app.py

import gradio as gr
import json
from transformers import BertTokenizer, BertForSequenceClassification
import torch
from quotes_spider import run_spider

# Load BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

def generate_response(message):
    # Customize the greeting response
    if message.lower() == "hi":
        return "Hi. How can I help you?"
    
    # Define other conversational responses
    greetings = ["hello", "hey", "how are you", "what's up"]
    if any(greet in message.lower() for greet in greetings):
        return "Hello! How can I assist you today?"

    # For other messages, use BERT for classification or response generation
    inputs = tokenizer(message, return_tensors="pt")
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    confidence, predicted_class = torch.max(probs, dim=-1)

    # Example classification (you can adapt this to your specific needs)
    if predicted_class == 0:  # Adjust according to your classification setup
        return "It seems like you're asking about something specific. Let me find that for you."
    else:
        return "I'm not sure how to help with that right now. Can you provide more details?"

def fetch_quotes():
    # Run Scrapy spider and get quotes
    quotes = run_spider()  # This should return a JSON string or dict
    return json.loads(quotes) if quotes else []

def chatbot_response(message, url):
    response = ""
    if url:
        quotes = fetch_quotes()
        if quotes:
            response += f"I found a quote: \"{quotes[0]['text']}\" by {quotes[0]['author']}."
    
    if message:
        response += f" {generate_response(message)}"
    
    return response.strip()

# Define the Gradio interface
iface = gr.Interface(
    fn=chatbot_response,
    inputs=[
        gr.Textbox(lines=1, placeholder="Enter your message here...", label="Message"),
        gr.Textbox(lines=1, placeholder="Enter URL here...", label="URL")
    ],
    outputs="text",
    title="Conversational Scrapy-BERT Chatbot"
)

iface.launch()