app.py

import streamlit as st
import cv2
import numpy as np
import tempfile
import os
# import pytesseract
import easyocr

# from langchain.document_loaders.image import UnstructuredImageLoader
# from langchain_community.document_loaders import UnstructuredImageLoader
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace

# Set Hugging Face API keys
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="💊"
)

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("---")

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)

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

if uploaded_file:
    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
        temp_file.write(uploaded_file.read())
        orig_path = temp_file.name

    # Step 1: Read and preprocess image
    image = cv2.imread(orig_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    _, binary_inv = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV)
    kernel = np.ones((3, 3), np.uint8)
    dilated = cv2.dilate(binary_inv, kernel, iterations=1)

    reader = easyocr.Reader(['en'])
    text_list = reader.readtext(dilated, detail=0)
    text = "\n".join(text_list)

    # text = pytesseract.image_to_string(dilated)

    # Save preprocessed image for OCR
    # dilated_path = orig_path.replace(".png", "_dilated.png")
    # cv2.imwrite(dilated_path, dilated)

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

    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)

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

    model = ChatHuggingFace(
        llm=llm_model,
        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)

   
    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(text)
    
        # st.code(extracted_text)

        if st.button("🔍 Analyze Text"):
            with st.spinner("Analyzing..."):
                response = chain.run(prescription_text=text)
                # response = chain.run(prescription_text=extracted_text)
            st.success(response)

    # Cleanup temp files
    os.remove(orig_path)
    os.remove(dilated_path)

else:
    st.markdown("<center><i>Upload a prescription image to begin analysis.</i></center>", unsafe_allow_html=True)



        # 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)