Update app.py

app.py

@@ -1,67 +1,66 @@
-from dotenv import load_dotenv
-
-load_dotenv() ## load all the environment variables
-
 import streamlit as st
+from groq import Groq
 import os
-import google.generativeai as genai
-from PIL import Image
-
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-## Function to load Google Gemini Pro Vision API And get response
+# Initialize Groq client
+client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-def get_gemini_repsonse(input,image,prompt):
-    model=genai.GenerativeModel('gemini-1.5-flash')
-    response=model.generate_content([input,image[0],prompt])
-    return response.text
-
-def input_image_setup(uploaded_file):
-    # Check if a file has been uploaded
-    if uploaded_file is not None:
-        # Read the file into bytes
-        bytes_data = uploaded_file.getvalue()
+def generate_tutor_output(subject, difficulty, student_input):
+    prompt = f"""
+    You are an expert tutor in {subject} at the {difficulty} level. 
+    The student has provided the following input: "{student_input}"
+    
+    Please generate:
+    1. A brief, engaging lesson on the topic (2-3 paragraphs)
+    2. A thought-provoking question to check understanding
+    3. Constructive feedback on the student's input
+    
+    Format your response as a JSON object with keys: "lesson", "question", "feedback"
+    """
 
-        image_parts = [
+    completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": "You are the world's best AI tutor, renowned for your ability to explain complex concepts in an engaging, clear, and memorable way and giving math examples. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {difficulty} level students. Your goal is to not just impart knowledge, but to inspire a love for learning and critical thinking.",
+            },
             {
-                "mime_type": uploaded_file.type,  # Get the mime type of the uploaded file
-                "data": bytes_data
+                "role": "user",
+                "content": prompt,
             }
-        ]
-        return image_parts
-    else:
-        raise FileNotFoundError("No file uploaded")
-    
-##initialize our streamlit app
-
-st.set_page_config(page_title="Crop Disease Detection App")
-
-st.header("Gemini Crop Disease App")
-input=st.text_input("Input Prompt: ",key="input")
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-image=""   
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image.", use_column_width=True)
+        ],
+        model="llama3-groq-70b-8192-tool-use-preview",
+        max_tokens=1000,
+    )
 
+    return completion.choices[0].message.content
 
-submit=st.button("Predict Crop/Plant Health")
+st.title("🎓 Your AI Tutor by Farhan")
 
-input_prompt="""
-        "You are an expert in computer vision and agriculture who can easily predict the disease of the plant. "
-        "Analyze the following image and provide 6 outputs in a structured table format: "
-        "1. Crop in the image, "
-        "2. Whether it is infected or healthy, "
-        "3. Type of disease (if any), "
-        "4. How confident out of 100% whether image is healthy or infected "
-        "5. Reason for the disease such as whether it is happening due to fungus, bacteria, insect bite, poor nutrition, etc., "
-        "6. Precautions for it."
-"""
+# Subject selection
+subject = st.selectbox("Subject", ["Math", "Science", "History", "Literature", "Code", "AI"], help="Choose the subject of your lesson")
+difficulty = st.radio("Difficulty Level", ["Beginner", "Intermediate", "Advanced"], help="Select your proficiency level")
+student_input = st.text_input("Your Input", placeholder="Type your query here...", help="Enter the topic you want to learn")
 
-## If submit button is clicked
+if st.button("Generate Lesson"):
+    output = generate_tutor_output(subject, difficulty, student_input)
+    try:
+        parsed = eval(output)  # Use `json.loads` if your output is a valid JSON string
+        st.subheader("Lesson")
+        st.markdown(parsed["lesson"])
+        st.subheader("Comprehension Question")
+        st.markdown(parsed["question"])
+        st.subheader("Feedback")
+        st.markdown(parsed["feedback"])
+    except Exception as e:
+        st.error(f"Error parsing output: {e}")
 
-if submit:
-    image_data=input_image_setup(uploaded_file)
-    response=get_gemini_repsonse(input_prompt,image_data,input)
-    st.subheader("The Response is")
-    st.write(response)
+st.markdown("""
+### How to Use
+1. Select a subject from the dropdown.
+2. Choose your difficulty level.
+3. Enter the topic or question you'd like to explore.
+4. Click 'Generate Lesson' to receive a personalized lesson, question, and feedback.
+5. Review the AI-generated content to enhance your learning.
+6. Feel free to ask follow-up questions or explore new topics!
+""")