up

model

gradio_llm_example.py

@@ -1,13 +1,22 @@
 import gradio as gr
-import numpy as np
-import random
 import time
+import random
 import os
 import shutil
-import codecs
 # How to RUN code ==> gradio gradio_llm_example.py 
 
 
+def load_llm_model(model: str = "google/flan-t5-large") -> HuggingFacePipeline:
+    llm = HuggingFacePipeline.from_model_id(
+        model_id=model,
+        task="text2text-generation",
+        model_kwargs={"max_length": 1500, "load_in_8bit": True},
+    )
+    return llm
+    
+from langchain import HuggingFacePipeline
+
+
 # Define text and title information 
 title1 = "##  </br> </br> </br> 🤗💬 QA App"
 
@@ -72,68 +81,99 @@ def upload_file(files_obj):
         file_name = os.path.basename(file_obj.name)
         file_name_list.append(file_name)
         shutil.copyfile(file_obj.name, os.path.join(temp_file_path, file_name))
-    return {uploaded_check : gr.Radio(choices=file_name_list, visible=True),
+    # return visible button for next selection 
+    return {uploaded_check : gr.CheckboxGroup(choices=file_name_list, visible=True),
             choose_btn : gr.Button(value="Choose", visible=True)}
 
 
-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)
-    with open(file_path, "rb") as file:
-        try:
-            content = file.read()
-            print(content)
-            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 { content_var : str(content[:10]),
-                    error_box : gr.Textbox(value=f"Error occurred while writing the file: {e}", visible=True)}
-
-
+def read_content(content, files_name):
+    """ Read and update the content variable (state) according to the several files_names to read from temp folder
+    return updated content_var (type : list of str)
+    return visible error_box to display logs error """
+    content_list = list()
+    text_list = list()
+    # Parse one or several docs among the selected ones
+    for file_name in files_name :
+        print(file_name, type(file_name))
+        temp_file_path = "./temp"
+        file_path = os.path.join(temp_file_path, file_name)
+        # Read doc
+        with open(file_path, "rb") as file:
+            try:
+                content = file.read()
+                #### YOUR FONCTION FOR CONTENT ==> must be str
+                my_content = str(content[:10])
+                content_list.append(my_content) 
+                text_list.append(f"File {file_name} ready to be used. \n")
+                print(content)
+            except Exception as e:
+                print(f"Error occurred while writing the file: {e}")
+                text_list.append(f"Error occurred while writing the file {file_name}: {e}")
+    return {content_var : content_list,
+            error_box : gr.Textbox(value=f"""{" and ".join(text_list)} \n You can ask a question about the uploaded PDF document.""", visible=True)}
+
+
+### YOUR model using the same inputand returning output 
 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_var == "":
+        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 = f" Voici le context {content_var}"
         chat_history.append((message, bot_message))
+
         return "", chat_history
 
 
+def queue_bot(history):
+    """ For in progress display during chat """
+    bot_message = history[-1][1]
+    history[-1][1] = ""
+    for character in bot_message:
+        history[-1][1] += character
+        time.sleep(0.05)
+        yield history
 
-# Layout
+
+# App
 with gr.Blocks(theme=gr.themes.Soft()) as gradioApp:
+
+    # Initialize the document context variable as empty without any drag and drop
+    content_var = gr.State([])
+
+    # Layout
     with gr.Row():
+        # Row 1 : About
         with gr.Column(scale=1, min_width=100):
-            logo_gr = gr.Markdown(""" <img src="file/logo_neovision.png" alt="logo" style="width:400px;"/>""") 
             # gr.Image("./logo_neovision.png")
+            logo_gr = gr.Markdown(""" <img src="file/logo_neovision.png" alt="logo" style="width:400px;"/>""") 
             about_gr = gr.Markdown(about)   
-            
+        
+        # Row 2 : Param
         with gr.Column(scale=2, min_width=500):
             title1_gr= gr.Markdown(title1)
             intro_gr = gr.Markdown(intro)
 
             #  Upload several documents
-            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")
-            uploaded_check = gr.Radio(label="Uploaded documents", visible=False,
+            # invisible button while no documents uploaded
+            uploaded_check = gr.CheckboxGroup(label="Uploaded documents", visible=False, 
                                       info="Do you want to use a supporting document?")
             choose_btn = gr.Button(value="Choose", visible=False)
+            # uploading one or several docs and display other buttons
             upload_button.upload(upload_file, upload_button, [uploaded_check, choose_btn])
 
             # Read only one document
-            error_box = gr.Textbox(label="Reading files... ", visible=False)
+            error_box = gr.Textbox(label="Reading files... ", visible=False) # display only when ready or error
             choose_btn.click(read_content, inputs=[content_var, uploaded_check], outputs=[content_var, error_box])
 
-            # Select advanced options
+            # Select advanced options, to be given as input for your model
             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")
@@ -145,21 +185,21 @@ with gr.Blocks(theme=gr.themes.Soft()) as gradioApp:
                 no_repeat_ngram_size= gr.Slider(label="repeat", minimum=0.1, maximum=1, value=3, step=0.1)
 
 
-        # Chat
+        # Row 3 : Chat
         with gr.Column(scale=2, min_width=600):
             title2_gr = gr.Markdown(title2)
-            chatbot = gr.Chatbot(label="Bot", height=500, queue = True)
+            chatbot = gr.Chatbot(label="Bot", height=500)
             msg = gr.Textbox(label="User", placeholder="Ask any question.")
-            # chatbot_btn = gr.Button("Submit")
-            msg.submit(my_model, queue = True,
+            ### YOUR MODEL TO ADAPT
+            msg.submit(my_model,
                        inputs=[msg, chatbot, content_var,
                                 language_choice, model_choice, max_length, temperature,
                                 num_return_sequences, top_p, no_repeat_ngram_size],
-                                outputs=[msg, chatbot])
+                                outputs=[msg, chatbot]).then(queue_bot, chatbot, chatbot)
             clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")
 
 
 gr.close_all()
 gradioApp.queue()
 gradioApp.launch(share=True, auth=("neovision", "gradio2023"))
-#auth=("neovision", "gradio2023")
+#auth=("neovision", "gradio2023") to be placed inside the launch parameters