sync with remote

app.py

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+import streamlit as st
+import os
+import google.generativeai as genai
+from huggingface_hub import hf_hub_download
+import base64
+
+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 download_pdf():
+    """
+    Downloads the PDF file from the Hugging Face Hub using the correct repo path and filename.
+    """
+    try:
+        hf_token = os.getenv("HF_TOKEN")
+        repo_id = "louiecerv/build_ml_huggingface_tutorial_dataset"  # Corrected dataset repo path
+        filename = "Deploy_Apps_on_Hugging_Face.pdf"
+        filepath = hf_hub_download(repo_id=repo_id, filename=filename, token=hf_token, repo_type="dataset")
+        return filepath
+    except Exception as e:
+        st.error(f"Failed to download PDF from Hugging Face Hub: {e}")
+        st.stop()  # Stop if the download fails
+
+# 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 = download_pdf() 
+
+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}"
+
+def display_download_button(file_path, file_name):
+    try:
+        with open(file_path, "rb") as f:
+            file_bytes = f.read()
+        b64 = base64.b64encode(file_bytes).decode()
+        href = f'<a href="data:application/pdf;base64,{b64}" download="{file_name}">Download the source document (PDF)</a>'
+        st.markdown(href, unsafe_allow_html=True)
+    except FileNotFoundError:
+        st.error("File not found for download.")
+    except Exception as e:
+        st.error(f"Error during download: {e}")
+
+# Define the ML Models
+tasks = ["About Building ML Apps on Huggingface", 
+         "Uploading the Dataset", 
+         "Creating and Deploying the Application",
+         "Develop the ML Application", 
+         "Push the Application to Hugging Face", 
+         "Deploy and Test the Application"]
+
+# --- Sidebar ---
+st.sidebar.title("🤖 Visual Q and A")
+selected_task = st.sidebar.selectbox("Select a task", tasks)
+
+# --- Main Page ---
+st.title(" Buiding Machine Learning Apps with Hugging Face 🤗")
+about = """
+
+**How to use this App**
+This app leverages Gemini 2.0 to provide insights on the provided document.
+Select a question from the dropdown menu or enter your own question to get 
+Gemini's generated response based on the provided document.
+"""
+
+with st.expander("How to use this App"):
+    st.markdown(about)
+
+# --- Q and A Tab ---
+st.header("Questions and Answers")
+
+# Generate 5 questions based on the selected model
+if selected_task == "About Building ML Apps on Huggingface":
+    questions = [
+    "What are the advantages of using separate repositories for datasets, models, and applications on Hugging Face?",
+    "How does modularity and reusability benefit ML app development on Hugging Face?",
+    "Explain the role of version control and collaboration in Hugging Face repositories.",
+    "What scalability and performance features does Hugging Face Spaces offer?",
+    "How does the Hugging Face community and ecosystem enhance ML app development?",
+    "Describe the steps to upload a dataset to Hugging Face.",
+    "What are the key components of creating and deploying an ML app on Hugging Face?",
+    "How can you set up a development environment for building ML apps on Hugging Face?",
+    "What is the process for developing and pushing an application to Hugging Face Spaces?",
+    "How does documentation and discoverability improve the usability of datasets, models, and applications on Hugging Face?"
+    ]
+if selected_task == "Uploading the Dataset":
+    questions = [
+    "Provide a step-by-step guide, including Python code if needed, to upload a dataset to Hugging Face."
+    ]
+if selected_task == "Creating and Deploying the Application":
+    questions = [
+    "Provide a step-by-step guide, including Python code if needed, to create and deploy an application on Hugging Face."
+    ]
+if selected_task == "Develop the ML Application":
+    questions = [
+    "Provide a step-by-step guide, including Python code if needed, to develop an ML application on Hugging Face."
+    ]
+if selected_task == "Push the Application to Hugging Face":
+    questions = [
+   "Provide a step-by-step guide, including Python code if needed, to push the application to Hugging Face."
+    ]
+if selected_task == "Deploy and Test the Application":
+    questions = [
+    "Provide a step-by-step guide, including Python code if needed, to deploy and test the application on Hugging Face."
+    ]
+
+# Create a selection box
+selected_question = st.selectbox("Choose a question", questions)
+
+# Display a checkbox
+if st.checkbox('Check this box to ask a question not listed above'):
+    # If the checkbox is checked, display a text box
+    selected_question = st.text_input('Enter a question')
+
+if st.button("Ask AI"):
+    with st.spinner("AI is thinking..."):
+        if st.session_state.uploaded_pdf_path is None:
+            st.session_state.uploaded_pdf_path = download_pdf()
+
+        filepath = st.session_state.uploaded_pdf_path
+        text_prompt = f"Use the provided document.  Focus on the task: {selected_task} to answer the following question: {selected_question}.  Use your own knowledge as well as sources from the web and the provided document. Always cite your sourcss."
+        response = multimodal_prompt(filepath, text_prompt)  # Use the downloaded filepath
+        st.markdown(f"**Question:** {selected_question}")
+        st.markdown(f"**Response:** {response}")
+
+if st.session_state.uploaded_pdf_path:
+    display_download_button(st.session_state.uploaded_pdf_path, "Deploy_Apps_on_Hugging_Face.pdf")
+
+st.markdown("[Visit our Hugging Face Space!](https://huggingface.co/wvsuaidev)")
+st.markdown("© 2025 WVSU AI Dev Team 🤖 ✨")

requirements.txt

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+streamlit
+huggingface_hub
+google-generativeai