Upload app.py

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 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()