fix bugs save caches

app.py

@@ -0,0 +1,67 @@
+import re
+import gradio as gr
+import os
+import asyncio
+from transformers import pipeline
+import time
+
+
+class TaskClassifier:
+    
+    def __init__(self):
+        self.classifier = pipeline("zero-shot-classification",
+                      model="facebook/bart-large-mnli")
+    
+    def __call__(self, client_input: str, task_types: str):
+        """Classify tasks for LLM-based gent"""
+        candidate_labels = [label.strip() for label in task_types.split(",")]
+        time_execution = time.time()
+        output = self.classifier(str(client_input), candidate_labels, multi_label=False)
+        # output = classifier(input, candidate_labels, multi_label=False)
+        time_execution = round(time.time() - time_execution, 2)
+        # return {"task_type": output['labels'][0], "confidence": round(output['scores'][0],2), "inference_time": time_execution}
+        return f"Task Type : {output['labels'][0]}\nScore : {round(output['scores'][0],2)}\nInference Time : {time_execution}"
+    
+
+
+
+def load_classifier(client_input, task_types):
+    global classifier
+    return classifier(client_input, task_types)
+
+
+def question_answer(client_input, task_types):
+    if client_input.strip()=='':
+        return '''[ERROR]: Please enter client input (e.g., 'Find the top products for a given category').'''
+    if task_types.strip() == '':
+        return '''[ERROR]: Please enter list of task type of LLM-based agents (e.g., 'Greeting, Information retrieval, Sentiment analysis, Text generation, Code generation, Q&A, Summarize'): '''
+    return load_classifier(client_input, task_types)
+
+
+classifier = TaskClassifier()
+
+title = 'Task Clarity for LLM-based Agents'
+description = """ Task Clarity for LLM-based Agents is a powerful tool that assists developers in crafting precise task instructions, identifies task types (e.g., Q&A, Text generation) for your LLM-based Agents."""
+
+
+with gr.Blocks() as demo:
+
+    gr.Markdown(f'<center><h1>{title}</h1></center>')
+    gr.Markdown(description)
+
+    with gr.Row():
+        
+        with gr.Group():
+            gr.Markdown(f'<p style="text-align:center">Report about the model: <a href="https://sinh-nguyen.notion.site/Report-Solving-Task-Clarity-for-LLM-based-Agents-4b49b5229a3f423984743b11f3c2bec8">here</a></p>')
+            client_input=gr.Textbox(label='''Please enter client's input (e.g., 'Hello?'): ''')
+            task_types = gr.Textbox(label='''Please enter list of task type of LLM-based agents (e.g., 'Greeting, Information retrieval, Sentiment analysis, Text generation, Code generation, Q&A, Summarization'): ''')
+            btn = gr.Button(value='Submit')
+            btn.style(full_width=True)
+#openai.api_key = os.getenv('Your_Key_Here') 
+        with gr.Group():
+            answer = gr.Textbox(label='The answer to your question is :')
+
+        btn.click(question_answer, inputs=[client_input, task_types], outputs=[answer])
+
+demo.launch(share=True)
+# demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

@@ -0,0 +1,8 @@
+huggingface_hub
+git+https://github.com/huggingface/transformers
+accelerate 
+peft 
+bitsandbytes
+trl 
+py7zr 
+gradio==3.42.0