Update app.py

app.py

@@ -2,6 +2,18 @@ import gradio as gr
 import pandas as pd
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
+import openai
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
+import os
+from google.auth.transport.requests import Request
+from google.oauth2.credentials import Credentials
+from google_auth_oauthlib.flow import InstalledAppFlow
+from googleapiclient.discovery import build
+
+# Access the API key from Hugging Face Secrets
+openai.api_key = os.environ.get("OPENAI_API_KEY")
 
 # Sample dataset of resources
 resources = pd.DataFrame({
@@ -10,6 +22,10 @@ resources = pd.DataFrame({
     "Learning Style": ["Visual", "Reading", "Visual", "Visual"]
 })
 
+# Global variables
+tasks = []
+points = 0
+
 # Function to recommend resources
 def recommend_resources(learning_style):
     vectorizer = TfidfVectorizer()
@@ -19,19 +35,112 @@ def recommend_resources(learning_style):
     recommended_index = similarities.argmax()
     return resources.iloc[recommended_index]
 
+# Function to add a task
+def add_task(task, deadline, priority):
+    global tasks
+    tasks.append({"Task": task, "Deadline": deadline, "Priority": priority, "Completed": False})
+    return "Task added successfully!"
+
+# Function to mark a task as completed
+def mark_completed(task_index):
+    global tasks, points
+    if 0 <= task_index < len(tasks):
+        tasks[task_index]["Completed"] = True
+        points += 10  # Award 10 points for completing a task
+        return f"Task marked as completed! You earned 10 points. Total points: {points}"
+    return "Invalid task index."
+
+# Function to visualize progress
+def show_progress():
+    completed = sum(1 for task in tasks if task["Completed"])
+    remaining = len(tasks) - completed
+    
+    # Create a progress chart
+    plt.bar(["Completed", "Remaining"], [completed, remaining], color=["green", "red"])
+    plt.title("Study Progress")
+    buffer = BytesIO()
+    plt.savefig(buffer, format="png")
+    buffer.seek(0)
+    image_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+    plt.close()
+    return f"data:image/png;base64,{image_base64}"
+
+# Function to interact with the chatbot
+def chatbot(user_input):
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=user_input,
+        max_tokens=50
+    )
+    return response.choices[0].text.strip()
+
+# Function to sync tasks with Google Calendar
+def sync_with_calendar():
+    creds = None
+    if os.path.exists("token.json"):
+        creds = Credentials.from_authorized_user_file("token.json", ["https://www.googleapis.com/auth/calendar"])
+    if not creds or not creds.valid:
+        if creds and creds.expired and creds.refresh_token:
+            creds.refresh(Request())
+        else:
+            flow = InstalledAppFlow.from_client_secrets_file("credentials.json", ["https://www.googleapis.com/auth/calendar"])
+            creds = flow.run_local_server(port=0)
+        with open("token.json", "w") as token:
+            token.write(creds.to_json())
+    
+    service = build("calendar", "v3", credentials=creds)
+    for task in tasks:
+        event = {
+            "summary": task["Task"],
+            "start": {"dateTime": task["Deadline"] + "T09:00:00", "timeZone": "UTC"},
+            "end": {"dateTime": task["Deadline"] + "T10:00:00", "timeZone": "UTC"},
+        }
+        event = service.events().insert(calendarId="primary", body=event).execute()
+    return "Tasks synced with Google Calendar!"
+
 # Gradio Interface
-def recommend(learning_style):
-    recommended_resource = recommend_resources(learning_style)
-    return f"Recommended Resource: {recommended_resource['Resource']}"
-
-# Create the Gradio app
-iface = gr.Interface(
-    fn=recommend,
-    inputs=gr.components.Textbox(label="Enter your preferred learning style (Visual/Reading):"),
-    outputs=gr.components.Textbox(label="Recommended Resource"),
-    title="AI-Powered Study Assistant",
-    description="Get personalized study recommendations based on your learning style."
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# AI-Powered Study Assistant")
+    
+    with gr.Tab("Task Management"):
+        with gr.Row():
+            task_input = gr.Textbox(label="Task")
+            deadline_input = gr.Textbox(label="Deadline (YYYY-MM-DD)")
+            priority_input = gr.Textbox(label="Priority (High/Medium/Low)")
+        add_task_button = gr.Button("Add Task")
+        task_output = gr.Textbox(label="Output")
+        add_task_button.click(add_task, inputs=[task_input, deadline_input, priority_input], outputs=task_output)
+        
+        with gr.Row():
+            task_index_input = gr.Number(label="Task Index to Mark as Completed")
+            mark_completed_button = gr.Button("Mark Completed")
+        mark_completed_output = gr.Textbox(label="Output")
+        mark_completed_button.click(mark_completed, inputs=task_index_input, outputs=mark_completed_output)
+        
+        gr.Markdown("### Tasks")
+        task_list = gr.Dataframe(headers=["Task", "Deadline", "Priority", "Completed"], value=tasks)
+    
+    with gr.Tab("Progress Tracking"):
+        progress_button = gr.Button("Show Progress")
+        progress_image = gr.Image(label="Progress Chart")
+        progress_button.click(show_progress, outputs=progress_image)
+    
+    with gr.Tab("Chatbot"):
+        chatbot_input = gr.Textbox(label="Ask a question:")
+        chatbot_output = gr.Textbox(label="Chatbot Response")
+        chatbot_button = gr.Button("Ask")
+        chatbot_button.click(chatbot, inputs=chatbot_input, outputs=chatbot_output)
+    
+    with gr.Tab("Recommendations"):
+        learning_style_input = gr.Textbox(label="Enter your preferred learning style (Visual/Reading):")
+        recommendation_output = gr.Textbox(label="Recommended Resource")
+        recommend_button = gr.Button("Get Recommendation")
+        recommend_button.click(recommend_resources, inputs=learning_style_input, outputs=recommendation_output)
+    
+    with gr.Tab("Google Calendar Sync"):
+        sync_button = gr.Button("Sync with Google Calendar")
+        sync_output = gr.Textbox(label="Output")
+        sync_button.click(sync_with_calendar, outputs=sync_output)
 
 # Launch the app
-iface.launch()
+demo.launch()