Create app.py

app.py

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+import streamlit as st
+import easyocr
+import cv2
+import numpy as np
+from PIL import Image
+import io
+from transformers import pipeline
+
+# Initialize OCR reader
+reader = easyocr.Reader(['en'])
+
+# Initialize Hugging Face text classifier (use a pre-trained or custom model)
+classifier = pipeline("text-classification", model="distilbert-base-uncased")
+
+# Function to check if the image is clear
+def is_image_clear(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
+    return laplacian_var > 100  # Threshold to determine clarity
+
+# Function to extract text using OCR
+def extract_text_from_image(image):
+    result = reader.readtext(image)
+    text = " ".join([res[1] for res in result])
+    return text
+
+# Function to analyze the text for abnormalities
+def analyze_report(text):
+    result = classifier(text)
+    severity = "Normal"
+    explanation = "No abnormalities detected"
+
+    # Example logic to determine severity (you can expand this)
+    if 'elevated' in text or 'high' in text:
+        severity = "Moderate"
+        explanation = "Elevated levels detected (e.g., glucose, blood pressure)"
+    elif 'critical' in text:
+        severity = "Severe"
+        explanation = "Critical levels detected"
+    return severity, explanation
+
+# Streamlit UI
+st.title("Medical Report Analysis Chatbot")
+
+uploaded_file = st.file_uploader("Upload your medical report image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Medical Report", use_column_width=True)
+    
+    # Convert to OpenCV format for clarity check
+    open_cv_image = np.array(image)
+    open_cv_image = open_cv_image[:, :, ::-1].copy()  # Convert RGB to BGR
+    
+    if is_image_clear(open_cv_image):
+        st.success("Image clarity: Clear")
+        
+        # Extract text
+        text = extract_text_from_image(open_cv_image)
+        st.write("Extracted Text: ")
+        st.write(text)
+        
+        # Analyze report
+        severity, explanation = analyze_report(text)
+        st.write(f"Report Type: Medical Report")
+        st.write(f"Severity Level: {severity}")
+        st.write(f"Explanation: {explanation}")
+        
+        # Ask for doctor consultation
+        consultation = st.radio("Would you like to consult a doctor?", ("Yes", "No"))
+        if consultation == "Yes":
+            st.write("Consultation fee: $50")
+            st.write("Your report will be sent to the doctor for further diagnosis.")
+    else:
+        st.warning("Image clarity: Blurry. Please upload a clearer image.")