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from flask import Flask, jsonify, request
from flask_cors import CORS
import google.generativeai as genai
from dotenv import load_dotenv
import os
import json
load_dotenv()
app = Flask(__name__)
CORS(app)
GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')
genai.configure(api_key=GOOGLE_API_KEY)
model = genai.GenerativeModel('gemini-pro')
user_id_ping = 0
user_chats = {}
@app.route('/')
def index():
return "Hello 👋."
@app.route('/tree', methods=["POST", "GET"])
def tree():
if request.method == 'POST':
data = request.get_json()
query = data.get('query')
response = model.generate_content('''I will give you a topic and you have to generate an explanation of the topic in points in hierarchical tree structure and respond with JSON structure as follows:
{
"name": "Java",
"children": [
{
"name": "Development Environment",
"children": [
{
"name": "Java Source Code",
"value": ".java files",
"description": "Human-readable code written with Java syntax."
},
{
"name": "Java Development Kit (JDK)",
"children": [
{
"name": "Compiler",
"value": "translates to bytecode",
"description": "Transforms Java source code into bytecode instructions understood by the JVM."
},
{
"name": "Java Class Library (JCL)",
"value": "predefined classes and functions",
"description": "Provides a collection of reusable code for common functionalities."
}
]
}
]
},
{
"name": "Execution",
"children": [
{
"name": "Java Runtime Environment (JRE)",
"children": [
{
"name": "Java Virtual Machine (JVM)",
"value": "executes bytecode",
"description": "Software program that interprets and executes bytecode instructions."
},
{
"name": "Class Loader",
"value": "loads bytecode into memory",
"description": "Loads .class files containing bytecode into JVM memory for execution."
}
]
},
{
"name": "Bytecode",
"value": ".class files (platform-independent)",
"description": "Machine-independent instructions generated by the compiler, executable on any system with JVM."
},
{
"name": "Just-In-Time (JIT) Compilation (optional)",
"value": "improves performance by translating bytecode to machine code",
"description": "Technique that translates frequently used bytecode sections into native machine code for faster execution."
}
]
},
{
"name": "Key Features",
"children": [
{
"name": "Object-Oriented Programming",
"value": "uses objects and classes",
"description": "Programs are structured around objects that encapsulate data and behavior."
},
{
"name": "Platform Independent (write once, run anywhere)",
"value": "bytecode runs on any system with JVM",
"description": "Java code can be compiled once and run on any platform with a JVM installed."
},
{
"name": "Garbage Collection",
"value": "automatic memory management",
"description": "JVM automatically reclaims memory from unused objects, simplifying memory management for developers."
}
]
}
]
}
Topic is: ''' + query)
# print(response.text)
return jsonify({'success': True, 'data': response.text})
# return temp
@app.route('/interview', methods=["POST", "GET"])
def interview():
if request.method == 'POST':
data = request.get_json()
print(data)
if data.get('from') == 'client':
user_id = data.get('user_id')
request_type = data.get('type')
if request_type == 1: # Initialize Questionarrie.
chat = model.start_chat(history=[])
user_chats[user_id] = chat
user_chats[user_id].processed = False
position = data.get('position')
difficulty_level = data.get('difficulty_level')
company_name = data.get('company_name')
response = chat.send_message('''You are a Interviewer. I am providing you with the the position for which the inerview is, difficulty level of the interview to be conducted, Company name.
You need to generate atmost 3 interview questions one after another.
The questions may consists of writing a small code along with text as well.
Now generate first question in following JSON format:
{
"question": "What is ...?"
}
I will respond to the question in the following JSON format:
{
"text_answer": "answer ...",
"code": "if any...."
}
Now after evaluating the answers you need to respond in the following JSON format:
{
"next_question": "What is ...?",
"text_correctness": "Test the correctness of text and return a range from 1 to 5 of correctness of text.",
"text_suggestions": "Some suggestions regarding the text_answer...."
"code_correctness": "Test the correctness of code and return a range from 1 to 5 of correctness of code",
"code_suggestions": "Any suggestions or optimizations to the code...",
}
At the end of the interview if no Questions are required then respond in the following format:
{
"text_correctness": "Test the correctness of text and return a range from 1 to 5 of correctness of text.",
"text_suggestions": "Some suggestions regarding the text_answer...."
"code_correctness": "Test the correctness of code and return a range from 1 to 5 of correctness of code",
"code_suggestions": "Any suggestions or optimizations to the code...",
"end": "No more questions. Thanks for your time!"
}
Position : '''+ position + '''
Difficullty Level : '''+ difficulty_level + '''
Company Interview : ''' + company_name)
print(response.text)
return jsonify({'success': True, 'data': response.text})
if request_type == 2:
text_data = data.get('text_data')
code = data.get('code')
chat = user_chats[user_id]
response = chat.send_message('''{"text_answer": "''' + text_data + '''", "code": "''' + code + '''"}''')
print(response.text)
json_text = json.loads(response.text)
try:
if json_text['end']:
user_id_ping = user_id
return jsonify({'success': True, 'data': response.text, 'end': True})
except Exception as e:
print(e)
return jsonify({'success': True, 'data': response.text, 'end': False})
elif data.get('from') == 'gradio':
print(data)
user_id = data.get('user_id')
user_chats[user_id].processed = True
user_chats[user_id].results = {'total_video_emotions': data.get('total_video_emotions'), 'emotions_final': data.get('emotions_final'), 'body_language': data.get('body_language'), 'distraction_rate': data.get('distraction_rate'), 'formatted_response': data.get('formatted_response'), 'total_transcript_sentiment': data.get('total_transcript_sentiment')}
return "Success"
@app.route('/result', methods=['POST', 'GET'])
def result():
if request.method == 'POST':
data = request.get_json()
user_id = data.get('user_id')
if user_chats[user_id].processed:
avg_text = 0
avg_code = 0
count = 0
for i, ele in enumerate(user_chats[user_id].history):
if i == 0:
continue
if ele['role'] == 'model':
temp = json.loads(ele['parts'][0]['text'])
if 'question' in temp.keys():
continue
elif 'next_question' in temp.keys() or 'end' in temp.keys():
count += 1
avg_text += temp['text_correctness']
avg_code += temp['code_correctness']
print(json.loads(ele['parts'][0]['text']), end='\n\n')
avg_text /= count
avg_code /= count
return jsonify({'success': True, 'avgText': avg_text, 'avgCode': avg_code, 'chatHistory': user_chats[user_id].history})
else:
return jsonify({'processing': True})
@app.route('/useridping', methods=['GET'])
def useridping():
if request.method == 'GET':
return jsonify(user_id_ping)
if __name__ == '__main__':
app.run() |