Update app.py

app.py

@@ -1,15 +1,10 @@
-# Dependencies:
-# gradio==3.3.1
-# transformers==4.27.1
-# torch==2.0.1
-# pymupdf==1.21.1
-
 import gradio as gr
-from transformers import pipeline
+import openai
 import fitz  # PyMuPDF
+import time
 
-# Load a summarization model from Hugging Face
-summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+# Set your OpenAI API key
+openai.api_key = 'sk-proj-fCrObs9lnucfEFwJdMkHT3BlbkFJA7auo8szgjDuHz28QGBW'
 
 def extract_text_from_pdf(pdf_file):
     text = ""
@@ -25,7 +20,20 @@ def extract_text_from_pdf(pdf_file):
         return str(e)
     return text
 
+def generate_summary(text):
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a summarization assistant."},
+            {"role": "user", "content": f"Please summarize the following text: {text}"}
+        ]
+    )
+    summary = response['choices'][0]['message']['content'].strip()
+    return summary
+
 def evaluate_text_against_rubric(rubric_text, text):
+    start_time = time.time()
+    
     # Split rubric into criteria
     criteria = [criterion.strip() for criterion in rubric_text.split('\n') if criterion.strip()]
     
@@ -38,7 +46,7 @@ def evaluate_text_against_rubric(rubric_text, text):
     evaluations = {}
     for i, criterion in enumerate(criteria):
         try:
-            summary = summarizer(text, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
+            summary = generate_summary(text[:1000])
             evaluations[f'Criteria {i+1}'] = {
                 "Criterion": criterion,
                 "Score": 3,  # Dummy score for now
@@ -53,6 +61,9 @@ def evaluate_text_against_rubric(rubric_text, text):
                 "Example": ""
             }
     
+    end_time = time.time()
+    print(f"Evaluation took {end_time - start_time} seconds")
+    
     return evaluations
 
 def evaluate(rubric_pdf, rubric_text, text):
@@ -90,7 +101,7 @@ with gr.Blocks() as interface:
     
     rubric_pdf_input = gr.File(label="Upload Rubric PDF (optional)", type="binary")
     rubric_text_input = gr.Textbox(lines=10, placeholder="Or enter your rubric text here...", label="Rubric Text (optional)")
-    text_input = gr.Textbox(lines=10, placeholder="Paste the text to be evaluated here...", label="Text to Evaluate")
+    text_input = gr.Textbox(lines=10, placeholder="Paste the text to be evaluated here (max 2000 characters)...", label="Text to Evaluate", max_chars=2000)
     
     evaluate_button = gr.Button("Evaluate")
     
@@ -109,3 +120,4 @@ interface.launch()
 
 
 
+