Adding files for api integration

app.py

@@ -1,7 +1,5 @@
 import gradio as gr
-from text_converter import generate_similar_sentence
-from text_converter import user_input_readability_level
-from text_converter import reading_levels
+from text_converter import reading_levels, model_types, generate_similar_sentence, user_input_readability_level, set_reading_levels
 
 APP_DESCRIPTION = '''# Reading Level Converter
 <div id="content_align">Convert any text to a specified reading level while retaining the core text meaning</div>'''
@@ -10,19 +8,6 @@ MIN_ENTAILMENT = 0.5
 MAX_ITER = 5
 SYSTEM_PROMPT = "You are a writing assistant. You help convert complex texts to simpler texts while maintaining the core meaning of the text."
 
-# Dictionary mapping grade levels to reading ease scores
-"""
-reading_levels = {
-    "5th Grade (90-100)": (90, 100),
-    "6th Grade (80-90)": (80, 90),
-    "7th Grade (70-80)": (70, 80),
-    "8th - 9th Grade (60-70)": (60, 70),
-    "10th - 12th Grade (50-60)": (50, 60),
-    "College (30-50)": (30, 50),
-    "College Graduate + Professionals (0-30)": (0, 30)
-}
-"""
-
 def convert_text(input_text, grade_level, input_reading_score):
     min_level, max_level = reading_levels[grade_level]
     output_text, similarity, reading_level, message = generate_similar_sentence(input_text, min_level, max_level, MIN_ENTAILMENT, SYSTEM_PROMPT, MAX_ITER, float(input_reading_score))
@@ -32,6 +17,15 @@ with gr.Blocks(css='styles.css') as app:
     gr.Markdown(APP_DESCRIPTION)
 
     with gr.Tab("Reading Level Calculator"):
+        with gr.Row():
+            model_select = gr.Radio(choices=model_types, label="Readability Score Model", value=model_types[1], interactive=True, scale=2)
+            model_select_btn = gr.Button("Select Readability Score Model", scale=1)
+
+        model_select_btn.click(
+            fn = set_reading_levels,
+            inputs=[model_select]
+        )
+
         input_text = gr.Textbox(label="Input Text", placeholder="Type here...", lines=4)
 
         fetch_score_and_lvl_btn = gr.Button("Fetch Score and Level")
@@ -45,7 +39,7 @@ with gr.Blocks(css='styles.css') as app:
             outputs=[output_input_reading_score, output_input_reading_level]
         )
         
-        grade_level = gr.Radio(choices=list(reading_levels.keys()), label="Target Reading Level", value=list(reading_levels.keys())[0])
+        grade_level = gr.Radio(choices=list(reading_levels.keys()), label="Target Reading Level", value=list(reading_levels.keys())[0], interactive=True)
         
         output_reading_level = gr.Textbox(label="Output Reading Level", placeholder="Output Reading Level...", lines=1)
         output_similarity = gr.Textbox(label="Similarity", placeholder="Similarity Score...", lines=1)

arte_score.py

@@ -0,0 +1,21 @@
+import requests
+import json
+import os
+
+ARTE_API_KEY = os.environ.get("ARTE_API_KEY")
+ARTE_BASE_URL = os.environ.get("ARTE_BASE_URL")
+arte_api_endpoint="ping_api"
+
+def ping_api(text, model_type="SBERT"):
+  payload = {
+      "APIkey": ARTE_API_KEY,  # Replace with your actual API key
+      "selection": [model_type],
+      "text": text
+  }
+  json_payload = json.dumps(payload)
+  headers = {
+      'Content-Type': 'application/json'
+  }
+  response = requests.post(ARTE_BASE_URL + arte_api_endpoint, data=json_payload, headers=headers)
+  data = json.loads(response.text)
+  return data.get(model_type).get("sc")

requirements.txt

@@ -3,4 +3,5 @@ keras==2.15.0
 torch
 transformers
 textstat
-openai
+openai
+requests

text_converter.py

@@ -1,5 +1,6 @@
 from bert_similarity import get_similarity
 from text_generator import get_gpt_response
+from arte_score import ping_api
 from textstat import flesch_reading_ease
 
 def generate_user_prompt(prompt_type, base_text):
@@ -24,7 +25,10 @@ def generate_user_prompt(prompt_type, base_text):
 
     return prompts[prompt_type].format(base_text=base_text)
 
-reading_levels = {
+model_types = ["FRE", "SBERT"]
+model_type = model_types[1]
+
+fre_levels = {
     "5th Grade (90-100)": (90, 100),
     "6th Grade (80-90)": (80, 90),
     "7th Grade (70-80)": (70, 80),
@@ -35,11 +39,33 @@ reading_levels = {
     "Research + Nobel laureate ((-infinity)-(-1))": (-float('inf'), -1)
     }
 
+sbert_levels = {
+    "Difficult (-100 to -1.88)": (-100, -1.88),
+    "Somewhat Difficult (-1.87 to -1.21)": (-1.87, -1.21),
+    "Intermediate (-1.20 to -0.65)": (-1.20, -0.65),
+    "Somewhat Easy (-0.64 to -0.05)": (-0.64, -0.05),
+    "Easy (> -0.04)": (-0.04, 100)
+    }
+
+# Setting SBert to be default to avoid any issues.
+reading_levels = sbert_levels
+
 inverse_reading_levels = {v: k for k, v in reading_levels.items()}
 
+def set_reading_levels(level_type):
+    global reading_levels
+    global inverse_reading_levels
+    global model_type
+    if level_type == "FRE":
+        reading_levels = fre_levels
+    elif level_type == "SBERT":
+        reading_levels = sbert_levels
+    inverse_reading_levels = {v: k for k, v in reading_levels.items()}
+    model_type = level_type
+
 def user_input_readability_level(input_text):
-    current_score = flesch_reading_ease(input_text)
-    print(f'Reading score for user input is: {current_score}')
+    current_score = ping_api(input_text, model_type) #flesch_reading_ease(input_text)
+    print(f'Reading score for user input is: {current_score} for model type: {model_type}')
     current_level = ''
     for (min, max), level in inverse_reading_levels.items():
         if min <= current_score <= max:
@@ -85,7 +111,7 @@ def generate_similar_sentence(input_text, min_reading_level, max_reading_level,
             generated_texts.append(response)
 
             similarity = get_similarity(response, input_text)
-            reading_level = flesch_reading_ease(response)
+            reading_level = ping_api(response, model_type) #flesch_reading_ease(response)
 
             # We add the generated text's reading level to the list.
             generated_text_scores.append((similarity, reading_level))
@@ -108,6 +134,7 @@ def generate_similar_sentence(input_text, min_reading_level, max_reading_level,
             i += 1
 
         # Printing all generated texts to the console.
+        print(f"=============== Model Type: {model_type} ===============\n")
         for index, text in enumerate(generated_texts):
             print(f"=============== Iteration {index} ===============")
             print(f"Generated text: {text}\nSimilarity: {generated_text_scores[index][0]}\nReadability Score: {generated_text_scores[index][1]}\n")