Update gradio_llm_example.py

gradio_llm_example.py

@@ -76,7 +76,7 @@ def upload_file(files_obj):
             choose_btn : gr.Button(value="Choose", visible=True)}
 
 
-def read_content(file_name):
+def read_content(content, file_name):
     print(file_name, type(file_name))
     temp_file_path = "./temp"
     file_path = os.path.join(temp_file_path, file_name)
@@ -84,22 +84,24 @@ def read_content(file_name):
         try:
             content = file.read()
             print(content)
-            return {error_box: gr.Textbox(value=f"File ready to be used. \n You can ask a question about the uploaded PDF document.", visible=True)}
+            return { content_var : str(content[:10]),
+                    error_box : gr.Textbox(value=f"File ready to be used. \n You can ask a question about the uploaded PDF document.", visible=True)}
         except Exception as e:
             print(f"Error occurred while writing the file: {e}")
-            return {error_box: gr.Textbox(value=f"Error occurred while writing the file: {e}", visible=True)}
+            return { content_var : str(content[:10]),
+                    error_box : gr.Textbox(value=f"Error occurred while writing the file: {e}", visible=True)}
 
 
-def respond(message, chat_history, 
-            language_choice, max_length, temperature,
+def my_model(message, chat_history, content_var,
+            language_choice, model_choice, max_length, temperature,
             num_return_sequences, top_p, no_repeat_ngram_size):
         #No LLM here, just respond with a random pre-made message
-        if content == "":
-            bot_message = f"j'ai {max_length}" + random.choice(["Tell me more about it", 
+        if content_var == "":
+            bot_message = f"Pas de context : {content_var}" + random.choice(["Tell me more about it", 
                                             "Cool, but I'm not interested", 
                                             "Hmmmm, ok then"]) 
         else:
-            bot_message = " j'ai besoin d'un modèle pour lire le {content[:3]}"
+            bot_message = f" Voici le context {content_var}"
         chat_history.append((message, bot_message))
         return "", chat_history
 
@@ -118,7 +120,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as gradioApp:
             intro_gr = gr.Markdown(intro)
 
             #  Upload several documents
-            content = ""
+            content_var = gr.State("")
             upload_button = gr.UploadButton("Browse files", label="Drag and drop your documents here", 
                                             size="lg", scale=0, min_width=100,
                                             file_types=["pdf"], file_count="multiple")
@@ -129,32 +131,35 @@ with gr.Blocks(theme=gr.themes.Soft()) as gradioApp:
 
             # Read only one document
             error_box = gr.Textbox(label="Reading files... ", visible=False)
-            choose_btn.click(read_content, inputs=uploaded_check, outputs=error_box)
+            choose_btn.click(read_content, inputs=[content_var, uploaded_check], outputs=[content_var, error_box])
 
             # Select advanced options
             gr.Markdown(""" ## Toolbox """)
             with gr.Accordion(label="Select advanced options",open=False):
+                model_choice = gr.Dropdown(["LLM", "Other"], label="Model", info="Choose your AI model")
                 language_choice = gr.Dropdown(["English", "French"], label="Language", info="Choose your language")
                 max_length = gr.Slider(label="Token length", minimum=1, maximum=100, value=50, step=1)
                 temperature= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=0.8, step=0.1)
-                num_return_sequences= gr.Slider(label="Temperature", minimum=0.1, maximum=50, value=1, step=0.1)
-                top_p= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=0.8, step=0.1)
-                no_repeat_ngram_size= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=3, step=0.1)
+                num_return_sequences= gr.Slider(label="Return Sequence", minimum=0.1, maximum=50, value=1, step=0.1)
+                top_p= gr.Slider(label="top p", minimum=0.1, maximum=1, value=0.8, step=0.1)
+                no_repeat_ngram_size= gr.Slider(label="repeat", minimum=0.1, maximum=1, value=3, step=0.1)
 
 
         # Chat
         with gr.Column(scale=2, min_width=600):
             title2_gr = gr.Markdown(title2)
-            chatbot = gr.Chatbot(label="Bot", height=500) 
+            chatbot = gr.Chatbot(label="Bot", height=500, queue = True)
             msg = gr.Textbox(label="User", placeholder="Ask any question.")
-            chatbot_btn = gr.Button("Submit")
-            chatbot_btn.click(respond, inputs=[msg, chatbot, 
-                                               language_choice, max_length, temperature,
-                                               num_return_sequences, top_p, no_repeat_ngram_size],
-                                               outputs=[msg, chatbot])
+            # chatbot_btn = gr.Button("Submit")
+            msg.submit(my_model, queue = True,
+                       inputs=[msg, chatbot, content_var,
+                                language_choice, model_choice, max_length, temperature,
+                                num_return_sequences, top_p, no_repeat_ngram_size],
+                                outputs=[msg, chatbot])
             clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")
 
 
 gr.close_all()
-gradioApp.launch(share=True, enable_queue=True, auth=("neovision", "gradio2023"))
+gradioApp.queue()
+gradioApp.launch(share=True, auth=("neovision", "gradio2023"))
 #auth=("neovision", "gradio2023")