app.py

import urllib.request 
import fitz
import re
import numpy as np
import openai
import gradio as gr
import os
from sklearn.neighbors import NearestNeighbors


title = 'MediDiagnostix AI'
description = """MediDiagnostix AI allows you to upload medical reports for analysis. Just click a picture of your medical report or upload a pdf report, it will 
 extract, analyze and provide you the medical interpretations of the report, potential diagnoses, and recommended follow-up actions. Furthermore, you can save diagnosis for future reference"""

import pytesseract # Assuming Tesseract OCR is used for image processing

def analyze_reports(files, num_reports):
 """
 Process and analyze the uploaded reports.

 Args:
 files (list): List of uploaded files (PDFs and images).
 num_reports (int): Number of reports to analyze.

 Returns:
 str: Analysis results in a formatted text.
 """
 # Check if the number of files matches num_reports
 if len(files) != num_reports:
 return "Number of uploaded files does not match the specified number of reports."

 # Initialize a list to hold text from each report
 report_texts = []

 for file in files:
 # Check file type and process accordingly
 if file.name.endswith('.pdf'):
 # Process PDF file
 pdf_text = pdf_to_text(file.name)
 report_texts.extend(pdf_text)
 else:
 # Process Image file
 image_text = image_to_text(file)
 report_texts.append(image_text)

 # Combine texts from all reports
 combined_text = ' '.join(report_texts)

 # Analyze the combined text (Placeholder for actual analysis logic)
 analysis_results = analyze_text(combined_text) # This function needs to be implemented

 return analysis_results

def image_to_text(image_file):
 """
 Extract text from an image file using OCR.

 Args:
 image_file (file): An image file.

 Returns:
 str: Extracted text from the image.
 """
 try:
 # Read the image file
 image = Image.open(image_file)
 # Extract text using OCR
 extracted_text = pytesseract.image_to_string(image)
 return extracted_text
 except Exception as e:
 return f"Error in text extraction from image: {e}"

def analyze_text(text):
 """
 Analyze the extracted text and generate insights.

 Args:
 text (str): Combined text from all reports.

 Returns:
 str: Analysis results based on the text.
 """
 # Placeholder for text analysis logic
 # This could involve calling an AI model, processing the text, etc.
 # Returning a dummy response for demonstration purposes
 return "Analysis results based on the processed text."


with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo:

 gr.Markdown(f'<center><h3>{title}</h3></center>')
 gr.Markdown(description)

 with gr.Row():
 
 with gr.Group():
 gr.Markdown(f'<p style="text-align:center">Enter the number of reports to analyze</p>')
 num_reports = gr.Number(label='Number of Reports', value=1)
 
 with gr.Accordion("Upload Reports"):
 file_upload = gr.File(label='Upload Reports (PDF/Image)', file_types=['.pdf', '.jpg', '.png'], interactive=True, type="file", allow_multiple=True)
 
 analyze_button = gr.Button(value='Analyze Reports')

 with gr.Group():
 analysis_results = gr.Textbox(label='Analysis Results', placeholder="Results will appear here after analysis", lines=20)

 analyze_button.click(
 #func=analyze_reports, # This function needs to be defined to handle the report analysis.
 inputs=[file_upload, num_reports],
 outputs=[analysis_results],
 )

demo.launch()