Create app.py

app.py

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+# Import Libraries
+import gradio as gr
+import google.generativeai as genai
+
+
+
+# using the gemini model
+# api
+genai.configure(api_key="AIzaSyCmdsQeYMapI6l3maUpAkrE_GkT348w5UA")
+
+
+# gemini model initialization
+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
+
+# funtion: prompt to model, parse the response to the judger, and enable user editing judgment
+def model_prompt_response(prompt):
+# using the Gemini model
+
+    # pompting the model for response
+    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].'
+
+  
+    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]. '
+
+    # prompting the judger for judgment
+    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
+
+
+    # assigning scores
+    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
+
+# anonators judgement
+def judge_response(score1_judger, score2_judger, judge_1, judge_2):
+    # user judgemnet
+  score1_anonator = score1_judger
+  score2_anonator = score2_judger
+  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 assigned the response from the model and outputs the corresponding judgment.
+      #### The user asses the judgment and alters the response 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)
+  with gr.Row():
+    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")
+
+
+# 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")
+
+  response_model.click(model_prompt_response, inputs=prompt, outputs=[response1_model,
+                                                                      judge_1,
+                                                                      score1_judger,
+                                                                      response2_model,
+                                                                      judge_2,
+                                                                      score2_judger,
+                                                                      ])
+  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()
+
+# Team Members
+'''
+ Collins Okoth: collins.okech@digitaldividedata.com
+ Albert Disi: albert.disi@digitaldividedata.com
+ Joseph Amadi: joseph.amadi@digitaldividedata.com
+'''