Update app.py

app.py

@@ -1,24 +1,21 @@
-import gradio as gr
 import fitz  # PyMuPDF
 import re
+import gradio as gr
 import pandas as pd
 import matplotlib.pyplot as plt
+import io
 
-st.set_page_config(page_title="Medical Report Analyzer", layout="centered")
-
-st.title("🩺 Medical Report Analyzer")
-uploaded_file = st.file_uploader("Upload Medical PDF Report", type=["pdf"])
-
+# Normal reference ranges
 normal_ranges = {
     "Hemoglobin": (13.5, 17.5),  # g/dL
-    "WBC": (4000, 11000),        # cells/mcL
-    "Platelets": (150000, 450000),  # cells/mcL
-    "RBC": (4.7, 6.1),           # million cells/mcL
+    "WBC": (4000, 11000),        # /µL
+    "Platelets": (150000, 450000),  # /µL
+    "RBC": (4.7, 6.1),           # million/µL
     "Creatinine": (0.6, 1.3),    # mg/dL
-    "Glucose": (70, 99),         # mg/dL
+    "Glucose": (70, 99),         # mg/dL (fasting)
 }
 
-def extract_text_from_pdf(file):
+def extract_text(file):
     try:
         doc = fitz.open(stream=file.read(), filetype="pdf")
         text = ""
@@ -26,10 +23,13 @@ def extract_text_from_pdf(file):
             text += page.get_text()
         return text
     except Exception as e:
-        st.error(f"❌ Error reading PDF: {e}")
-        return None
+        return f"❌ Error: {str(e)}"
+
+def analyze_report(file):
+    text = extract_text(file)
+    if "❌ Error" in text:
+        return text, None
 
-def parse_report(text):
     results = {}
     for test, (low, high) in normal_ranges.items():
         pattern = rf"{test}[^0-9\-]*([\d\.]+)"
@@ -38,41 +38,46 @@ def parse_report(text):
             value = float(match.group(1))
             status = "Normal" if low <= value <= high else "Abnormal"
             results[test] = {"value": value, "low": low, "high": high, "status": status}
-    return results
 
-def plot_results(results):
-    labels = list(results.keys())
-    values = [results[k]["value"] for k in labels]
-    colors = ["green" if results[k]["status"] == "Normal" else "red" for k in labels]
+    if not results:
+        return "No known lab values found in report.", None
+
+    # Create dataframe
+    df = pd.DataFrame([
+        {
+            "Test": test,
+            "Value": data["value"],
+            "Normal Range": f'{data["low"]} - {data["high"]}',
+            "Status": data["status"]
+        }
+        for test, data in results.items()
+    ])
 
-    fig, ax = plt.subplots()
-    ax.bar(labels, values, color=colors)
+    # Plot chart
+    fig, ax = plt.subplots(figsize=(6, 4))
+    colors = ["green" if row["Status"] == "Normal" else "red" for _, row in df.iterrows()]
+    ax.bar(df["Test"], df["Value"], color=colors)
     ax.set_title("Medical Report Values")
     ax.set_ylabel("Measured Value")
-    plt.xticks(rotation=45)
-    st.pyplot(fig)
+    ax.tick_params(axis='x', rotation=45)
+
+    buf = io.BytesIO()
+    plt.tight_layout()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+
+    return df, buf
 
-if uploaded_file:
-    with st.spinner("🔍 Analyzing report..."):
-        text = extract_text_from_pdf(uploaded_file)
-        if text:
-            results = parse_report(text)
-            if results:
-                df = pd.DataFrame([
-                    {
-                        "Test": test,
-                        "Value": data["value"],
-                        "Normal Range": f'{data["low"]} - {data["high"]}',
-                        "Status": data["status"]
-                    }
-                    for test, data in results.items()
-                ])
-                def highlight_abnormal(row):
-                    return ['background-color: red; color: white' if row.Status == 'Abnormal' else '' for _ in row]
+demo = gr.Interface(
+    fn=analyze_report,
+    inputs=gr.File(label="Upload Medical PDF Report"),
+    outputs=[
+        gr.Dataframe(label="Extracted Values"),
+        gr.Image(type="pil", label="Visualization")
+    ],
+    title="🩺 Medical Report Analyzer",
+    description="Upload a medical lab report (PDF). Extracted lab values will be compared with normal reference ranges. Abnormal values are flagged and visualized in red."
+)
 
-                st.subheader("📋 Extracted Test Results")
-                st.dataframe(df.style.apply(highlight_abnormal, axis=1), use_container_width=True)
-                st.subheader("📊 Visual Comparison")
-                plot_results(results)
-            else:
-                st.warning("No known medical values found.")
+if __name__ == "__main__":
+    demo.launch()