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import os
import re
import time
import base64
from openai import OpenAI # access the open router api
from together import Together
from PIL import Image
import io
def math_solution_openrouter(api_key, prob_text, history=None):
if not api_key.strip():
return "please enter your OpenRouter Key.",history
if not prob_text.strip():
return "please enter a math problem so that I can solve it for you",
try:
client= OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=api_key,
)
messages= [
{ "role": "system", "content":
"""You are a very smart math tutor who can able to solve math and give proper explanations clearly and in a precise manner.
You can analyze the math problem given to you and provide the details solution step by step.
For every step:
1. Show all the mathematical explanations.
2. Explain each step and its necessity.
3. Connect it to the relevant mathematical concept.
Format your response with clear section of headers using markdown and comment if necessary.
Begin with a section of analysis named "Initail step" and followed number of step and conclude with "Final step" section.
"""},
]
if history:
for exchange in history:
messages.append({"role":"user","content":exchange[0]}) #@ task a math problem
if exchange[1]: # check if there is a problem
messages.append({"role": "assistant","content": exchange[1]}) # AI response with solution.
messages.append({"role":"user","content":f"Solve the given math problem step-by-step: {prob_text}"}) # math problem
completion = client.chat.completions.create(
model= "openai/gpt-4o",
message= messages,
extra_headers={
"HTTP-Referer": "https://smartmathtutor.edu", # Optional. Site URL for rankings on openrouter.ai.
"X-Title": "Smart Math Tutor", # Optional. Site title for rankings on openrouter.ai.
}
)
solution = completion.choices[0].message.content
#update history
if history is None:
history=[]
history.append((prob_text, solution ))
return solution, history
except Exception as e:
error_message= f"Error:{str(e)}"
return error_message, history
#image processing
def img_conv_base64(img_path):
if img_path is None:
return None
try:
with open(img_path,"rb") as img_file:
return base64.b64encode(img_file.read()).decode("utf-8")
except Exception as e:
print(f"Error converting image to base64:{str(e)}")
return None
# function for a given math problem for image data[Togetherai]
def gen_math_sol_together(api_key, prob_text, img_path=None, history=None):
if not api_key.strip():
return "Please enter your Valid Together API key",history
if not prob_text.strip() and img_path is None:
return "please enter a valid math problem or upload an image of a math problem", history
try:
client= Together(api_key=api_key)
messages=[{ "role": "system", "content":
"""You are a very smart math tutor who can able to solve math and give proper explanations clearly and in a precise manner.
You can analyze the math problem from image given to you and provide the details solution step by step.
For every step:
1. Show all the mathematical explanations.
2. Explain each step and its necessity.
3. Connect it to the relevant mathematical concept.
Format your response with clear section of headers using markdown and comment if necessary.
Begin with a section of analysis named "Initail step" and followed number of step and conclude with "Final step" section.
"""},]
if history:
for exchange in history:
messages.append({"role":"user","content":exchange[0]}) #@ task a math problem
if exchange[1]: # check if there is a problem
messages.append({"role": "assistant","content": exchange[1]}) # AI response with solution.
user_message_content= [] # if we want to add some instructions regaring how to solve the math from given image
if prob_text.strip(): # for text
user_message_content.append({
"type": "text",
"text": f"Solve this math problem:{prob_text}"
})
else: #image
user_message_content.append({
"type": "text",
"text": "Solve this math problem from the given image"
})
if img_path:
base64_image= img_conv_base64(img_path)
if base64_image:
user_message_content.append({
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
})
messages.append({
"role": "user", #conversation saved with image data
"content": user_message_content
})
response = client.chat.completions.create(
model= "meta-llama/Llama-Vision-Free",
message= messages,
stream= False
)
solution= response.choices[0].message.content
if history is None:
history=[]
history.append(prob_text if prob_text.strip() else "Image problem ..", solution)
return solution, history
except Exception as e:
error_msg= f"Error: {str(e)}"
return error_msg, history
def create_demo():
with gr.Blocks(theme=gr.themes.Ocean(primary_hue="blue")) as demo:
gr.Markdown("# Smart Math Tutor")
gr.Markdown("""This applications provide step by step solution to a math paroblem using AI Tools.
Choose between OpenPouter's Phi-4-reasoning-plus for text based analysis and Together AI's Llama-Vision for problem with images""")\
with gr.Tabs():
with gr.TabItem("Text problem solver (OpenRouter)"):
with gr.Row():
with gr.Column(scale=1): #left side
openrouter_api_key= gr.Textbox(
label= "OpenRouter API key",
placeholder= "enter your API key here",
type= "password"
)
text_prob_input= gr.Textbox(
label="Math Problem",
placeholder= "Enter ypur math problem here..",
lines= 5
)
example_problems= gr.Examples(
examples=[
["Solve the quadratic equation: 3x² + 5x - 2 = 0"],
["Find the derivative of f(x) = x²ln(x)"],
["Calculate the area of a circle with radius 5 cm"],
["Find all values of x that satisfy the equation: log₁₀(x+1) + log₁₀(x²) = 5"]
],
input=[text_prob_input],
label="Example Problems"
)
with gr.Row():
openrouter_submit_btn= gr.Button("solve problem", variant= "primary")
openrouter_clear_btn= gr.Button("Clear")
with gr.Column(scale=2): #right side
openrouter_solution_output= gr.Markdown(label="Solution")
openrouter_conversation_history= gr.State(value=None)
openrouter_submit_btn.click(
fn=math_solution_openrouter,
inputs=[openrouter_api_key, text_prob_input, openrouter_conversation_history],
outputs=[openrouter_solution_output, openrouter_conversation_history]
)
openrouter_clear_btn.click(
fn=lambda: ("",None),
inputs=[],
outputs=[openrouter_solution_output, openrouter_conversation_history]
)
with gr.TabItem("Text problem solver (Together AI)"):
with gr.Row():
with gr.Column(scale=1): #left side
together_api_key= gr.Textbox(
label= "Together API key",
placeholder= "enter your API key here",
type= "password"
)
together_prob_input= gr.Textbox(
label="Math Problem Description",
placeholder= "Enter additional math problem here..",
lines= 3
)
together_image_input= gr.Image(
label="upload math problem image",
type="filepath"
)
with gr.Row():
together_submit_btn= gr.Button("Solve Problem", variant="primary")
together_clear_btn = gr.Button("clear")
with gr.Column(scale=2):
together_solution_output= gr.Markdown(label="Solution")
together_conversation_history= gr.State(value=None)
# Button actions
together_submit_btn.click(
fn=generate_math_solution_together,
inputs=[together_api_key, together_problem_input, together_image_input, together_conversation_history],
outputs=[together_solution_output, together_conversation_history]
)
together_clear_btn.click(
fn=lambda: ("", None),
inputs=[],
outputs=[together_solution_output, together_conversation_history]
)
# Footer
gr.Markdown("""
---
### About
This application uses Microsoft's Phi-4-reasoning-plus model via OpenRouter for text-based problems
and Llama-Vision-Free via Together AI for image-based problems.
Your API keys are required but not stored permanently.
""")
return demo
# Launch the app
if __name__ == "__main__":
demo = create_demo()
demo.launch()
|