Main

main.py

from flask import Flask, render_template, request, redirect, url_for
from langchain import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
import spacy

app = Flask(__name__)

# Load spaCy's English model for entity recognition
nlp = spacy.load("en_core_web_sm")

# GPT-4 setup
llm = OpenAI(temperature=0.7, model="gpt-4")

# Chat prompt template
chat_prompt = PromptTemplate(
    input_variables=["text"],
    template="You are a helpful assistant. {text}"
)

# Function to extract entities and generate highlighted text
def extract_entities_and_highlight(text):
    doc = nlp(text)
    highlighted_text = text

    for ent in doc.ents:
        if ent.label_ in ["PERSON", "ORG", "GPE", "LOC", "MEDICAL", "DRUG", "DISEASE"]:
            highlighted_text = highlighted_text.replace(ent.text, f'<span class="entity">{ent.text}</span>')

    return highlighted_text

# Route for rendering the chat page
@app.route('/', methods=['GET', 'POST'])
def chat():
    conversation = []

    if request.method == 'POST':
        user_input = request.form['message']
        
        # Generate a response from GPT-4
        chain = LLMChain(llm=llm, prompt=chat_prompt)
        response = chain.run({"text": user_input})

        # Extract and highlight entities in the response
        highlighted_response = extract_entities_and_highlight(response)

        # Append both user and bot messages to the conversation
        conversation.append({'sender': 'User', 'message': user_input})
        conversation.append({'sender': 'Bot', 'message': highlighted_response})

    return render_template('chat.html', conversation=conversation)

if __name__ == '__main__':
    app.run(debug=True)