# import gradio as gr # import os # from mistralai.client import MistralClient # from mistralai.models.chat_completion import ChatMessage # # Ensure the environment variable for the API key is set # api_key = os.getenv("MISTRAL_API_KEY") # if not api_key: # raise ValueError("MISTRAL_API_KEY environment variable not set") # model = "mistral-tiny" # client = MistralClient(api_key=api_key) # def generate_goals(input_var): # messages = [ # ChatMessage(role="user", content=f"Generate 10 specific, industry relevant goals for {input_var} using Python and Pandas. Each goal should include a brief name and a one-sentence description of the task or skill. Focus on practical applications in educational assessment, covering areas such as data processing, statistical analysis, visualization, and advanced techniques") # ] # try: # response = client.chat(model=model, messages=messages) # content = response.choices[0].message.content # return content # except Exception as e: # return f"An error occurred: {str(e)}" # # HTML content # html_content = """ # # # # # # Comprehensive Exam Data Analysis with Pandas - 30 Industry Goals with Connections # # # # #

Comprehensive Exam Data Analysis with Pandas - 30 Industry Goals with Connections

# # # # """ # # Gradio interface # iface = gr.Interface( # fn=generate_goals, # inputs=gr.Textbox(label="Goal Name"), # outputs=gr.Textbox(label="Generated Goals"), # title="Exam Data Analysis Goals Generator", # description="Click on a goal in the visualization to generate related goals.", # allow_flagging="never", # theme="default", # css=html_content # ) # if __name__ == "__main__": # iface.launch() from flask import Flask, request, jsonify, render_template_string import os from mistralai.client import MistralClient from mistralai.models.chat_completion import ChatMessage app = Flask(__name__) # Mistral AI setup api_key = os.getenv("MISTRAL_API_KEY") if not api_key: raise ValueError("MISTRAL_API_KEY environment variable not set") model = "mistral-tiny" client = MistralClient(api_key=api_key) def generate_goals(input_var): messages = [ ChatMessage(role="user", content=f"Generate 5 specific, industry relevant goals for {input_var} using Python and Pandas in exam data analysis. Each goal should include a brief name and a one-sentence description of the task or skill.") ] try: response = client.chat(model=model, messages=messages) return response.choices[0].message.content except Exception as e: return f"An error occurred: {str(e)}" html_content = """ Exam Data Analysis Goals Generator

Exam Data Analysis Goals Generator

""" @app.route('/') def index(): return render_template_string(html_content) @app.route('/generate_goals', methods=['POST']) def generate_goals_api(): input_var = request.json['input_var'] goals = generate_goals(input_var) return jsonify({'goals': goals}) if __name__ == "__main__": app.run(host='0.0.0.0', port=7860) # imp # from http.server import HTTPServer, SimpleHTTPRequestHandler # from pyngrok import ngrok # import os # from mistralai.client import MistralClient # from mistralai.models.chat_completion import ChatMessage # import json # # Mistral AI setup # api_key = os.getenv("MISTRAL_API_KEY") # if not api_key: # raise ValueError("MISTRAL_API_KEY environment variable not set") # model = "mistral-tiny" # client = MistralClient(api_key=api_key) # def generate_goals(input_var): # messages = [ # ChatMessage(role="user", content=f"Generate 5 specific, industry relevant goals for {input_var} using Python and Pandas in exam data analysis. Each goal should include a brief name and a one-sentence description of the task or skill.") # ] # try: # response = client.chat(model=model, messages=messages) # return response.choices[0].message.content # except Exception as e: # return f"An error occurred: {str(e)}" # html_content = """ # # # # # # Exam Data Analysis Goals Generator # # # # #

Exam Data Analysis Goals Generator

# # # # """ # class MyHandler(SimpleHTTPRequestHandler): # def do_GET(self): # self.send_response(200) # self.send_header('Content-type', 'text/html') # self.end_headers() # self.wfile.write(html_content.encode()) # def do_POST(self): # if self.path == '/generate_goals': # content_length = int(self.headers['Content-Length']) # post_data = self.rfile.read(content_length) # data = json.loads(post_data.decode('utf-8')) # input_var = data['input_var'] # goals = generate_goals(input_var) # self.send_response(200) # self.send_header('Content-type', 'application/json') # self.end_headers() # self.wfile.write(json.dumps({'goals': goals}).encode()) # else: # self.send_error(404) # if __name__ == '__main__': # port = 7860 # server = HTTPServer(('', port), MyHandler) # public_url = ngrok.connect(port).public_url # print(f" * ngrok tunnel \"{public_url}\" -> \"http://127.0.0.1:{port}\"") # server.serve_forever() # here # from http.server import HTTPServer, SimpleHTTPRequestHandler # import os # from mistralai.client import MistralClient # from mistralai.models.chat_completion import ChatMessage # import json # # Mistral AI setup # api_key = os.getenv("MISTRAL_API_KEY") # if not api_key: # raise ValueError("MISTRAL_API_KEY environment variable not set") # model = "mistral-tiny" # client = MistralClient(api_key=api_key) # def generate_goals(input_var): # messages = [ # ChatMessage(role="user", content=f"Generate 5 specific, industry relevant goals for {input_var} using Python and Pandas in exam data analysis. Each goal should include a brief name and a one-sentence description of the task or skill.") # ] # try: # response = client.chat(model=model, messages=messages) # return response.choices[0].message.content # except Exception as e: # return f"An error occurred: {str(e)}" # html_content = """ # # # # # # Exam Data Analysis Goals Generator # # # # #

Exam Data Analysis Goals Generator