Create app.py

app.py

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+import os
+
+os.environ['GOOGLE_API_KEY'] = "AIzaSyA5LzhPqWiUEEms-AnlYqI75C8YhggrMkw"
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+import openai
+os.environ["OPENAI_API_KEY"] = "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"
+openai.api_key =  "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"
+
+def get_completion_ai(prompt, model="gpt-3.5-turbo"):
+  messages = [{"role": "user", "content": prompt}]
+  response = openai.ChatCompletion.create(
+      model=model,
+      messages=messages,
+      temperature=0, # this is the degree of randomness of the model's output
+  )
+  return response.choices[0].message["content"] 
+
+def evaluate_answer(question,answer,context):
+  prompt_sum = f'''Given the {context}, check whether the answer {answer} is the correct answer to the question {question}. 
+  Here are the steps:
+  1.Extract the {question} and its answer from the provided context
+  2.Now compare the answer in the context with the {answer} provided by the user
+  3.If the answer is correct, 
+  tell the user that he did a good job. If the answer is wrong, use the same tone to come up with the explanation.'''
+  response = get_completion_ai(prompt_sum)
+  return response
+
+    
+
+gemini_model = ChatGoogleGenerativeAI(model = "gemini-pro")
+def generate_multiple_questions(topic):
+  prompt_sum = f'''Given the {topic}, generate atleast 10 multiple choice questions that test the users understanding of the topic. Here is the format:
+  
+  Question: 
+  Four Multiple choice options:
+  Correct answer:
+  
+  Here is an example: 
+Question: Which of the following statements is NOT a consequence of Newton's third law?
+Options:
+  A. For every action, there is an equal and opposite reaction.
+  B. The total momentum of a closed system remains constant.
+  C. The force of gravity between two objects is directly proportional to their masses.
+  D. The force of friction is always in the direction opposite to the motion of the object.
+Correct Answer: The force of gravity between two objects is directly proportional to their masses'''
+  response = get_completion(prompt_sum)
+  return response
+
+def generate_questions(topic):
+   # mcqs = qna_engine.generate_mcq(topic = topic,
+    #                           level = "Advanced",
+     #                          num = 10,
+      #                         llm = gemini_model
+      #                         )
+    mcqs = generate_multiple_questions(topic)
+    print(f"Context: {mcqs}")
+    question = extract_questions(mcqs)
+    return question, mcqs
+def get_completion(text):
+
+  model = genai.GenerativeModel('gemini-pro')
+  response = model.generate_content(text)
+  return response.text 
+
+def extract_questions(mcq):
+  prompt_sum = f'''Given questions {mcq}, select one question and present to the user with the options. Do not show the correct answer.'''
+  response = get_completion(prompt_sum)
+  return response
+
+import gradio as gr 
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Agent Socrates: Evaluate your understanding of a topic 🔥")
+    caption = gr.Textbox(label="Enter a topic of your choice ")
+    btn_caption = gr.Button("Generate a question")
+    questions = gr.Textbox(label="Here is the question. Now answer")
+    context = gr.Textbox(label="Here is the context", visible=False)
+    btn_caption.click(fn=generate_questions, inputs=caption, outputs=[questions,context])
+    print (context)
+    #audio_input = [gr.Audio(source="microphone", type="filepath", label="Start speaking"),caption]
+    
+    answer = gr.Textbox(label = "Enter your answer")
+
+    btn_submit = gr.Button("Evaluate your response")
+    evaluation_report = gr.Textbox(label="Your evaluation report would show up here")
+    btn_submit.click(fn=evaluate_answer, inputs=[questions, answer, context], outputs=[evaluation_report])
+
+    
+gr.close_all()
+#demo.launch(share=True, debug=True)
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(debug=True)