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| import os | |
| import streamlit as st | |
| from groq import Groq | |
| from PIL import Image | |
| from transformers import TrOCRProcessor, TrOCRForConditionalGeneration | |
| import pytesseract | |
| # Load the TrOCR model | |
| processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") | |
| model = TrOCRForConditionalGeneration.from_pretrained("microsoft/trocr-base-handwritten") | |
| # Set up Groq API client | |
| client = Groq( | |
| api_key=os.environ.get("GROQ_API_KEY"), | |
| ) | |
| # Function to extract text from image using TrOCR | |
| def extract_text_from_image(image): | |
| image = image.convert("RGB") | |
| text = pytesseract.image_to_string(image) | |
| return text | |
| # Function to analyze the extracted text using Groq API | |
| def analyze_report(text): | |
| chat_completion = client.chat.completions.create( | |
| messages=[{"role": "user", "content": text}], | |
| model="llama-3.3-70b-versatile", | |
| ) | |
| return chat_completion.choices[0].message.content | |
| # Streamlit app UI | |
| st.title("Medical Test Report Analyzer") | |
| st.write(""" | |
| Upload a medical test report in JPG format to get an analysis. The application will extract the text from the image | |
| and provide a detailed explanation of the test results, including abnormal findings and recommended actions. | |
| """) | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload a JPG file", type=["jpg", "jpeg"]) | |
| if uploaded_file is not None: | |
| # Display the uploaded image | |
| st.image(uploaded_file, caption="Uploaded Report", use_column_width=True) | |
| # Extract text from the image | |
| image = Image.open(uploaded_file) | |
| extracted_text = extract_text_from_image(image) | |
| if extracted_text: | |
| st.subheader("Extracted Text:") | |
| st.text(extracted_text) | |
| # Send the extracted text to the LLM for analysis | |
| analysis = analyze_report(extracted_text) | |
| st.subheader("Test Report Analysis:") | |
| st.write(analysis) | |
| # Chatbot interface for user queries | |
| st.subheader("Ask Questions About the Test Report:") | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| user_input = st.text_input("Your question:") | |
| if user_input: | |
| # Add user's message to session state | |
| st.session_state.messages.append({"role": "user", "content": user_input}) | |
| # Get response from the model | |
| chat_response = client.chat.completions.create( | |
| messages=st.session_state.messages, | |
| model="llama-3.3-70b-versatile", | |
| ) | |
| response_text = chat_response.choices[0].message.content | |
| st.session_state.messages.append({"role": "assistant", "content": response_text}) | |
| # Display the conversation | |
| for message in st.session_state.messages: | |
| role = message["role"] | |
| content = message["content"] | |
| if role == "user": | |
| st.write(f"**You:** {content}") | |
| else: | |
| st.write(f"**Assistant:** {content}") | |