Update app.py

app.py

@@ -2,6 +2,7 @@ import torch
 import spaces
 
 import gradio as gr
+import pandas as pd
 from threading import Thread
 import re
 import time
@@ -69,6 +70,7 @@ asr_pl = pipeline(
 application_title = "Enlight Innovations Limited -- Demo"
 application_description = "This demo is designed to illustrate our basic ideas and feasibility in implementation."
 
+# Chatbot Interface functions
 @spaces.GPU
 def respond(
     message,
@@ -107,7 +109,7 @@ def respond(
         response += token
         yield response
 
-
+# Transcribe Interface functions
 @spaces.GPU
 def transcribe(asr_inputs, task):
     #print("Type: " + str(type(asr_inputs)))
@@ -117,30 +119,69 @@ def transcribe(asr_inputs, task):
     text = asr_pl(asr_inputs, batch_size=ASR_BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
     return text.strip()
 
+# Profile Interface functions
+def load_profiles():
+    try:
+        return pd.read_csv("temp_profiles.csv")
+    except FileNotFoundError:
+        return pd.DataFrame()
+
+def save_profile(profile_data):
+    df = load_profiles()
+    df = df.append(profile_data, ignore_index=True)
+    df.to_csv("temp_profiles.csv", index=False)
+
+def lookup_profile():
+    df = load_profiles()
+    assessment_id = assessment_id_input.value.strip()
+    if not assessment_id:
+        status.update("Please enter an Assessment ID", color="red")
+        return
+    
+    results = df[df["Assessment_ID"].str.contains(assessment_id, case=False)]
+    if results.empty:
+        status.update("No profiles found for this ID", color="red")
+        return
+    
+    profile_preview.update(
+        value=results.to_markdown(index=False)
+    )
+    status.update("Profile(s) found!", color="green")
 
 """Gradio User Interface"""
 #audio_input = gr.Audio(sources="upload", type="filepath", label="Audio: from file") #gr.Audio(sources="microphone", type="filepath", label="Audio: from microphone")
 #audio_input_choice = gr.Radio(["audio file", "microphone"], label="Audio Input Source", value="audio file") #
 
-# (transcribe) Interface components
+# Profile Interface components
+with gr.Blocks() as profile_interface:
+    # Profile Lookup Section
+    with gr.Column():
+        assessment_id_input = gr.Textbox(
+            label="Assessment Object/Session ID",
+            placeholder="Enter ID here...",
+            required=True
+        )
+        lookup_btn = gr.Button("Lookup Profile", variant="primary")
+        clear_btn = gr.Button("Clear Results", variant="secondary")
+        status = gr.Status(elem_classes="status-container")
+        profile_preview = gr.Markdown(label="Profile Results", value="")
+    
+    # Event Bindings
+    lookup_btn.click(lookup_profile, None, None)
+    clear_btn.click(lambda: profile_preview.update(""), status.update("", color=""))
+    assessment_id_input.change(lambda: status.update("", color=""), None, None)
+
+    # Load existing profiles on startup
+    load_profiles()
+    
+# Profile Interface
+
+# Transcribe Interface components
 audio_input = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Audio Input Source")
 task_input_choice = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
 task_output = gr.Textbox(label="Transcribed Output")
 
-# ChatInterface components
-chatbot_main = gr.Chatbot(label="Extraction Output")
-chatbot_main_input = gr.MultimodalTextbox({"text": "Choose the referred material(s) and ask your question.", "files":[]})
-chatbot_sys_output = gr.Textbox(value="You are a friendly Chatbot.", label="System Message")
-chatbot_max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max. New Tokens")
-chatbot_temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature")
-chatbot_top_p = gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        )
-
+# Transcribe Interface
 transcribe_interface = gr.Interface(
     fn=transcribe,
     inputs=[
@@ -156,6 +197,19 @@ transcribe_interface = gr.Interface(
     allow_flagging="never",
 )
 
+# ChatInterface components
+chatbot_main = gr.Chatbot(label="Extraction Output")
+chatbot_main_input = gr.MultimodalTextbox({"text": "Choose the referred material(s) and ask your question.", "files":[]})
+chatbot_sys_output = gr.Textbox(value="You are a friendly Chatbot.", label="System Message")
+chatbot_max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max. New Tokens")
+chatbot_temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature")
+chatbot_top_p = gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        )
 
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
@@ -177,7 +231,7 @@ chat_interface = gr.ChatInterface(
 )
 
 with gr.Blocks() as demo:
-    gr.TabbedInterface([transcribe_interface, chat_interface], ["Step 1: Transcribe", "Step 2: Extract"])
+    gr.TabbedInterface([profile_interface, transcribe_interface, chat_interface], ["Step 0: Profile", "Step 1: Transcribe", "Step 2: Extract"])
 
     """
     def clear_audio_input():