| import streamlit as st
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| import base64
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| import requests
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|
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|
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| GROK_API_KEY = st.secrets["GROK_API_KEY"]
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|
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| GROK_API_URL = "https://api.x.ai/v1/chat/completions"
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|
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| sample_prompt = """You are a medical practitioner and an expert in analyzing medical-related images working for a very reputed hospital. You will be provided with images and you need to identify the anomalies, any disease or health issues. You need to generate the result in a detailed manner. Write all the findings, next steps, recommendation, etc. You only need to respond if the image is related to a human body and health issues. You must have to answer but also write a disclaimer saying that "Consult with a Doctor before making any decisions".
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|
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| Remember, if certain aspects are not clear from the image, it's okay to state 'Unable to determine based on the provided image.'
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|
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| Now analyze the image and answer the above questions in the same structured manner defined above."""
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|
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| if 'uploaded_file' not in st.session_state:
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| st.session_state.uploaded_file = None
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| if 'result' not in st.session_state:
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| st.session_state.result = None
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|
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| def encode_image_bytes(uploaded_file):
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| file_bytes = uploaded_file.read()
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| return base64.b64encode(file_bytes).decode('utf-8')
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|
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| def call_grok_model_for_analysis(uploaded_file, sample_prompt=sample_prompt):
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| base64_image = encode_image_bytes(uploaded_file)
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| messages = [
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| {
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| "role": "user",
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| "content": [
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| {"type": "text", "text": sample_prompt},
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| {
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| "type": "image_url",
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| "image_url": {
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| "url": f"data:image/jpeg;base64,{base64_image}",
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| "detail": "high"
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| }
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| }
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| ]
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| }
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| ]
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| headers = {
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| "Authorization": f"Bearer {GROK_API_KEY}",
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| "Content-Type": "application/json"
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| }
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| data = {
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| "model": "grok-1.5",
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| "messages": messages,
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| "max_tokens": 1500
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| }
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| response = requests.post(GROK_API_URL, headers=headers, json=data)
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| if response.status_code == 200:
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| return response.json()['choices'][0]['message']['content']
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| else:
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| return f"Error: {response.status_code} - {response.text}"
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|
|
| def chat_eli(query):
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| eli5_prompt = "You have to explain the below piece of information to a five years old:\n" + query
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| messages = [
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| {
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| "role": "user",
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| "content": eli5_prompt
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| }
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| ]
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| headers = {
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| "Authorization": f"Bearer {GROK_API_KEY}",
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| "Content-Type": "application/json"
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| }
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| data = {
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| "model": "grok-1.5",
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| "messages": messages,
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| "max_tokens": 1500
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| }
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| response = requests.post(GROK_API_URL, headers=headers, json=data)
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| if response.status_code == 200:
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| return response.json()['choices'][0]['message']['content']
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| else:
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| return f"Error: {response.status_code} - {response.text}"
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| st.title("🧠 Medical Help using Multimodal LLM (Grok xAI)")
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|
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| with st.expander("ℹ️ About this App"):
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| st.write("Upload a medical-related image to get an AI-based analysis using Grok (xAI) with vision capabilities.")
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|
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| uploaded_file = st.file_uploader("📤 Upload an Image", type=["jpg", "jpeg", "png"])
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|
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| if uploaded_file is not None:
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| st.session_state['uploaded_file'] = uploaded_file
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| st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
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|
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| if st.button('🔍 Analyze Image'):
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| if st.session_state['uploaded_file'] is not None:
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| with st.spinner("Analyzing the image with Grok..."):
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| st.session_state['result'] = call_grok_model_for_analysis(st.session_state['uploaded_file'])
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| st.markdown(st.session_state['result'], unsafe_allow_html=True)
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|
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| if st.session_state.get('result'):
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| st.info("Want a simple explanation? Try ELI5 👶")
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| if st.radio("Explain Like I'm 5 (ELI5)?", ('No', 'Yes')) == 'Yes':
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| with st.spinner("Simplifying..."):
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| simplified_explanation = chat_eli(st.session_state['result'])
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| st.markdown(simplified_explanation, unsafe_allow_html=True)
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|
|
