Test

app.py

@@ -1,46 +1,36 @@
 import gradio as gr
-from transformers import pipeline
-import argparse
 from run_llm import main
 
-# Initialize the GPT-2 pipeline
-pipe = pipeline("text-generation", model="gpt2")
-
-theme = gr.themes.Soft()
-
-
-
-
-# Function that generates text based on instruction-based prompting
-#def generate_text(input_instruction):
-#    # Use the input instruction to generate text
-#    generated_text = pipe(input_instruction, max_length=500)[0]['generated_text']
-#    return generated_text
-
-# Create a function that takes 3 inputs:
-# - A prompt which will be a random string
-# - From the first dropdown select the task (1,2,3)
-# - From the second dropdown select the model type
-# use run_llm.py to feed the models and then output 3 results in 3 output boxes, one for each strategy (strategy 1, 2 and 3)
-
-def generate_text(prompt, task_number, model_type):
-    generated_text = pipe(prompt, max_length=500)[0]['generated_text']
-    return generated_text
-
+def run_llm_interface(model, task, sentence):
+    args = argparse.Namespace(
+        model_path=model,
+        prompt=task,
+        start=0,
+        end=1  # Set to 1 to process a single sentence
+    )
+    main(args)
+    
+    # Read the outputs from the result files
+    with open(f'result/prompt1_qa/{model}/ptb/per_ent/NOUN/0.txt', 'r') as f:
+        output_1 = f.read()
+    
+    with open(f'result/prompt2_instruction/chunking/{model}/ptb/0.txt', 'r') as f:
+        output_2 = f.read()
+    
+    with open(f'result/prompt3_structured_prompt/chunking/{model}/ptb/0.txt', 'r') as f:
+        output_3 = f.read()
+
+    return {"output_1": output_1, "output_2": output_2, "output_3": output_3}
 
 # Define example instructions for testing
 instruction_examples = [
-    ["Describe the origin of the universe"],
-    ["Explain the concept of artificial intelligence"],
-    ["Describe the most common types of cancer"],
+    ["gpt3.5", "POS Tagging", "Describe the origin of the universe"],
+    ["vicuna-7b", "Chunking", "Explain the concept of artificial intelligence"],
+    ["fastchat-t5", "Parsing", "Describe the most common types of cancer"],
 ]
 
-# Function that echoes the input text
-#def echo_text(input_text):
-#    return input_text
-
 with gr.Interface(
-    fn=generate_text,
+    fn=run_llm_interface,
     inputs=[
         gr.Dropdown(['gpt3.5', 'vicuna-7b', 'vicuna-13b', 'fastchat-t5', 'llama-7b', 'llama-13b', 'llama-30b', 'alpaca'], label="Select Model", default='gpt3.5', key="model"),
         gr.Dropdown(['POS Tagging', 'Chunking', 'Parsing'], label="Select Task", default='POS Tagging', key="task"),