app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util
from transformers import pipeline, GPT2Tokenizer
import os

# Define paths and models
filename = "output_country_details.txt"  # Adjust the filename as needed
retrieval_model_name = 'output/sentence-transformer-finetuned/'
gpt2_model_name = "gpt2"  # GPT-2 model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Load models
try:
    retrieval_model = SentenceTransformer(retrieval_model_name)
    gpt_model = pipeline("text-generation", model=gpt2_model_name)
    print("Models loaded successfully.")
except Exception as e:
    print(f"Failed to load models: {e}")

# Load and preprocess text from the country details file
def load_and_preprocess_text(filename):
    try:
        with open(filename, 'r', encoding='utf-8') as file:
            segments = [line.strip() for line in file if line.strip()]
        print("Text loaded and preprocessed successfully.")
        return segments
    except Exception as e:
        print(f"Failed to load or preprocess text: {e}")
        return []

segments = load_and_preprocess_text(filename)

def find_relevant_segment(user_query, segments):
    try:
        query_embedding = retrieval_model.encode(user_query)
        segment_embeddings = retrieval_model.encode(segments)
        similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]
        best_idx = similarities.argmax()
        print("Relevant segment found:", segments[best_idx])
        return segments[best_idx]
    except Exception as e:
        print(f"Error finding relevant segment: {e}")
        return ""

def generate_response(user_query, relevant_segment):
    try:
        # Construct the prompt with the user query
        prompt = f"Thank you for your question! this is an additional fact about your topic: {relevant_segment}"
        
        # Generate response with adjusted max_length for completeness
        max_tokens = len(tokenizer(prompt)['input_ids']) + 50
        response = gpt_model(prompt, max_length=max_tokens, temperature=0.25)[0]['generated_text']
        
        # Clean and format the response
        response_cleaned = clean_up_response(response, relevant_segment)
        return response_cleaned
    except Exception as e:
        print(f"Error generating response: {e}")
        return ""

def clean_up_response(response, segments):
    # Split the response into sentences
    sentences = response.split('.')
    
    # Remove empty sentences and any repetitive parts
    cleaned_sentences = []
    for sentence in sentences:
        if sentence.strip() and sentence.strip() not in segments and sentence.strip() not in cleaned_sentences:
            cleaned_sentences.append(sentence.strip())
    
    # Join the sentences back together
    cleaned_response = '. '.join(cleaned_sentences).strip()
    
    # Check if the last sentence ends with a complete sentence
    if cleaned_response and not cleaned_response.endswith((".", "!", "?")):
        cleaned_response += "."
    
    return cleaned_response

# Define the welcome message with markdown for formatting and larger fonts
welcome_message = """
# Welcome to VISABOT!

## Your AI-driven visa assistant for all travel-related queries.

"""

# Define topics and countries with flag emojis
topics = """
### Feel Free to ask me anything from the topics below!
- Visa issuance
- Documents needed
- Application process
- Processing time
- Recommended Vaccines
- Health Risks
- Healthcare Facilities
- Currency Information
- Embassy Information 
- Allowed stay
"""

countries = """
### Our chatbot can currently answer questions for these countries!
- 🇨🇳 China
- 🇫🇷 France
- 🇬🇹 Guatemala
- 🇱🇧 Lebanon
- 🇲🇽 Mexico
- 🇵🇭 Philippines
- 🇷🇸 Serbia
- 🇸🇱 Sierra Leone
- 🇿🇦 South Africa
- 🇻🇳 Vietnam
"""

# Define the Gradio app interface
def query_model(question):
    if question == "":  # If there's no input, the bot will display the greeting message.
        return welcome_message
    relevant_segment = find_relevant_segment(question, segments)
    response = generate_response(question, relevant_segment)
    return response

# Create Gradio Blocks interface for custom layout
with gr.Blocks() as demo:
    gr.Markdown(welcome_message)  # Display the welcome message with large fonts
    with gr.Row():
        with gr.Column():
            gr.Markdown(topics)  # Display the topics on the left
        with gr.Column():
            gr.Markdown(countries)  # Display the countries with flag emojis on the right
    with gr.Row():
        img = gr.Image(os.path.join(os.getcwd(), "final.png"), width=500)  # Adjust width as needed
    with gr.Row():
        with gr.Column():
            question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
            answer = gr.Textbox(label="VisaBot Response", placeholder="VisaBot will respond here...", interactive=False, lines=10)
            submit_button = gr.Button("Submit")
            submit_button.click(fn=query_model, inputs=question, outputs=answer)

# Launch the app
demo.launch()