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import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv
from PyPDF2 import PdfReader
import tempfile
load_dotenv()
groq_api_key = os.getenv('GROQ_API_KEY')
st.title("🔬 Health Report Analyzer")
st.markdown("Quick analysis of your health reports")
st.error("**Note:** Always consult healthcare professionals for medical advice")
llm = ChatGroq(
groq_api_key=groq_api_key,
model_name="Llama3-8b-8192",
temperature=0.3,
max_tokens=2048
)
analysis_prompt = ChatPromptTemplate.from_template(
"""Analyze this report section briefly. Provide key insights in 2-3 sentences maximum.
Report Section: {report_content}
Question: {user_input}
Give a direct, focused response:"""
)
def extract_pdf_text(pdf_file):
try:
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(pdf_file.getvalue())
tmp_file.seek(0)
pdf_reader = PdfReader(tmp_file.name)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
except Exception as e:
st.error(f"Error processing PDF: {str(e)}")
return None
finally:
if 'tmp_file' in locals():
os.unlink(tmp_file.name)
def chunk_text(text, chunk_size=1000):
words = text.split()
chunks = []
current_chunk = []
current_length = 0
for word in words:
current_length += len(word) + 1
if current_length > chunk_size:
chunks.append(' '.join(current_chunk))
current_chunk = [word]
current_length = len(word)
else:
current_chunk.append(word)
if current_chunk:
chunks.append(' '.join(current_chunk))
return chunks
def analyze_chunk(chunk, question):
try:
formatted_prompt = analysis_prompt.format(
report_content=chunk,
user_input=question
)
response = llm.invoke(formatted_prompt)
return response.content
except Exception as e:
return f"Error analyzing this section: {str(e)}"
if "messages" not in st.session_state:
st.session_state.messages = []
if "report_chunks" not in st.session_state:
st.session_state.report_chunks = None
uploaded_file = st.file_uploader("Upload health report (PDF)", type="pdf")
if uploaded_file:
with st.spinner("Processing..."):
report_text = extract_pdf_text(uploaded_file)
if report_text:
st.session_state.report_chunks = chunk_text(report_text)
st.success("Report processed!")
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Ask about your report"):
if not st.session_state.report_chunks:
st.warning("Please upload a report first!")
else:
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
with st.spinner("Analyzing..."):
analyses = [analyze_chunk(chunk, prompt) for chunk in st.session_state.report_chunks]
final_response = " ".join(filter(None, analyses))
st.markdown(final_response)
st.session_state.messages.append({"role": "assistant", "content": final_response})
with st.sidebar:
st.markdown("""
### 📊 Quick Guide
- Lab results
- Vital signs
- Test results
- Measurements
""") |