import streamlit as st
from PIL import Image
import os
import base64
import io
from dotenv import load_dotenv
from groq import Groq
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
from reportlab.lib.styles import getSampleStyleSheet
# ======================
# CONFIGURATION SETTINGS
# ======================
PAGE_CONFIG = {
"page_title": "Rice Quality Analyzer",
"page_icon": "🌾",
"layout": "wide",
"initial_sidebar_state": "expanded"
}
ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
CSS_STYLES = """
"""
# ======================
# CORE FUNCTIONS
# ======================
def configure_application():
"""Initialize application settings and styling"""
st.set_page_config(**PAGE_CONFIG)
st.markdown(CSS_STYLES, unsafe_allow_html=True)
def initialize_api_client():
"""Create and validate Groq API client"""
load_dotenv()
api_key = os.getenv("GROQ_API_KEY")
if not api_key:
st.error("API key not found. Please verify .env configuration.")
st.stop()
return Groq(api_key=api_key)
def process_image_data(uploaded_file):
"""Convert image to base64 encoded string"""
try:
image = Image.open(uploaded_file)
buffer = io.BytesIO()
image.save(buffer, format=image.format)
return base64.b64encode(buffer.getvalue()).decode('utf-8'), image.format
except Exception as e:
st.error(f"Image processing error: {str(e)}")
return None, None
def generate_pdf_report(report_text):
"""Generate PDF document from report text"""
buffer = io.BytesIO()
doc = SimpleDocTemplate(buffer, pagesize=letter)
styles = getSampleStyleSheet()
story = []
title = Paragraph("Rice Quality Report", styles['Title'])
story.append(title)
story.append(Spacer(1, 12))
content = Paragraph(report_text.replace('\n', '
'), styles['BodyText'])
story.append(content)
doc.build(story)
buffer.seek(0)
return buffer
def generate_rice_report(uploaded_file, client):
"""Generate AI-powered rice quality analysis"""
base64_image, img_format = process_image_data(uploaded_file)
if not base64_image:
return None
image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
try:
response = client.chat.completions.create(
model="llama-3.2-11b-vision-preview",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": (
"Analyze the rice grain image and provide a detailed report including:\n"
"1. Rice type classification\n2. Quality assessment (broken grains %, discoloration %, impurities %)\n"
"3. Foreign object detection\n4. Size and shape consistency\n5. Recommendations for processing or improvement"
)},
{"type": "image_url", "image_url": {"url": image_url}},
]
}],
temperature=0.2,
max_tokens=400,
top_p=0.5
)
return response.choices[0].message.content
except Exception as e:
st.error(f"API communication error: {str(e)}")
return None
# ======================
# UI COMPONENTS
# ======================
def display_main_interface():
"""Render primary application interface"""
st.title("🌾 Rice Quality Analyzer")
st.subheader("AI-Powered Rice Grain Inspection")
st.markdown("---")
# Display analysis results
if st.session_state.get('analysis_result'):
st.markdown("### 📋 Analysis Report")
st.markdown(
f'