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import streamlit as st
import os
import google.generativeai as genai
import json
from PIL import Image
MODEL_ID = "gemini-2.0-flash-exp" # Keep the model ID as is
try:
api_key = os.getenv("GEMINI_API_KEY")
model_id = MODEL_ID
genai.configure(api_key=api_key)
except Exception as e:
st.error(f"Error: {e}")
st.stop()
model = genai.GenerativeModel(MODEL_ID)
chat = model.start_chat()
def get_local_pdf_path():
"""
Returns the path to the local PDF file.
"""
try:
pdf_path = os.path.join("problems", "problems.pdf")
if not os.path.exists(pdf_path):
raise FileNotFoundError(f"{pdf_path} does not exist.")
return pdf_path
except Exception as e:
st.error(f"Failed to find the local PDF: {e}")
st.stop() # Stop if the file is not found
# Initialize conversation history in Streamlit session state
if "conversation_history" not in st.session_state:
st.session_state.conversation_history = []
if "uploaded_file_part" not in st.session_state: # Store the file *part*
st.session_state.uploaded_file_part = None
if "uploaded_pdf_path" not in st.session_state:
st.session_state.uploaded_pdf_path = get_local_pdf_path()
def multimodal_prompt(pdf_path, text_prompt):
"""
Sends a multimodal prompt to Gemini, handling file uploads efficiently.
Args:
pdf_path: The path to the PDF file.
text_prompt: The text prompt for the model.
Returns:
The model's response as a string, or an error message.
"""
try:
if st.session_state.uploaded_file_part is None: # First time, upload
pdf_part = genai.upload_file(pdf_path, mime_type="application/pdf")
st.session_state.uploaded_file_part = pdf_part
prompt = [text_prompt, pdf_part] # First turn includes the actual file
else: # Subsequent turns, reference the file
prompt = [text_prompt, st.session_state.uploaded_file_part] # Subsequent turns include the file reference
response = chat.send_message(prompt)
# Update conversation history
st.session_state.conversation_history.append({"role": "user", "content": text_prompt, "has_pdf": True})
st.session_state.conversation_history.append({"role": "assistant", "content": response.text})
return response.text
except Exception as e:
return f"An error occurred: {e}"
# --- Main Page ---
st.title("📚❓Problem Solving Tutor")
about = """
**How to use this App**
Replace this placeholder with the actual text.
"""
with st.expander("How to use this App"):
st.markdown(about)
# --- Load the image ---
# image = Image.open("higher_ed.png")
# st.image(image, width=400)
st.header("Quadratic Equations")
#load the problems form the pdf
with st.spinner("AI is thinking..."):
if st.session_state.uploaded_pdf_path is None:
st.session_state.uploaded_pdf_path = get_local_pdf_path()
filepath = st.session_state.uploaded_pdf_path
text_prompt = """Use the provided document. "Read the list of 5 quadratic equations.
Return your response in JSON format, as a list of strings.
Do not include any extra text, explanations, or backslashes.
Example JSON output:
[
"x^2 - 5x + 6 = 0",
"2x^2 + 3x - 1 = 0",
"x^2 - 9 = 0",
"3x^2 - 2x + 4 = 0",
"x^2 + 8x + 15 = 0"
]
"""
response = multimodal_prompt(filepath, text_prompt) # Use the local filepath
st.markdown(response)
try:
problems = json.loads(response)
equations = []
for problem in problems:
equation = problem.split(": ")[1] #Split the string at ": " and take the second part.
equations.append(equation)
st.write("Equations only:")
for equation in equations:
st.write(equation)
except json.JSONDecodeError:
st.write("Error: Invalid JSON format in the response.")
except Exception as e:
st.write(f"An unexpected error occurred: {e}")
st.markdown("Visit our Hugging Face Space!")
st.markdown("© 2025 WVSU AI Dev Team 🤖 ✨") |