Update src/streamlit_app.py

src/streamlit_app.py

@@ -8,6 +8,7 @@ from document_classifier import DocumentClassifier
 import time
 from typing import List, Dict
 import json
+import requests
 
 # Page configuration
 st.set_page_config(
@@ -108,6 +109,29 @@ def classify_multiple_files(file_paths: List[str]) -> List[Dict]:
     except Exception as e:
         return [{"error": str(e), "success": False}]
 
+def classify_with_api(file_path: str) -> Dict:
+    """Call FastAPI classify endpoint with image file."""
+    api_url = "http://localhost:8000/classify"  # Adjust if API runs elsewhere
+    try:
+        with open(file_path, "rb") as file_data:
+            files = {"file": (os.path.basename(file_path), file_data)}
+            response = requests.post(api_url, files=files)
+            if response.status_code == 200:
+                data = response.json()
+                # Some keys might not be present in API/minimal - match display_classification_result
+                data.setdefault('file_path', file_path)
+                data.setdefault('file_name', os.path.basename(file_path))
+                data.setdefault('file_extension', os.path.splitext(file_path)[1].replace('.', ''))
+                data.setdefault('content_length', 0)
+                data.setdefault('text_preview', '')
+                data.setdefault('file_type', os.path.splitext(file_path)[1].replace('.', ''))
+                data.setdefault('all_scores', {})
+                return data
+            else:
+                return {"error": response.text, "success": False}
+    except Exception as e:
+        return {"error": str(e), "success": False}
+
 def display_classification_result(result: Dict):
     """Display a single classification result."""
     if not result.get('success', False):
@@ -259,7 +283,18 @@ def main():
     tab1, tab2, tab3 = st.tabs(["📁 Single File", "📂 Batch Upload", "📊 Results"])
     
     with tab1:
-        st.subheader("📁 Classify Single Document")
+        st.subheader("\U0001F4C1 Classify Single Document")
+        sample_scan_folder = os.path.join(os.path.dirname(__file__), "sample_scans")
+        sample_files = [f for f in os.listdir(sample_scan_folder) if f.lower().endswith((".png", ".jpg", ".jpeg"))]
+        sample_files.sort()
+        selected_sample = st.selectbox("Or select from example scans:", ["--- Select a sample ---"] + sample_files)
+        if selected_sample != "--- Select a sample ---":
+            sample_file_path = os.path.join(sample_scan_folder, selected_sample)
+            st.image(sample_file_path, caption=f"Sample: {selected_sample}", use_column_width=True)
+            if st.button("🔍 Classify Sample Scan", key="classify_sample_scan"):
+                with st.spinner("Calling API to classify sample scan..."):
+                    result = classify_with_api(sample_file_path)
+                    display_classification_result(result)
         
         uploaded_file = st.file_uploader(
             "Choose a document file",
@@ -386,4 +421,5 @@ def main():
                     )
 
 if __name__ == "__main__":
-    main()
+    main()
+