Create app.py

app.py

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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.