Update app.py

app.py

@@ -202,6 +202,66 @@ def extract_title(text):
     return title[-200:]
 
 def process_image(image_input, user_prompt):
+    SaveNewFile=True
+    image_file_name=''
+    if isinstance(image_input, str):
+        image_file_name = image_input
+        with open(image_input, "rb") as image_file:
+            image_input = image_file.read()
+            SaveNewFile=False # file is there and this is just prompt inference
+    else:
+        image_file_name = image_input.name
+        image_input = image_input.read()
+        SaveNewFile=True
+    
+    st.markdown('Processing image: ' + image_file_name)
+    base64_image = base64.b64encode(image_input).decode("utf-8")
+    response = client.chat.completions.create(
+        model=MODEL,
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant that responds in Markdown."},
+            {"role": "user", "content": [
+                {"type": "text", "text": user_prompt},
+                {"type": "image_url", "image_url": {
+                    "url": f"data:image/png;base64,{base64_image}"}
+                }
+            ]}
+        ],
+        temperature=0.0,
+    )
+    image_response = response.choices[0].message.content
+    st.markdown(image_response)
+    
+    # Save markdown on image AI output from gpt4o
+    filename_md = generate_filename(image_file_name + '- ' + image_response, "md")
+    # Save markdown on image AI output from gpt4o 
+    filename_png = filename_md.replace('.md', '.' + image_file_name.split('.')[-1])
+            
+    create_file(filename_md, image_response, '', True)
+
+    with open(filename_md, "w", encoding="utf-8") as f:
+        f.write(image_response)
+
+    # Extract boldface terms from image_response then autoname save file
+    boldface_terms = extract_title(image_response).replace(':','')
+    filename_stem, extension = os.path.splitext(image_file_name)
+    filename_img = f"{filename_stem}  {''.join(boldface_terms)}{extension}"
+    if SaveNewFile:
+        newfilename = save_image(image_input, filename_img)
+        filename_md = newfilename.replace('.png', '.md')
+        create_file(filename_md, '', image_response, True)
+    else:
+        
+        filename = generate_filename(filename_md, "md")
+        create_file(filename, image_file_name, image_response, should_save)
+        
+        #filename_md = image_file_name.replace('.png', '.md')
+        #create_file(filename_md, '', image_response, True)
+
+    
+    return image_response
+
+def process_image_old(image_input, user_prompt):
     if image_input:
         st.markdown('Processing image:  ' + image_input.name )
         if image_input:
@@ -1052,7 +1112,7 @@ def display_glossary(glossary, area):
 
 
 #@st.cache_resource
-def display_videos_and_links(num_columns):
+def display_videos_and_links_old(num_columns):
     video_files = [f for f in os.listdir('.') if f.endswith('.mp4')]
     if not video_files:
         st.write("No MP4 videos found in the current directory.")
@@ -1072,7 +1132,6 @@ def display_videos_and_links(num_columns):
             display_glossary_entity(k)  
         col_index += 1  # Increment column index to place the next video in the next column
 
-#@st.cache_resource
 def display_images_and_wikipedia_summaries(num_columns=4):
     image_files = [f for f in os.listdir('.') if f.endswith('.png')]
     if not image_files:
@@ -1084,6 +1143,55 @@ def display_images_and_wikipedia_summaries(num_columns=4):
     cols = st.columns(num_columns)  # Use specified num_columns for layout
     col_index = 0  # Initialize column index for cycling through columns
 
+    for image_file in image_files_sorted:
+        with cols[col_index % num_columns]:  # Cycle through columns based on num_columns
+            image = Image.open(image_file)
+            st.image(image, caption=image_file, use_column_width=True)
+            k = image_file.split('.')[0]  # Assumes keyword is the file name without extension
+            display_glossary_entity(k)
+            
+            # Add text input for image file
+            image_text_input = st.text_input(f"Image Prompt for {image_file}", key=f"image_prompt_{image_file}")
+            if image_text_input:
+                process_image(image_file, image_text_input)
+                
+        col_index += 1  # Increment to move to the next column in the next iteration
+
+def display_videos_and_links(num_columns):
+    video_files = [f for f in os.listdir('.') if f.endswith('.mp4')]
+    if not video_files:
+        st.write("No MP4 videos found in the current directory.")
+        return
+    
+    video_files_sorted = sorted(video_files, key=lambda x: len(x.split('.')[0]))
+    cols = st.columns(num_columns)  # Define num_columns columns outside the loop
+    col_index = 0  # Initialize column index
+
+    for video_file in video_files_sorted:
+        with cols[col_index % num_columns]:  # Use modulo to alternate between columns
+            k = video_file.split('.')[0]  # Assumes keyword is the file name without extension
+            st.video(video_file, format='video/mp4', start_time=0)
+            display_glossary_entity(k)  
+            
+            # Add text input for video file
+            video_text_input = st.text_input(f"Video Prompt for {video_file}", key=f"video_prompt_{video_file}")
+            if video_text_input:
+                process_video(video_file, video_text_input)
+                
+        col_index += 1  # Increment column index to place the next video in the next column
+
+#@st.cache_resource
+def display_images_and_wikipedia_summaries_old(num_columns=4):
+    image_files = [f for f in os.listdir('.') if f.endswith('.png')]
+    if not image_files:
+        st.write("No PNG images found in the current directory.")
+        return
+
+    image_files_sorted = sorted(image_files, key=lambda x: len(x.split('.')[0]))
+
+    cols = st.columns(num_columns)  # Use specified num_columns for layout
+    col_index = 0  # Initialize column index for cycling through columns
+
     for image_file in image_files_sorted:
         with cols[col_index % num_columns]:  # Cycle through columns based on num_columns
             image = Image.open(image_file)