Update app.py

app.py

@@ -5,6 +5,9 @@ from PIL import Image
 from transformers import pipeline
 import re
 
+# Ensure Tesseract-OCR is properly configured (Uncomment & update path if needed)
+# pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
+
 # Load pre-trained Hugging Face models
 summarizer = pipeline("summarization", model="t5-small")
 medical_qa = pipeline("question-answering", model="deepset/bert-base-cased-squad2")
@@ -13,56 +16,61 @@ medical_qa = pipeline("question-answering", model="deepset/bert-base-cased-squad
 def extract_text_from_pdf(pdf_file):
     with pdfplumber.open(pdf_file) as pdf:
         text = "\n".join(page.extract_text() for page in pdf.pages if page.extract_text())
-    return text if text else "No text found."
+    return text if text else "No text found in PDF."
 
-# Function to extract text from images
+# Function to extract text from images (JPG, PNG)
 def extract_text_from_image(image_file):
     image = Image.open(image_file)
     text = pytesseract.image_to_string(image)
-    return text if text.strip() else "No text found in the image."
+    return text.strip() if text else "No text found in Image."
 
 # Function to summarize medical report
 def summarize_report(text):
-    text = text[:1000]  # Limiting input text length for better summarization accuracy
+    if len(text) > 500:  # Handle long text
+        text = text[:500]  
     summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
     return summary[0]['summary_text']
 
-# Function to explain medical terms dynamically
+# Function to find medical terms dynamically using regex
 def extract_medical_terms(text):
-    words = re.findall(r'\b[A-Z][a-z]+(?:\s[A-Z][a-z]+)*\b', text)
-    return list(set(words))[:5]  # Extract and limit to top 5 terms
+    words = re.findall(r'\b[A-Z][a-z]+(?:[ -][A-Z][a-z]+)*\b', text)
+    return list(set(words))
 
-# Function to get explanation for medical terms
+# Function to explain medical terms
 def explain_term(term):
-    context = "Medical terminology explanation."
+    context = "Hypercholesterolemia is a condition with high cholesterol in the blood. Atherosclerosis refers to artery narrowing due to fat buildup."
     response = medical_qa(question=f"What is {term}?", context=context)
     return response["answer"]
 
 # Streamlit UI
-st.title("🩺 AI Medical Report Summarizer & Explanation Assistant")
-st.write("Upload a medical report (PDF/Image) and get a simplified summary with medical term explanations.")
+st.title("🩺 AI Medical Report Analyzer")
+st.write("Upload a medical **PDF or Image (JPG, PNG)** to get a summarized report with term explanations.")
+
+uploaded_file = st.file_uploader("Upload a PDF or Image", type=["pdf", "jpg", "png"])
 
-uploaded_file = st.file_uploader("Upload Medical Report (PDF or Image)", type=["pdf", "png", "jpg"])
 if uploaded_file:
-    if uploaded_file.type == "application/pdf":
+    file_type = uploaded_file.type
+
+    if file_type == "application/pdf":
         text = extract_text_from_pdf(uploaded_file)
-    else:
+        st.subheader("📜 Extracted Text from PDF:")
+    elif file_type in ["image/png", "image/jpeg"]:
         text = extract_text_from_image(uploaded_file)
-    
-    st.subheader("📜 Extracted Medical Report Text:")
+        st.subheader("🖼️ Extracted Text from Image:")
+
     st.text_area("Report Content:", text, height=200)
-    
+
     if st.button("Generate AI Summary"):
         summary = summarize_report(text)
         st.subheader("📑 AI-Generated Summary:")
-        st.markdown(f"<div style='background-color:#f0f8ff;padding:10px;border-radius:5px;'>{summary}</div>", unsafe_allow_html=True)
-    
+        st.markdown(f"**{summary}**")
+
     if st.button("Explain Medical Terms"):
         terms = extract_medical_terms(text)
         if terms:
             st.subheader("📖 Medical Term Explanations:")
-            for term in terms:
+            for term in terms[:5]:  # Limit to 5 terms for efficiency
                 explanation = explain_term(term)
                 st.markdown(f"**{term}:** {explanation}")
         else:
-            st.write("No specific medical terms detected in the report.")
+            st.write("No medical terms detected.")