sync with remote

app.py

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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 = f"Use the provided document. Create a list of math problems found in the document. Format the output as a JSON string"
+    response = multimodal_prompt(filepath, text_prompt)  # Use the local filepath
+    st.markdown(response)
+
+try:
+    problems = json.loads(response)
+    for problem in problems:
+        st.write(problem)
+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 🤖 ✨")

requirements.txt

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+streamlit
+Pillow
+google-generativeai