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197 changes: 197 additions & 0 deletions educonnect.py
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# Import necessary libraries
import google.generativeai as genai
import PIL.Image
import os
import streamlit as st
from groq import Groq

# Set up Streamlit app title and description
st.set_page_config(page_title="EduConnect.AI", layout="wide")
st.title("🎓 EduConnect.AI: Enhance Your Learning with AI")
st.sidebar.success("Select a feature above to get started!")

# Sidebar for navigation between different sections
with st.sidebar:
st.header("Navigation")
options = ["Roadmap Generator", "Image Content Extraction", "Text Query", "Learning Schedule Generator", "AI Tutor", "Progress Tracker"]
selected_feature = st.radio("Choose a feature:", options)

# Initialize Groq and Google API clients
groq_api_key = "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" # Replace with your actual API key
groq_client = Groq(api_key=groq_api_key)

def llama_model(prompt):
"""Utility to call the LLaMA model from Groq."""
chat_completion = groq_client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model="llama3-8b-8192",
)
return chat_completion

# 1. Roadmap Generator
if selected_feature == "Roadmap Generator":
st.header("🗺️ Roadmap Generator")
st.write("Enter the topic you want to generate a roadmap for, and receive YouTube and website links to relevant content.")

sam = st.text_input("Which roadmap do you want?", placeholder="e.g., Data Science, Full Stack Development")

if st.button("Generate Roadmap"):
if sam:
with st.spinner('Generating roadmap...'):
data = f"Give me the roadmap for {sam} with YouTube links and other website links."
chat_completion = llama_model(data)
st.success("Roadmap generated!")
st.write(chat_completion.choices[0].message.content)
else:
st.error("Please enter a topic to generate a roadmap.")

# 2. Image Content Extraction
elif selected_feature == "Image Content Extraction":
st.header("🖼️ Image Content Extraction")
st.write("Upload an image and choose the task (get short notes, explanation, or ask a custom question).")

# Choose the task for the image
data_photo = st.radio(
"What do you want to do with this image?",
('shortnotes', 'explanation', 'Provide a custom question')
)

# If custom question is selected, show the input field
custom_question = None
if data_photo == "Provide a custom question":
custom_question = st.text_input("Enter your custom question:", placeholder="e.g., What does this image represent?")

# Image uploader
photo = st.file_uploader("Upload an image:", type=["png", "jpg", "jpeg"])

if photo:
img = PIL.Image.open(photo)
st.image(img, caption="Uploaded Image", use_column_width=True)

# Generate content button
if st.button("Generate Content"):
# If a custom question is provided, use that, otherwise fall back to selected task (shortnotes or explanation)
prompt = custom_question if custom_question else f"Provide {data_photo} for this image."

with st.spinner("Generating content..."):
try:
userdata = {'gemma': 'AIzaSyCtkHjX1sXhvEQ5_6ySoWqCHIO2xMdEsak'} # Replace with actual API key
api_key = userdata.get('gemma')
genai.configure(api_key=api_key)
model = genai.GenerativeModel(model_name="gemini-1.5-flash")

# Here you can modify the prompt based on the task or question
response = model.generate_content([prompt, img])
st.success("Content generated!")
st.write(response.text)
except Exception as e:
st.error(f"Error occurred: {e}")

# 3. Text Query
elif selected_feature == "Text Query":
st.header("💬 Ask a Question")
st.write("Type a question you'd like to ask, and get an AI-generated response!")

data = st.text_input("Enter your question:", placeholder="e.g., What is the capital of France?")

if st.button("Ask"):
if data:
with st.spinner('Generating answer...'):
chat_completion = llama_model(data)
st.success("Answer generated!")
st.write(chat_completion.choices[0].message.content)
else:
st.error("Please enter a question to ask.")

# 4. Learning Schedule Generator
elif selected_feature == "Learning Schedule Generator":
st.header("📅 Learning Schedule Generator")
st.write("Plan your learning with AI! Enter a topic and the number of weeks, and get a detailed study schedule.")

sam = st.text_input("Topic you want to schedule learning for:", placeholder="e.g., Machine Learning")
days = st.slider("How many weeks?", min_value=1, max_value=52, value=4, step=1)

if st.button("Generate Schedule"):
if sam and days:
with st.spinner(f"Creating a {days}-week learning schedule for {sam}..."):
bata = f"I want to learn {sam} in {days} weeks. Make a schedule for it with some study materials."
userdata = {'gemma': 'AIzaSyCtkHjX1sXhvEQ5_6ySoWqCHIO2xMdEsak'} # Replace with actual API key
api_key = userdata.get('gemma')
genai.configure(api_key=api_key)
model = genai.GenerativeModel(model_name="gemini-1.5-flash")
response = model.generate_content(bata)
st.success(f"{days}-week schedule generated!")
st.write(response.text)
else:
st.error("Please provide both the topic and number of weeks.")

# 5. AI Tutor Chatbot
elif selected_feature == "AI Tutor":
st.header("👨‍🏫 AI Tutor")
query = st.text_input("Ask the AI Tutor any question:")

if st.button("Ask AI Tutor"):
if query:
with st.spinner("Generating AI Tutor response..."):
answer = llama_model(query)
st.write(answer.choices[0].message.content)
else:
st.error("Please ask a question to get an answer.")

# 6. Progress Tracker (with To-Do List functionality)
# 6. Progress Tracker (with n tasks input functionality)
elif selected_feature == "Progress Tracker":
st.header("📊 Progress Tracker")

# Initialize the to-do list in session state
if 'tasks' not in st.session_state:
st.session_state.tasks = []

# Function to add multiple tasks to the list
def add_tasks(task_list):
for task in task_list:
if task: # Ensure task is not empty
st.session_state.tasks.append({"task": task, "completed": False})

# Input to determine how many tasks to add at once
num_tasks = st.number_input("How many tasks do you want to add?", min_value=1, max_value=50, step=1, value=1)

# Generate task input fields dynamically
task_list = []
st.write("### Enter your tasks:")
for i in range(num_tasks):
task = st.text_input(f"Task {i + 1}", key=f"task_{i}")
task_list.append(task)

if st.button("Add Tasks"):
add_tasks(task_list)
st.success(f"{num_tasks} tasks added!")

# Display current tasks
if st.session_state.tasks:
st.write("### Your To-Do List:")
for idx, task_info in enumerate(st.session_state.tasks):
col1, col2 = st.columns([6, 1])
with col1:
st.write(f"{idx + 1}. {task_info['task']}")
with col2:
if st.button(f"Complete {idx + 1}", key=f"complete_{idx}"):
st.session_state.tasks[idx]["completed"] = True

# Display completed tasks
completed_tasks = [task for task in st.session_state.tasks if task["completed"]]
if completed_tasks:
st.write("### Completed Tasks:")
for task in completed_tasks:
st.write(f"- {task['task']}")

# Track total tasks and completed progress
total_tasks = len(st.session_state.tasks)
completed_tasks_count = len([task for task in st.session_state.tasks if task["completed"]])
if total_tasks > 0:
st.progress(completed_tasks_count / total_tasks)
if completed_tasks_count == total_tasks:
st.success("You've completed all your tasks! 🎉")

# Streamlit run command:
# Run this script in your terminal using the command: `streamlit run <script_name>.py`