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MediAssist
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import streamlit as st
import cv2
import numpy as np
import tempfile
import os

from langchain_community.document_loaders import UnstructuredImageLoader
from langchain.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace

os.environ["HUGGINGFACEHUB_API_KEY"] = os.getenv("HF")
os.environ["HF_TOKEN"] = os.getenv("HF")

st.set_page_config(
    page_title="MediAssist - Prescription Analyzer",
    layout="wide",
    page_icon="💊"
)

# Sidebar
st.sidebar.title("💊 MediAssist")
st.sidebar.markdown("Analyze prescriptions with ease using AI")
st.sidebar.markdown("---")

st.sidebar.markdown("🔗 **Connect with me:**")
st.sidebar.markdown("""
<div style='display: flex; gap: 10px;'>
    <a href="https://github.com/Yashvj22" target="_blank">
        <img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white" style="height:30px;">
    </a>
    <a href="https://www.linkedin.com/in/yash-jadhav-454b0a237/" target="_blank">
        <img src="https://img.shields.io/badge/LinkedIn-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white" style="height:30px;">
    </a>
</div>
""", unsafe_allow_html=True)

st.sidebar.markdown("---")

# Main Title
st.markdown("""
    <h1 style='text-align: center; color: #4A90E2;'>🧠 MediAssist</h1>
    <h3 style='text-align: center;'>Prescription Analyzer using AI and OCR</h3>
    <p style='text-align: center;'>Upload a doctor's prescription image, and MediAssist will extract, translate, and explain it for you.</p>
    <br>
""", unsafe_allow_html=True)

# Image Upload
uploaded_file = st.file_uploader("📤 Upload Prescription Image (JPG/PNG)", type=["jpg", "jpeg", "png"])

 
dilated_path = orig_path.replace(".png", "_dilated.png")
cv2.imwrite(dilated_path, dilated)

# Load and extract text
loader = UnstructuredImageLoader(dilated_path)
documents = loader.load()
extracted_text = "\n".join([doc.page_content for doc in documents])

# Define prompt
template = """
You are a helpful assistant. Here is a prescription text extracted from an image:

{prescription_text}

Please summarize the key medicine names and instructions in bullet points.
"""
prompt = PromptTemplate(input_variables=["prescription_text"], template=template)

model = HuggingFaceEndpoint(
    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
    provider="novita",
    temperature=0.6,
    max_new_tokens=300,
    task="conversational"
)

chain = LLMChain(llm=model, prompt=prompt)

# Columns for layout
col1, col2 = st.columns([1, 2])

    with col1:
        st.image(dilated, caption="Preprocessed Prescription", channels="GRAY", use_container_width=True)

    with col2:
        st.success("✅ Prescription Uploaded & Preprocessed Successfully")

        st.markdown("### 📜 Extracted Text")
        st.code(extracted_text)
    
        if st.button("🔍 Analyze Text"):
            with st.spinner("Analyzing..."):
                response = chain.run(prescription_text=extracted_text)
            # st.markdown("### LLM Output")
            st.success(response)


        # st.markdown("### 🌐 Translated Text")
        # st.code("पेरासिटामोल 500 मिलीग्राम\nभोजन के बाद दिन में दो बार 1 गोली लें", language='text')

        # st.markdown("### ⏱️ Tablet Timing & Instructions")
        # st.info("- Morning after breakfast\n- Night after dinner\n- Take with water\n- Do not exceed 2 tablets in 24 hours")

        # st.markdown("### ⚠️ Possible Side Effects")
        # st.warning("- Nausea\n- Dizziness\n- Liver damage (on overdose)")

    os.remove(temp_path)
    os.remove(orig_path)
    os.remove(dilated_path)

else:
    st.image("https://cdn.dribbble.com/users/285475/screenshots/14711920/media/bd46dc2873f7099e4ef9fd53e6f7f1df.png", width=600)
    st.markdown("<center><i>Upload a prescription image to begin analysis.</i></center>", unsafe_allow_html=True)