Update app.py

app.py

@@ -18,6 +18,7 @@ def markdown_to_html(md_content):
     return markdown.markdown(md_content)
 
 
+
 def predict(input, images = []):
   client = Client("https://roboflow-gemini.hf.space/--replicas/bkd57/")
   result = client.predict(
@@ -53,17 +54,17 @@ def generate_predictions(book_name, author, language_choice, detail_options=[]):
         query_template = detail_queries.get(option).format(book_name=book_name, author=author) + '. Answer in ' + language_choice[3:]
         try:
             response = predict(query_template)
-            details += f"\n\n**{option}**:\n{response['content']}"
+            details += f"\n\n**{option}**:\n{response}"
         except:
             time.sleep(2)
             try:
                 response = predict(query_template)
-                details += f"\n\n**{option}**:\n{response['content']}"
+                details += f"\n\n**{option}**:\n{response}"
             except:
                 pass
             
     summary = fetch_summary(book_name, author, language_choice[3:])
-    combined_summary = summary["content"] + details
+    combined_summary = summary + details
     try:
         telegraph_url = post_to_telegraph(f"Summary of {book_name} by {author}", combined_summary)
     except requests.exceptions.ConnectionError:
@@ -80,10 +81,9 @@ with gr.Blocks(title="📚 BookMindAI", theme=gr.themes.Base()).queue() as demo:
                 author_name_input = gr.Textbox(placeholder="Enter Author Name", label="Author Name")
                 language_input = gr.Dropdown(choices=languages, label="Language")
                 detail_options_input = gr.CheckboxGroup(choices=list(detail_queries.keys()), label="Details to Include", visible=True)
-                run_button_summarize = gr.Button(label="Run", visible=True)
+                run_button_summarize = gr.Button("Run", visible=True)
 
             with gr.Column():
-                book_cover_output = gr.Gallery(label="Book Cover", visible=True)
                 telegraph_link_output = gr.Markdown(label="View on Telegraph", visible=True)
         with gr.Row():
             summary_output = gr.Markdown(label="Parsed Content", visible=True)
@@ -109,7 +109,7 @@ with gr.Blocks(title="📚 BookMindAI", theme=gr.themes.Base()).queue() as demo:
         def chat_response(message, history):
             for i in range(len(message)):
                 response = predict(message)
-                yield response['content']
+                yield response
 
         chat_interface = gr.ChatInterface(chat_response, examples=chat_examples, title='Talk with Gemini PRO about any book.')