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app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+from transformers import pipeline, GPT2Tokenizer
+import os
+
+# Define paths and model identifiers for easy reference and maintenance
+filename = "output_country_details.txt"  # Filename for stored country details
+retrieval_model_name = 'output/sentence-transformer-finetuned/'
+gpt2_model_name = "gpt2"  # Identifier for the GPT-2 model used
+tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
+
+# Load models and handle potential failures gracefully
+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}")
+
+def load_and_preprocess_text(filename):
+    """
+    Load text data from a file and preprocess it by stripping whitespace and ignoring empty lines.
+    
+    Args:
+    filename (str): Path to the file containing text data.
+    
+    Returns:
+    list of str: Preprocessed lines of text from the file.
+    """
+    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):
+    """
+    Identify the most relevant text segment from a list based on a user's query using sentence embeddings.
+    
+    Args:
+    user_query (str): User's input query.
+    segments (list of str): List of text segments to search from.
+    
+    Returns:
+    str: The text segment that best matches the query.
+    """
+    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):
+    """
+    Generate a response to a user's query using a text generation model based on a relevant text segment.
+    
+    Args:
+    user_query (str): The user's query.
+    relevant_segment (str): The segment of text that is relevant to the query.
+    
+    Returns:
+    str: A response generated from the model.
+    """
+    try:
+        prompt = f"Thank you for your question! This is an additional fact about your topic: {relevant_segment}"
+        max_tokens = len(tokenizer(prompt)['input_ids']) + 50
+        response = gpt_model(prompt, max_length=max_tokens, temperature=0.25)[0]['generated_text']
+        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):
+    """
+    Clean and format the generated response by removing empty sentences and repetitive parts.
+    
+    Args:
+    response (str): The raw response generated by the model.
+    segments (str): The text segment used to generate the response.
+    
+    Returns:
+    str: Cleaned and formatted response.
+    """
+    sentences = response.split('.')
+    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())
+    cleaned_response = '. '.join(cleaned_sentences).strip()
+    if cleaned_response and not cleaned_response.endswith((".", "!", "?")):
+        cleaned_response += "."
+    return cleaned_response
+
+# Gradio interface and application logic
+def query_model(question):
+    """
+    Process a question through the model and return the response.
+    
+    Args:
+    question (str): The question submitted by the user.
+    
+    Returns:
+    str: Generated response or welcome message if no question is provided.
+    """
+    if question == "":
+        return welcome_message
+    relevant_segment = find_relevant_segment(question, segments)
+    response = generate_response(question, relevant_segment)
+    return response
+
+with gr.Blocks() as demo:
+    gr.Markdown(welcome_message)
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(topics)
+        with gr.Column():
+            gr.Markdown(countries)
+    with gr.Row():
+        img = gr.Image(os.path.join(os.getcwd(), "final.png"), width=500)
+    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)
+
+demo.launch()

requirements.txt

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+gradio==2.2.15
+sentence-transformers==2.1.0
+transformers==4.15.0
+tokenizers>=0.10.1,<0.11
+datasets==1.14.0
+pandas==1.3.3
+tokenizers==0.10.0
+torch==1.10.0