Update app.py

app.py

@@ -1,70 +1,91 @@
 # Import Libraries
 import gradio as gr
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-import pandas as pd
-import numpy as np
-import random
+import google.generativeai as genai
 
 
 
-# Load pre-trained model and tokenizer
-model_name = "gpt2"
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-model = GPT2LMHeadModel.from_pretrained(model_name)
+# using the gemini model
+# api
+genai.configure(api_key="AIzaSyCmdsQeYMapI6l3maUpAkrE_GkT348w5UA")
 
+# model_1 = genai.GenerativeModel('gemini-pro')
+# model_2 = genai.GenerativeModel('gemini-pro')
 
+model = genai.GenerativeModel('gemini-pro')
+
+# assign score function
+def assign_score(response):
+    if 'score=1' in response:
+        score = 1
+    elif 'score=2' in response:
+        score = 2
+    elif 'score=3' in response:
+        score = 3
+    elif 'score=4' in response:
+        score = 4
+    elif 'score=5' in response:
+        score = 5
+    else:
+        score = 0
+    return score
 
 def model_prompt_response(prompt):
-  score1_judger = random.randint(1,5)
-  score2_judger = random.randint(1,5)
+# using the gemini model
   
-  # Tokenize the prompt
-  input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    prompt_response_1 = model.generate_content(prompt + "in 3 sentences or less")
+    prompt_response_2 = model.generate_content(prompt + "in 3 sentences or less")
+    response1_model = prompt_response_1.text
+    response2_model = prompt_response_2.text
+
+    prompt_to_judge_1 = f'Given the question, {prompt} and answer {response1_model}, decide whether: score = 1 when answer is strongly irrelevant to the question, score = 2 when the answer is slitely relevant, score = 3 when the answer is relevant, score = 4 when it is slitely relevant and score = 5 for strongly relevant answer. Give reason for your answer and start with scores and write the score as [score=1], [score=2], [score=3], [score=4], or [score=5].'
 
-  # Generate response using beam search
-  response1_model_out = model.generate(input_ids, max_length=100,
-                          num_return_sequences=4, no_repeat_ngram_size=2, num_beams=5)
   
+    prompt_to_judge_2 = f'Given the question, {prompt} and answer {response2_model}, decide whether: score = 1 when answer is strongly irrelevant to the question, score = 2 when the answer is slitely relevant, score = 3 when the answer is relevant, score = 4 when it is slitely relevant and score = 5 for strongly relevant answer. Give reason for your answer and start with scores and write the score as [score=1], [score=2], [score=3], [score=4], or [score=5]. '
+
+    judge_1_response_1 = model.generate_content(prompt_to_judge_1)
+    judge_2_response_2 = model.generate_content(prompt_to_judge_2)
+    judge_1 = judge_1_response_1.text
+    judge_2 = judge_2_response_2.text
 
-  responses = []
-  for i, out in enumerate(response1_model_out):
-    response_out = tokenizer.decode(out, skip_special_tokens=True)
-    responses.append(response_out)
 
-  response1_model = responses[0]
-  response2_model = responses[1]
-  judge_1 = responses[2]
-  judge_2 = responses[3]
+    score1_judger = assign_score(judge_1)
+    score2_judger = assign_score(judge_2) 
+
 
   # return values
 
-  return response1_model, judge_1, score1_judger, response2_model, judge_2, score2_judger
+    return response1_model, judge_1, score1_judger, response2_model, judge_2, score2_judger
+
+
 
 # anonators judgement
-def judge_response(score1_judger, score2_judger):
+def judge_response(score1_judger, score2_judger, judge_1, judge_2):
   score1_anonator = score1_judger
   score2_anonator = score2_judger
-  response1_judger = f'This is a representation for response 1'
-  response2_judger = f'This is a representation for response 2'
+  response1_judger = judge_1
+  response2_judger = judge_2
 
   return response1_judger, score1_anonator, response2_judger, score2_anonator
 
 demo = gr.Blocks(theme=gr.themes.Monochrome(), title='Group1_Subtask2')
 
+
+
+
 with demo:
 
   gr.Markdown(
       """
       # Group1_Subtask2
-      ## The Model takes a request from the user and returns a response. 
-      #### A judger is asigned the response from the model and outputs corresponding judgement. 
+      ## The Model takes a request from the user and returns a response.
+      #### A judger is asigned the response from the model and outputs corresponding judgement.
       #### The user asses the judgement and alters the responds to correspond to their views or objective truth
       """
   )
   prompt = gr.Textbox(label='Question to the model')
   with gr.Row():
     response1_model = gr.Textbox(label='Model Response 1')
-  
+
     judge_1 = gr.Textbox(label='Judger Model Response 1/User Editable', interactive=True)
 
     score1_judger = gr.Number(value=2, label='Judger Model Score 1/User Editable', interactive=True)
@@ -72,16 +93,21 @@ with demo:
     response2_model = gr.Textbox(label='Model Response 2')
     judge_2 = gr.Textbox(label='Judger Model Response 2/User Editable', interactive=True)
     score2_judger = gr.Number(value=2, label='Judger Model Score 2/User Editable', interactive=True)
-  
+
 
   response_model = gr.Button("Model response")
 
-#
 
-  response1_judger = gr.Textbox(label='Corrected Response 1')
-  score1_anonator = gr.Number(label='Corrected score 1')
-  response2_judger = gr.Textbox(label='Corrected Response 2')
-  score2_anonator = gr.Number(label='Corrected Score 2')
+
+
+
+# user/judger edited response
+  with gr.Row():
+    response1_judger = gr.Textbox(label='Corrected Response 1')
+    score1_anonator = gr.Number(label='Corrected score 1')
+  
+    response2_judger = gr.Textbox(label='Corrected Response 2')
+    score2_anonator = gr.Number(label='Corrected Score 2')
 
 
   judge_model = gr.Button("Judge-Annonator")
@@ -93,13 +119,16 @@ with demo:
                                                                       judge_2,
                                                                       score2_judger,
                                                                       ])
-  judge_model.click(judge_response, inputs=[score1_judger, score2_judger] , outputs=[response1_judger, score1_anonator, response2_judger, score2_anonator])
+  judge_model.click(judge_response, inputs=[score1_judger, score2_judger, judge_1, judge_2] , outputs=[response1_judger, score1_anonator, response2_judger, score2_anonator])
+
+
 
-demo.launch()
+
+demo.launch(share=True)
 
 # Team Members
 '''
  Collins Okoth: collins.okech@digitaldividedata.com
  Albert Disi: albert.disi@digitaldividedata.com
  Joseph Amadi: joseph.amadi@digitaldividedata.com
-'''
+'''