Update app.py

app.py

@@ -4,45 +4,54 @@ import numpy as np
 import tempfile
 import os
 import easyocr
+from PIL import Image, ImageDraw, ImageFont
+from translate import Translator
 
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from transformers import pipeline
 
-# Set Hugging Face API keys
+# Set API keys
 os.environ["HUGGINGFACEHUB_API_KEY"] = os.getenv("HF")
 os.environ["HF_TOKEN"] = os.getenv("HF")
 
-# Streamlit page setup
-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("---")
+# Function to save text as an image
+def save_text_as_image(text, file_path):
+    font = ImageFont.load_default()
+    lines = text.split('\n')
+    max_width = max([font.getbbox(line)[2] for line in lines]) + 20
+    line_height = font.getbbox(text)[3] + 10
+    img_height = line_height * len(lines) + 20
+
+    img = Image.new("RGB", (max_width, img_height), "white")
+    draw = ImageDraw.Draw(img)
+    y = 10
+    for line in lines:
+        draw.text((10, y), line, font=font, fill="black")
+        y += line_height
+
+    img.save(file_path)
+    return file_path
+
+# Setup
+st.set_page_config(page_title="MediAssist 💊", layout="wide")
 
 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>
+    <style>
+    .stButton>button {
+        background-color: #4CAF50;
+        color: white;
+        font-weight: bold;
+        padding: 8px 20px;
+        border-radius: 8px;
+    }
+    </style>
 """, unsafe_allow_html=True)
 
+st.title("💊 MediAssist - Prescription Analyzer")
+st.markdown("##### Upload your prescription, get AI-based medicine insights, translate and download!")
+
 uploaded_file = st.file_uploader("📤 Upload Prescription Image (JPG/PNG)", type=["jpg", "jpeg", "png"])
 
 if uploaded_file:
@@ -50,39 +59,42 @@ if uploaded_file:
         temp_file.write(uploaded_file.read())
         orig_path = temp_file.name
 
-    # Preprocessing
+    # 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)
 
-    # Save preprocessed image for future reference/removal
     dilated_path = orig_path.replace(".png", "_dilated.png")
     cv2.imwrite(dilated_path, dilated)
 
-    # OCR using EasyOCR
+    # OCR
     reader = easyocr.Reader(['en'])
     text_list = reader.readtext(dilated, detail=0)
     text = "\n".join(text_list)
 
-    # Prompt Template
-    template = """
-        You are a helpful medical assistant.
-
-        Here is a prescription text extracted from an image:
-
-        {prescription_text}
-
-        Please do the following:
-
-        1. Extract only the medicine names mentioned in the prescription (ignore any other text).
-        2. For each medicine, provide:
-           - When to take it (timing and dosage)
-           - Possible side effects
-           - Any special instructions
+    # Display
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        st.image(dilated, caption="Preprocessed Prescription", channels="GRAY", use_container_width=True)
+    with col2:
+        st.success("✅ Image Uploaded and Preprocessed")
+        st.markdown("#### 📝 Extracted Text from Image")
+        st.code(text)
 
-        Format your answer as bullet points, listing only medicines and their details.
+    # Prompt
+    template = """
+    You are a helpful medical assistant.
+    Here is a prescription text extracted from an image:
+    {prescription_text}
+    Please do the following:
+    1. Extract only the medicine names.
+    2. For each, give:
+       - Dosage and Timing
+       - Possible Side Effects
+       - Special Instructions
+    Format in bullet points, medicine-wise.
     """
     prompt = PromptTemplate(input_variables=["prescription_text"], template=template)
 
@@ -93,7 +105,7 @@ if uploaded_file:
         max_new_tokens=300,
         task="conversational"
     )
-
+    
     llm = ChatHuggingFace(
         llm=llm_model,
         repo_id="aaditya/Llama3-OpenBioLLM-70B",
@@ -102,30 +114,189 @@ if uploaded_file:
         max_new_tokens=300,
         task="conversational"
     )
-
+    
     chain = LLMChain(llm=llm, prompt=prompt)
 
-    col1, col2 = st.columns([1, 2])
-
-    with col1:
-        st.image(dilated, caption="Preprocessed Prescription", channels="GRAY", use_container_width=True)
+    if st.button("🔍 Analyze Extracted Text"):
+        with st.spinner("Analyzing with LLM..."):
+            response = chain.run(prescription_text=text)
+        st.markdown("#### 💡 Analyzed Medicine Info")
+        st.text_area("Output", response, height=300)
 
-    with col2:
-        st.success("✅ Prescription Uploaded & Preprocessed Successfully")
-        st.markdown("### 📜 Extracted Text")
-        st.code(text)
+        # Save txt and image
+        txt_path = "prescription_output.txt"
+        with open(txt_path, "w") as f:
+            f.write(response)
 
-        if st.button("🔍 Analyze Text"):
-            with st.spinner("Analyzing..."):
-                response = chain.run(prescription_text=text)
-            st.success(response)
+        img_path = "prescription_output.png"
+        save_text_as_image(response, img_path)
 
-    # Cleanup temp files
+        # Target language code (like 'hi' for Hindi, 'mr' for Marathi, 'gu' for Gujarati)
+        target_lang = "hi"  
+        
+        translator = Translator(to_lang=target_lang)
+        hindi_text = translator.translate(response)
+
+        # # Translation to Hindi
+        # translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
+        # hindi_text = translator(response, max_length=400)[0]['translation_text']
+
+        st.markdown("#### 🌐 Translate to Hindi")
+        st.text_area("Translated (Hindi)", hindi_text, height=300)
+
+        st.markdown("#### 📥 Download Options")
+        colA, colB, colC, colD = st.columns(4)
+        with colA:
+            st.download_button("⬇️ Download TXT", data=response, file_name="medicine_analysis.txt")
+        with colB:
+            with open(img_path, "rb") as img_file:
+                st.download_button("🖼️ Download Image", data=img_file, file_name="medicine_analysis.png", mime="image/png")
+        with colC:
+            st.download_button("⬇️ Hindi TXT", data=hindi_text, file_name="hindi_medicine_analysis.txt")
+        with colD:
+            hindi_img_path = "hindi_output.png"
+            save_text_as_image(hindi_text, hindi_img_path)
+            with open(hindi_img_path, "rb") as hindi_img_file:
+                st.download_button("🖼️ Hindi Image", data=hindi_img_file, file_name="hindi_output.png", mime="image/png")
+
+    # Cleanup
     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("<center><i>📸 Upload a prescription image to get started</i></center>", unsafe_allow_html=True)
+
+
+
+
+
+# import streamlit as st
+# import cv2
+# import numpy as np
+# import tempfile
+# import os
+# import easyocr
+
+# 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")
+
+# # Streamlit page setup
+# 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
+
+#     # Preprocessing
+#     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)
+
+#     # Save preprocessed image for future reference/removal
+#     dilated_path = orig_path.replace(".png", "_dilated.png")
+#     cv2.imwrite(dilated_path, dilated)
+
+#     # OCR using EasyOCR
+#     reader = easyocr.Reader(['en'])
+#     text_list = reader.readtext(dilated, detail=0)
+#     text = "\n".join(text_list)
+
+#     # Prompt Template
+#     template = """
+#         You are a helpful medical assistant.
+
+#         Here is a prescription text extracted from an image:
+
+#         {prescription_text}
+
+#         Please do the following:
+
+#         1. Extract only the medicine names mentioned in the prescription (ignore any other text).
+#         2. For each medicine, provide:
+#            - When to take it (timing and dosage)
+#            - Possible side effects
+#            - Any special instructions
+
+#         Format your answer as bullet points, listing only medicines and their details.
+#     """
+#     prompt = PromptTemplate(input_variables=["prescription_text"], template=template)
+
+#     llm_model = HuggingFaceEndpoint(
+#         repo_id="aaditya/Llama3-OpenBioLLM-70B",
+#         provider="nebius",
+#         temperature=0.6,
+#         max_new_tokens=300,
+#         task="conversational"
+#     )
+
+#     llm = ChatHuggingFace(
+#         llm=llm_model,
+#         repo_id="aaditya/Llama3-OpenBioLLM-70B",
+#         provider="nebius",
+#         temperature=0.6,
+#         max_new_tokens=300,
+#         task="conversational"
+#     )
+
+#     chain = LLMChain(llm=llm, 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)
+
+#         if st.button("🔍 Analyze Text"):
+#             with st.spinner("Analyzing..."):
+#                 response = chain.run(prescription_text=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)