Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import openai
+import requests
+
+# Set up OpenAI API key (or use Hugging Face models if preferred)
+openai.api_key = "YOUR_OPENAI_API_KEY"
+
+# Define the study assistant chatbot
+class StudyAssistantChatbot:
+    def __init__(self):
+        # Use Hugging Face's Transformers pipeline or OpenAI for Q&A
+        self.qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+    
+    def get_chat_response(self, question: str):
+        # Query the chatbot model (OpenAI) to generate a response
+        response = openai.Completion.create(
+            engine="text-davinci-003",
+            prompt=f"Provide a study-relevant answer to the question: {question}",
+            max_tokens=150
+        )
+        return response.choices[0].text.strip()
+    
+    def get_study_tips(self, subject: str):
+        # Placeholder for GROQ API call to fetch study resources
+        # Example: query GROQ API for study resources related to the subject
+        response = requests.get(f"https://api.groq.io/v1/resources?topic={subject}&api_key=YOUR_GROQ_API_KEY")
+        if response.status_code == 200:
+            resources = response.json()
+            return [resource['title'] + ": " + resource['url'] for resource in resources['data']]
+        else:
+            return ["Resource not available. Please check your API connection."]
+    
+    def get_productivity_tips(self):
+        # Static productivity tips as an example
+        return [
+            "Break your study time into 25-minute blocks with 5-minute breaks.",
+            "Set specific, achievable study goals each day.",
+            "Limit distractions by turning off notifications.",
+            "Try to explain what you learned to someone else to reinforce understanding."
+        ]
+    
+    def get_practice_questions(self, topic: str):
+        # Example set of practice questions based on a topic
+        return [
+            f"Explain the key concepts of {topic}.",
+            f"List the important formulas related to {topic}.",
+            f"How would you apply {topic} in real-world scenarios?"
+        ]
+
+# Instantiate chatbot
+chatbot = StudyAssistantChatbot()
+
+# Streamlit app
+st.title("Personalized Study Assistant Chatbot")
+st.write("Ask me study-related questions, and I’ll provide tips, resources, and more!")
+
+# Input fields for user queries and topics
+query = st.text_input("Enter your study question:")
+subject = st.text_input("Enter the subject you're studying:")
+
+# Display response when the user submits a query
+if st.button("Get Study Response"):
+    if query:
+        response = chatbot.get_chat_response(query)
+        st.write("### Chatbot Response:")
+        st.write(response)
+
+if st.button("Get Study Tips"):
+    if subject:
+        study_tips = chatbot.get_study_tips(subject)
+        st.write("### Study Tips:")
+        for tip in study_tips:
+            st.write(f"- {tip}")
+    else:
+        st.write("Please enter a subject to get study tips.")
+
+if st.button("Get Productivity Tips"):
+    productivity_tips = chatbot.get_productivity_tips()
+    st.write("### Productivity Tips:")
+    for tip in productivity_tips:
+        st.write(f"- {tip}")
+
+if st.button("Get Practice Questions"):
+    if subject:
+        practice_questions = chatbot.get_practice_questions(subject)
+        st.write("### Practice Questions:")
+        for question in practice_questions:
+            st.write(f"- {question}")
+    else:
+        st.write("Please enter a subject to get practice questions.")