Restore streamlit_app.py with proper code

src/streamlit_app.py

@@ -1 +1,57 @@
-Fix indentation and unify spaces
+import streamlit as st
+import numpy as np
+from pathlib import Path
+import sys
+
+# Ensure src directory is on Python path for inference import
+sys.path.append(str((Path(__file__).resolve().parent)))
+
+from inference.model import run_inference
+
+# Configure the Streamlit page
+st.set_page_config(page_title="DeepECG PDF Demo", layout="centered")
+
+st.title("\U0001F4AC DeepECG - PDF Analyse (Demo)")
+st.markdown(
+    """
+    **Hinweis:**
+    Diese Demo analysiert EKG-PDFs zu Evaluations- und Forschungszwecken.
+    Kein Medizinprodukt. Keine Patientendaten speichern.
+    """
+)
+
+# File uploader for EKG PDFs
+uploaded_file = st.file_uploader(
+    "EKG-PDF hochladen",
+    type=["pdf"]
+)
+
+# When a file is uploaded
+if uploaded_file is not None:
+    st.success("PDF erfolgreich hochgeladen")
+
+    if st.button("\U0001F9E0 Analyse starten"):
+        with st.spinner("Analyse läuft ... bitte warten"):
+            # TODO: Replace this dummy signal with real PDF-to-signal extraction
+            dummy_signal = np.random.rand(12, 5000)
+
+            # Run inference (real model)
+            results = run_inference(dummy_signal)
+
+            # Display results
+            st.subheader("Ergebnis")
+
+            st.metric(
+                label="Gesamtrisiko",
+                value=f"{results['risk_score']} %"
+            )
+
+            st.subheader("Top-Diagnosen")
+            for diag in results["top_diagnoses"]:
+                st.write(f"- {diag['label']} ({diag['probability']} %)")
+
+            st.subheader("Einschätzung")
+            st.success(results["interpretation"])
+
+            st.subheader("Beispiel-EKG (Lead I)")
+            st.line_chart(dummy_signal[0])