Update app.py

app.py

@@ -1,205 +1,68 @@
-##################################### Imports ######################################
-# Generic imports
-import spaces
-import gradio as gr
-import json
-
-# Specialized imports
-#from utilities.modeling import modeling
+###########################
+# UI for Meeting RAG Q&A. #
+###########################
 
-# server import
-from server import submit_weights #, train_model, submit_weights
+##################### Imports #####################
+import gradio as gr
+from utilities.setup import get_files
+from server import EmbeddingService, QAService
 
 
-# Module imports
-from utilities.setup import get_files
-from utilities.templates import prompt_template
+#################### Functions ####################
+def process_transcripts(files):
+    with EmbeddingService() as e:
+        e.run(files)
+    # some way to wait or a progress bar?
+    return 0
 
-########################### Global objects and functions ###########################
 
-conf = get_files.json_cfg()
+def retrieve_answer(question):
+    with QAService() as q:
+        q.run(question)
+        answer = retriever.answer()
+    return answer
 
-class update_visibility:
 
-    def textbox_vis(radio):
-        value = radio
-        if value == "Hugging Face Hub Dataset":
-            return gr.Dropdown(visible=bool(1))
-        else:
-            return gr.Dropdown(visible=bool(0))
+##################### Process #####################
+def main(conf):
+    with gr.Blocks() as demo:
     
-    def textbox_button_vis(radio):
-        value = radio
-        if value == "Hugging Face Hub Dataset":
-            return gr.Button(visible=bool(1))
-        else:
-            return gr.Button(visible=bool(0))
+        # Main page
+        with gr.TabItem(conf["layout"]["page_names"][0]):
+            gr.Markdown(get_files.load_markdown_file(conf["layout"]["About"]))
     
-    def upload_vis(radio):
-        value = radio
-        if value == "Upload Your Own":
-            return gr.UploadButton(visible=bool(1)) #make it visible
-        else:
-            return gr.UploadButton(visible=bool(0))
-@spaces.GPU
-def train(model_name, 
-          inject_prompt, 
-          dataset_predefined,
-          peft,
-          sft,
-          max_seq_length,
-          random_seed,
-          num_epochs,
-          max_steps,
-          data_field,
-          repository,
-          model_out_name):
-    """The model call"""
-
-    # Get models
-    # trainer = modeling(model_name, max_seq_length, random_seed,
-    #                    peft, sft, dataset, data_field)
-    # trainer_stats = trainer.train()
-
-    # Return outputs of training.
     
-    return f"Hello!! Using model: {model_name} with template: {inject_prompt}"
-
-
-
-##################################### App UI #######################################
-
-
-
-def main():
-    with gr.Blocks() as demo:
-
-        with gr.Tabs():
-            with gr.TabItem("About"):
-                # About page!!
-                gr.Markdown(get_files.load_markdown_file("README.md"))
-
-            with gr.TabItem("Basic Setup"):
-                gr.Markdown("# Select Model and Input details")
-                # Select Model
-                modelnames = conf['model']['choices']
-                model_name = gr.Dropdown(label="Supported Models", 
-                                         choices=modelnames, 
-                                         value=modelnames[0])
-                # Select Generic Model parameters
-                repository = gr.Textbox(label="Your User Name",
-                                        value=conf['model']['general']["repository"])   
-                model_out_name = gr.Textbox(label="Your Model Output Name",
-                                        value=conf['model']['general']["model_name"])
-                hf_token = gr.Textbox(label="Your Huggingface Token",
-                                      type='password',
-                                      value='')
-                
-
-            with gr.TabItem("Upload Data"):
-                # Toggle dataset load types
-                gr.Markdown("# Dataset Selection and Upload")
-                
-                dataset_choice = gr.Radio(label="Choose Dataset", 
-                                          choices=["Hugging Face Hub Dataset", "Upload Your Own"], 
-                                          value="Hugging Face Hub Dataset")
-                dataset_predefined = gr.Textbox(label="Hugging Face Hub Training Dataset", 
-                                                value='yahma/alpaca-cleaned', 
-                                                visible=True)
-                dataset_predefined_load = gr.Button("Upload Dataset (.csv, .jsonl, or .txt)")
-        
-                dataset_uploaded_load = gr.UploadButton(label="Upload Dataset (.csv, .jsonl, or .txt)", 
-                                                 file_types=[".csv",".jsonl", ".txt"], 
-                                                 visible=False)
-                # Safety output to show if upload succeeded.
-                data_snippet = gr.Markdown()
-
-                # Visibility toggler
-                dataset_choice.change(update_visibility.textbox_vis, 
-                                      dataset_choice, 
-                                      dataset_predefined)
-                dataset_choice.change(update_visibility.upload_vis, 
-                                      dataset_choice,
-                                      dataset_uploaded_load)
-                dataset_choice.change(update_visibility.textbox_button_vis,
-                                      dataset_choice,
-                                      dataset_predefined_load)
-                # Prompt template
-                inject_prompt = gr.Textbox(label="Prompt Template", 
-                                             value=prompt_template())
-                # Dataset buttons
-                dataset_predefined_load.click(fn=get_files.predefined_dataset,
-                                          inputs=dataset_predefined,
-                                          outputs=data_snippet)
-        
-                dataset_uploaded_load.click(fn=get_files.uploaded_dataset,
-                                         inputs=dataset_uploaded_load,
-                                         outputs=data_snippet)
-         
-            
-            with gr.TabItem("Train Model"):
-                ##### Model Parameter Inputs #####
-                gr.Markdown("# Model Parameter Selection")
-
-                # Parameters
-                data_field = gr.Textbox(label="Dataset Training Field Name",
-                                        value=conf['model']['general']["dataset_text_field"])
-                max_seq_length = gr.Textbox(label="Maximum sequence length", 
-                                             value=conf['model']['general']["max_seq_length"])
-                random_seed = gr.Textbox(label="Seed",
-                                        value=conf['model']['general']["seed"])
-                num_epochs = gr.Textbox(label="Training Epochs",
-                                        value=conf['model']['general']["num_train_epochs"])
-                max_steps = gr.Textbox(label="Maximum steps",
-                                        value=conf['model']['general']["max_steps"])    
-        
-                # Hyperparameters (allow selection, but hide in accordion.)
-                with gr.Accordion("Advanced Tuning", open=False):
-        
-                    sftparams = conf['model']['general']
-                    # accordion container content
-                    dict_string = json.dumps(dict(conf['model']['peft']), indent=4)
-                    peft = gr.Textbox(label="PEFT Parameters (json)", value=dict_string)
-                    
-                    dict_string = json.dumps(dict(conf['model']['sft']), indent=4)
-                    sft = gr.Textbox(label="SFT Parameters (json)", value=dict_string)            
-                
-                ##### Execution #####
-            
-                # Setup buttons
-                tune_btn = gr.Button("Start Fine Tuning")
-                gr.Markdown("### Model Progress")
-                # Text output (for now)
-                output = gr.Textbox(label="Output") 
-                
-                
-                # Data retrieval
-                
-                
-                # Execute buttons
-                tune_btn.click(fn=train, 
-                               inputs=[model_name, 
-                                       inject_prompt, 
-                                       dataset_predefined,
-                                       peft,
-                                       sft,
-                                       max_seq_length,
-                                       random_seed,
-                                       num_epochs,
-                                       max_steps,
-                                       data_field,
-                                       repository,
-                                       model_out_name
-                                      ],
-                               outputs=output)
-                # stop button
         
-                # submit button
-        
-        # Launch baby
+        # User config page
+        with gr.TabItem(conf["layout"]["page_names"][1]):
+            gr.Markdown("# Upload Transcript and Necessary Context")
+            gr.Markdown("Please wait as the transcript is being processed.")
+            load_file = gr.UploadButton(label="Upload Transcript (.vtt)", 
+                                                     file_types=[".vtt"])
+            goals = gr.Textbox(label="Goals for the Meeting",
+                                            value=conf["defaults"]["goals"]) # not incorporated yet. Will be with Q&A.
+            repository = gr.Textbox(label="Blank", visible=False) # since there is no output.
+            upload_button.upload(process_transcripts, load_file, repository)
+    
+    
+    
+        # Meeting Question & Answer Page
+        with gr.TabItem(conf["layout"]["page_names"][2]):
+            question = gr.Textbox(label="Ask a Question",
+                                  value=conf["default"]["question"])
+            ask_button = gr.Button("Ask!")
+            model_output = gr.components.Textbox(label="Answer")
+            dataset_predefined_load.click(fn=retrieve_answer,
+                                              inputs=question,
+                                              outputs=model_output)
+    
+    
+    
         demo.launch()
 
-##################################### Launch #######################################
 
+##################### Execute #####################
 if __name__ == "__main__":
-    main()
+    # Get config
+    conf = get_files.json_cfg()
+    main(conf)