Update app.py

app.py

@@ -413,51 +413,51 @@ else:
 
 
 
-
-with column1:
-# Create a canvas component
-    changes,implementation,current=st.columns([0.01,0.9,0.01])
-
-    with implementation:
-                st.write("<br>"*3,unsafe_allow_html=True)
-                if bg_doc:
-                    
-                    canvas_result=None
-
-                    with open("temp.pdf", "wb") as f:
-                        f.write(bg_doc.getbuffer())
-                    
-                    # Process the uploaded PDF file
-                    data = process_pdf("temp.pdf")
-                    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-                    chunks = text_splitter.split_documents(data)
-                    # chunk_texts = [str(chunk.page_content) for chunk in chunks] 
-                    # print("testing",chunk_texts)
-                    model_name = "all-MiniLM-L6-v2"
-                    model = SentenceTransformer(model_name)
-                    embeddings = [model.encode(str(chunk.page_content)) for chunk in chunks]
-
-                    vector_store = []
-                    for chunk, embedding in zip(chunks, embeddings):
-                        vector_store.append((embedding, chunk.page_content) )
+with st.spinner('Wait for it...'):
+    with column1:
+    # Create a canvas component
+        changes,implementation,current=st.columns([0.01,0.9,0.01])
+    
+        with implementation:
+                    st.write("<br>"*3,unsafe_allow_html=True)
+                    if bg_doc:
                         
-                else:
-
-                    
-                    
-                    canvas_result = st_canvas(
-                        fill_color="rgba(0, 0, 0, 0.3)",  # Fixed fill color with some opacity
-                        stroke_width=stroke_width,
-                        stroke_color=stroke_color,
-                        background_color=bg_color,
-                        background_image=gen_image if gen_image else Image.open("/home/user/app/ALL_image_formation/image_gen.png"),
-                        update_streamlit=True,
-                        height=int(screen_height//2.16) if screen_height!=1180 else screen_height//2,
-                        width=int(screen_width//2.3)  if screen_width!=820 else screen_width//2,
-                        drawing_mode=drawing_mode,
-                        point_display_radius=point_display_radius if drawing_mode == 'point' else 0,
-                        key="canvas",
-                    )
+                        canvas_result=None
+    
+                        with open("temp.pdf", "wb") as f:
+                            f.write(bg_doc.getbuffer())
+                        
+                        # Process the uploaded PDF file
+                        data = process_pdf("temp.pdf")
+                        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+                        chunks = text_splitter.split_documents(data)
+                        # chunk_texts = [str(chunk.page_content) for chunk in chunks] 
+                        # print("testing",chunk_texts)
+                        model_name = "all-MiniLM-L6-v2"
+                        model = SentenceTransformer(model_name)
+                        embeddings = [model.encode(str(chunk.page_content)) for chunk in chunks]
+    
+                        vector_store = []
+                        for chunk, embedding in zip(chunks, embeddings):
+                            vector_store.append((embedding, chunk.page_content) )
+                            
+                    else:
+    
+                        
+                        
+                        canvas_result = st_canvas(
+                            fill_color="rgba(0, 0, 0, 0.3)",  # Fixed fill color with some opacity
+                            stroke_width=stroke_width,
+                            stroke_color=stroke_color,
+                            background_color=bg_color,
+                            background_image=gen_image if gen_image else Image.open("/home/user/app/ALL_image_formation/image_gen.png"),
+                            update_streamlit=True,
+                            height=int(screen_height//2.16) if screen_height!=1180 else screen_height//2,
+                            width=int(screen_width//2.3)  if screen_width!=820 else screen_width//2,
+                            drawing_mode=drawing_mode,
+                            point_display_radius=point_display_radius if drawing_mode == 'point' else 0,
+                            key="canvas",
+                        )
 
 
 
@@ -471,35 +471,36 @@ with column1:
 
     # run=st.button("run_experiment")
 if bg_doc:
-    if len(dictionary['every_prompt_with_val'])==0:
-        query_embedding = model.encode(["something"])
-    else:
-
-        query_embedding = model.encode([dictionary['every_prompt_with_val'][-1][0]])
-    retrieved_chunks = max([(util.cos_sim(match[0],query_embedding),match[-1])for  match in vector_store])[-1]
-
-
-
-    with implementation:
-        with st.spinner('Wait for it...'):        
-            text_lookup=retrieved_chunks
-            pages=[]
-            buffer = bg_doc.getbuffer()
-            byte_data = bytes(buffer)
-            with fitz.open("temp.pdf") as doc:
-           
-                for page_no in range(doc.page_count):
-                    pages.append(doc.load_page(page_no - 1))
-                
-                # areas = pages[page_number-1].search_for(text_lookup)
-                with st.container(height=int(screen_height//1.8)):
-                    for pg_no in pages[::-1]:
-                        areas = pg_no.search_for(text_lookup)
-                        for area in areas:
-                            pg_no.add_rect_annot(area)
-
-                        pix = pg_no.get_pixmap(dpi=100).tobytes()
-                        st.image(pix,use_container_width=True)
+    with st.spinner('Wait for it...'):
+        if len(dictionary['every_prompt_with_val'])==0:
+            query_embedding = model.encode(["something"])
+        else:
+    
+            query_embedding = model.encode([dictionary['every_prompt_with_val'][-1][0]])
+        retrieved_chunks = max([(util.cos_sim(match[0],query_embedding),match[-1])for  match in vector_store])[-1]
+    
+    
+    
+        with implementation:
+            with st.spinner('Wait for it...'):        
+                text_lookup=retrieved_chunks
+                pages=[]
+                buffer = bg_doc.getbuffer()
+                byte_data = bytes(buffer)
+                with fitz.open("temp.pdf") as doc:
+               
+                    for page_no in range(doc.page_count):
+                        pages.append(doc.load_page(page_no - 1))
+                    
+                    # areas = pages[page_number-1].search_for(text_lookup)
+                    with st.container(height=int(screen_height//1.8)):
+                        for pg_no in pages[::-1]:
+                            areas = pg_no.search_for(text_lookup)
+                            for area in areas:
+                                pg_no.add_rect_annot(area)
+    
+                            pix = pg_no.get_pixmap(dpi=100).tobytes()
+                            st.image(pix,use_container_width=True)
                         
 if bg_doc and prompt:
     query_embedding = model.encode([prompt])