FIXed imports

api_backend.py

@@ -10,7 +10,7 @@ import base64
 # 💡 Import modules
 from yolo_module import run_yolo
 from ocr_module import extract_text, count_elements, validate_structure
-from flowchart_builder import map_arrows, build_flowchart_json  # renamed for clarity
+from graph_module import map_arrows, build_flowchart_json 
 from summarizer_module import summarize_flowchart
 
 app = FastAPI()

app.py

@@ -1,23 +1,29 @@
+# app.py
 import streamlit as st
 from PIL import Image
 import io
 import base64
 import os
 
-from yolo_module import run_yolo  # Local detection module
-from ocr_module import extract_text  # EasyOCR wrapper
-from flowchart_builder import map_arrows, build_flowchart_json
-from summarizer import summarize_flowchart  # Your LLM logic
+# Local modules
+from yolo_module import run_yolo
+from ocr_module import extract_text
+from graph_module import map_arrows, build_flowchart_json
+from summarizer_module import summarize_flowchart
 
 st.set_page_config(page_title="Flowchart to English", layout="wide")
 st.title("📄 Flowchart to Plain English")
 
+# Enable debug mode
 debug_mode = st.toggle("🔧 Show Debug Info", value=False)
 
+# Upload image
 uploaded_file = st.file_uploader("Upload a flowchart image", type=["png", "jpg", "jpeg"])
 
 if uploaded_file:
-    image = Image.open(uploaded_file)
+    image = Image.open(uploaded_file).convert("RGB")
+
+    # Show resized preview
     max_width = 600
     ratio = max_width / float(image.size[0])
     resized_image = image.resize((max_width, int(image.size[1] * ratio)))
@@ -26,19 +32,36 @@ if uploaded_file:
     if st.button("🔍 Analyze Flowchart"):
         progress = st.progress(0, text="Detecting boxes and arrows...")
         results, arrows, vis_debug = run_yolo(image)
-        progress.progress(30, text="Running OCR...")
+        progress.progress(25, text="Running OCR...")
+
+        debug_log = []
+        debug_log.append(f"📦 Detected {len(results)} boxes")
+        debug_log.append(f"➡️  Detected {len(arrows)} arrows")
 
-        # Add text to results
         for node in results:
             node["text"] = extract_text(image, node["bbox"], debug=debug_mode)
+            label = node.get("label", "box")
+            text = node["text"]
+            debug_log.append(f"🔖 {node['id']} | Label: {label} | Text: {text}")
 
-        progress.progress(60, text="Building flowchart structure...")
+        progress.progress(50, text="Mapping arrows to nodes...")
         edges = map_arrows(results, arrows)
+
+        progress.progress(75, text="Building graph structure...")
         flowchart = build_flowchart_json(results, edges)
 
-        progress.progress(80, text="Generating plain English explanation...")
+        progress.progress(90, text="Generating explanation...")
         summary = summarize_flowchart(flowchart)
 
+        # Show Debug Info first
+        if debug_mode:
+            st.markdown("### 🧪 Debug Info")
+            st.code("\n".join(debug_log), language="markdown")
+
+            st.markdown("### 🖼️ YOLO Detected Bounding Boxes")
+            st.image(vis_debug, caption="YOLO Detected Boxes", use_container_width=True)
+
+        # Show results: JSON (left), Summary (right)
         col1, col2 = st.columns(2)
         with col1:
             st.subheader("🧠 Flowchart JSON")
@@ -47,18 +70,7 @@ if uploaded_file:
             st.subheader("📝 English Summary")
             st.markdown(summary)
 
-        if debug_mode:
-            st.subheader("🖼️ YOLO Visual Debug")
-            st.image(vis_debug, caption="Detected Boxes & Arrows", use_container_width=True)
-
         progress.progress(100, text="Done!")
+
 else:
-    st.info("Upload a flowchart image to begin.")
-
-# For Render compatibility
-if __name__ == "__main__":
-    import streamlit.web.cli as stcli
-    import sys
-    port = int(os.environ.get("PORT", 7860))
-    sys.argv = ["streamlit", "run", "app.py", "--server.port", str(port), "--server.address=0.0.0.0"]
-    sys.exit(stcli.main())
+    st.info("Upload a flowchart image to begin.")