', | |
| 'providing_details', 'listing_prices', 'delivery_update', 'greeting' # Added greeting as common seller intent | |
| ] | |
| classification_result = "unclassified" | |
| if intent in buyer_intents: | |
| classification_result = 'buyer' | |
| elif intent in seller_intents: | |
| classification_result = 'seller' | |
| # Return a more descriptive string for the Gradio interface | |
| return f"Detected Intent: {intent.replace('_', ' ').title()} (Score: {score:.2f})\nClassification: {classification_result.upper()}" | |
| # --- Gradio Interface --- | |
| # Define the Gradio interface components | |
| input_text = gr.Textbox(lines=5, label="Enter message text here:") | |
| output_text = gr.Textbox(label="Classification Result") | |
| # Create the Gradio interface | |
| gr.Interface( | |
| fn=classify_message_with_ai, | |
| inputs=input_text, | |
| outputs=output_text, | |
| title="Buyer/Seller Message Classifier", | |
| description="Enter a message to classify it as 'Buyer' or 'Seller' based on detected intent. Remember to adjust the mapping logic in the code for best results!" | |
| ).launch() | |
| # The `organize_messages_by_type` function is removed as it's for local file system operations. | |
| # You would not typically run a Gradio app directly from __main__ like the old script. | |
| # When deployed to Hugging Face Spaces, the `app.py` or `run.py` file would simply contain | |
| # the Gradio interface definition and its `.launch()` call. | |