Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+import pytesseract
+import PyPDF2
+import pdfplumber
+import torch
+
+# Load the BART model for summarization and NLI
+@st.cache_resource
+def load_model():
+    return pipeline("zero-shot-classification", model="facebook/bart-large-mnli", device=0 if torch.cuda.is_available() else -1)
+
+classifier = load_model()
+
+# OCR for Image using Tesseract
+def extract_text_from_image(image):
+    return pytesseract.image_to_string(image)
+
+# Extract text from PDF using pdfplumber
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    with pdfplumber.open(pdf_file) as pdf:
+        for page in pdf.pages:
+            text += page.extract_text()
+    return text
+
+# Summarize, interpret and give actionable insights
+def analyze_report(text):
+    # Provide a summary
+    summary = classifier(text, candidate_labels=["summary"], multi_label=False)['labels'][0]
+
+    # Interpretation of results
+    interpretation = classifier(text, candidate_labels=["interpretation", "normal", "abnormal"], multi_label=True)
+
+    # Recommendations
+    recommendations = classifier(text, candidate_labels=["follow-up", "Holistic/OTC treatment", "dietary change", "medication"], multi_label=True)
+
+    return {
+        "summary": summary,
+        "interpretation": interpretation['labels'],
+        "recommendations": recommendations['labels']
+    }
+
+# Streamlit UI
+st.title("Medical Lab Report Analyzer")
+st.write("Upload your medical lab report (PDF/Image) for insights.")
+
+uploaded_file = st.file_uploader("Choose a PDF/Image file", type=["pdf", "png", "jpg", "jpeg"])
+
+if uploaded_file:
+    file_type = uploaded_file.type
+
+    # Extract text based on file type
+    if file_type == "application/pdf":
+        with st.spinner("Extracting text from PDF..."):
+            extracted_text = extract_text_from_pdf(uploaded_file)
+    else:
+        with st.spinner("Extracting text from Image..."):
+            image = Image.open(uploaded_file)
+            extracted_text = extract_text_from_image(image)
+
+    # Analyze the extracted text
+    if extracted_text:
+        with st.spinner("Analyzing report..."):
+            result = analyze_report(extracted_text)
+        
+        # Display the results
+        st.subheader("Summary")
+        st.write(result['summary'])
+
+        st.subheader("Interpretation of Results")
+        for label in result['interpretation']:
+            st.write(f"- {label.capitalize()}")
+
+        st.subheader("Actionable Recommendations")
+        for rec in result['recommendations']:
+            st.write(f"- {rec.capitalize()}")
+    else:
+        st.error("No text could be extracted. Please try with a different file.")