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- app.py +87 -0
- requirements.txt +6 -0
- utils.py +41 -0
app.py
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from dotenv import load_dotenv
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load_dotenv()
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from utils import encode_image_to_base64
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import os
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from langchain_groq.chat_models import ChatGroq
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from langchain_groq.chat_models import ChatGroq
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from langchain_core.messages import HumanMessage
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import streamlit as st
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def analyse(file):
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api_key = os.getenv("GROQ_API_KEY")
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if not api_key:
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raise ValueError("GROQ_API_KEY not found")
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print("Encoding image...")
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encoded_file, mime_type = encode_image_to_base64(file)
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print(f"Image encoded successfully. MIME type: {mime_type}")
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print("Initializing ChatGroq...")
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llm = ChatGroq(model="llama-3.2-90b-vision-preview")
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print("Creating message...")
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message = HumanMessage(
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content=[
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{
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"type": "text",
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"text": """Analyse the students performance very strictly,
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and generate a detailed 10 point summary informing about the performance and weakness of the student.
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Keep More emphasis on the theoretical marks of the student."""
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:{mime_type};base64,{encoded_file}"
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}
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}
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]
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)
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print("Sending request to ChatGroq...")
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response = llm.invoke([message])
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print("Response received!")
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return response.content
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def generate_plan(report):
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api_key = os.getenv("GROQ_API_KEY")
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if not api_key:
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raise ValueError("GROQ_API_KEY not found")
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print("Initializing ChatGroq...")
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llm = ChatGroq(model="llama-3.3-70b-versatile")
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print("Creating message...")
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message = HumanMessage(
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content=[
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{
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"type": "text",
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"text": f"""Based on the given report Generate a Learning plan for studying the weak subjects.
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Since the student might have school during the weekdays, balance out the workload and study hours accordingly.
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<report>{report}</report>"""
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},
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]
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)
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print("Sending request to ChatGroq...")
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response = llm.invoke([message])
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print("Response received!")
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return response.content
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if __name__ == "__main__":
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file = st.file_uploader(label="Upload Latest Report Card:-")
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print(f"Testing with file: {file}")
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col1,col2=st.columns(2)
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with col1:
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if st.button("Analyse"):
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res=analyse(file)
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st.write(res)
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with col2:
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if st.button("Generate Plan"):
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aux=analyse(file)
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res=generate_plan(aux)
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st.write(res)
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requirements.txt
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langchain
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langchain_groq
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groq
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# bs64
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python-dotenv
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streamlit
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utils.py
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import base64
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import os
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def encode_image_to_base64(image_path):
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"""
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Read an image file and convert it to a base64 string.
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Args:
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image_path (str): Path to the image file
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Returns:
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tuple: (base64_string, mime_type)
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"""
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try:
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# Get the file extension
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_, ext = os.path.splitext(image_path)
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ext = ext.lower()
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# Determine MIME type
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mime_types = {
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'.png': 'image/png',
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'.jpg': 'image/jpeg',
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'.jpeg': 'image/jpeg'
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}
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mime_type = mime_types.get(ext, 'image/png')
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with open(image_path, 'rb') as image_file:
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# Read the binary data
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binary_data = image_file.read()
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# Encode to base64
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base64_encoded = base64.b64encode(binary_data)
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# Convert to string and remove b'' prefix
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return base64_encoded.decode('utf-8'), mime_type
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except FileNotFoundError:
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raise FileNotFoundError(f"Image file not found at: {image_path}")
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except Exception as e:
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raise Exception(f"Error encoding image: {str(e)}")
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# print(encode_image_to_base64("backiee-81547.jpg"))
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