app.py

# -*- coding: utf-8 -*-
"""saivv_protoype"""

# Import necessary libraries
import cv2  # For image processing with OpenCV
import pytesseract  # For Optical Character Recognition (OCR) on receipts
import gradio as gr  # For creating the Gradio interface
import speech_recognition as sr  # For voice recognition

# Model setup (using transformers)
import torch
import transformers  # Added import for transformers
from transformers import AutoTokenizer
from langchain_community.llms import HuggingFacePipeline  # Updated import for HuggingFacePipeline

# Initialize device and model config
device = 'cpu'  # Use CPU since CUDA is unavailable
print(device)

# Load GPT-2 model instead of zephyr-7b-beta
model_id = 'gpt2'  # Use GPT-2, a smaller and CPU-friendly model
model_config = transformers.AutoConfig.from_pretrained(
    model_id,
    trust_remote_code=True,
    max_new_tokens=1024
)
model = transformers.AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    config=model_config,
    device_map='auto',  # Set to CPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Create the query pipeline for GPT-2 model
query_pipeline = transformers.pipeline(
    'text-generation',
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.float32,  # Use float32 for CPU compatibility
    max_length=6000,
    max_new_tokens=500,
    device_map="auto"
)

llm = HuggingFacePipeline(pipeline=query_pipeline)

# User profile setup
user_profile = """
User Profile:
Age: 40, Gender: Non-Binary, Marital Status: Divorced, Income Level: Medium ($2733),
Education: PhD, Occupation: Manager, Residential Status: Mortgaged, Dependents: 1,
Debt: $27664, Debt-to-Income Ratio: 10.12

Spending:
Groceries: $496.0, Supplies: $454.42, Food: $341.69, Electronics: $351.92,
Home Shopping: $235.68, Others: $253.45
"""

question = "Based on this data, can I buy a Lamborghini?"
prompt = f"{user_profile}\n\nQuestion: {question}"

# Get response from LLM
response = llm(prompt=prompt)

# Display result
from IPython.display import display, Markdown
def colorize_text(text):
    for word, color in zip(["Reasoning", "Question", "Answer", "Total time"], ["blue", "red", "green", "magenta"]):
        text = text.replace(f"{word}:", f"\n\n**<font color='{color}'>{word}:</font>**")
    return text

full_response = f"**Question:** {question}\n\n**Answer:** {response}"
display(Markdown(colorize_text(full_response)))

# Placeholder function for receipt scanning with OCR
def scan_receipt(image):
    try:
        img_orig = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        options = "--psm 6"
        text = pytesseract.image_to_string(img_orig, config=options)
        return text.strip()
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Placeholder function for voice recording
def record_expense(audio_path):
    recognizer = sr.Recognizer()
    with sr.AudioFile(audio_path) as source:
        audio = recognizer.record(source)
    try:
        # Transcribe the audio file
        transcription = recognizer.recognize_google(audio)
        return transcription
    except sr.UnknownValueError:
        return "Audio not clear, please try again."
    except sr.RequestError:
        return "Could not request results; check internet connection."

# Recommendation chatbot function
def recommendation_chatbot(user_input):
    prompt = f"{user_profile}\n\nQuestion: {user_input}"
    response = llm(prompt=prompt)
    return str(response)  # Convert to string if needed

# Wrapper function to handle receipt scanning and voice recording
def process_inputs(image, audio):
    receipt_data = scan_receipt(image)
    expense_data = record_expense(audio)
    return receipt_data, expense_data

# Gradio Interface setup using Blocks
with gr.Blocks() as iface:
    # Centered title and description
    gr.Markdown("<h1 style='text-align: center; font-size: 2.5em; color: #2B2D42;'>SAIVV</h1>")
    gr.Markdown("<p style='text-align: center; font-size: 1.2em; color: #8D99AE;'>An AI-powered fintech solution for tracking expenses and managing finances.</p>")

    # Profile and Spending section with improved formatting
    with gr.Row():
        gr.Markdown("""
            <div style="padding: 20px; border: 1px solid #2B2D42; border-radius: 10px; background-color: #EDF2F4;">
                <h2 style="color: #2B2D42; margin-bottom: 10px;">User Profile: Mohamed</h2>
                <ul style="font-size: 1.1em; color: #2B2D42; list-style-type: none; padding: 0;">
                    <li><strong>Age:</strong> 40</li>
                    <li><strong>Gender:</strong> Male</li>
                    <li><strong>Marital Status:</strong> Married</li>
                    <li><strong>Income Level:</strong> Medium ($2733)</li>
                    <li><strong>Education:</strong> PhD</li>
                    <li><strong>Occupation:</strong> Manager</li>
                    <li><strong>Residential Status:</strong> Mortgaged</li>
                    <li><strong>Dependents:</strong> 1</li>
                    <li><strong>Debt:</strong> $27,664</li>
                    <li><strong>Debt-to-Income Ratio:</strong> 10.12%</li>
                </ul>
            </div>
        """, elem_id="user-profile")

        gr.Markdown("""
            <div style="padding: 20px; border: 1px solid #2B2D42; border-radius: 10px; background-color: #EDF2F4;">
                <h2 style="color: #2B2D42; margin-bottom: 10px;">Spending</h2>
                <ul style="font-size: 1.1em; color: #2B2D42; list-style-type: none; padding: 0;">
                    <li><strong>Groceries:</strong> $496.0</li>
                    <li><strong>Supplies:</strong> $454.42</li>
                    <li><strong>Food:</strong> $341.69</li>
                    <li><strong>Electronics:</strong> $351.92</li>
                    <li><strong>Home Shopping:</strong> $235.68</li>
                    <li><strong>Others:</strong> $253.45</li>
                </ul>
            </div>
        """, elem_id="spending-info")

    # Input and output components for receipt scanning and expense recording
    with gr.Row():
        receipt_input = gr.Image(type="numpy", label="Capture Receipt")
        audio_input = gr.Audio(type="filepath", label="Record Expense by Voice")

    # Outputs for receipt and expense recording
    receipt_output = gr.Textbox(label="Receipt Data")
    expense_output = gr.Textbox(label="Recorded Expense Data")

    # Button to process inputs
    submit_btn = gr.Button("Submit")

    submit_btn.click(process_inputs, inputs=[receipt_input, audio_input], outputs=[receipt_output, expense_output])

# Launch the interface
iface.launch(debug=True)