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import streamlit as st
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from PIL import Image
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import os
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import base64
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import io
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from dotenv import load_dotenv
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from groq import Groq
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from reportlab.lib.pagesizes import letter
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from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
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from reportlab.lib.styles import getSampleStyleSheet
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PAGE_CONFIG = {
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"page_title": "Rice Quality Analyzer",
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"page_icon": "πΎ",
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"layout": "wide",
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"initial_sidebar_state": "expanded"
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}
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ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
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CSS_STYLES = """
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<style>
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.main { background-color: #f4f9f9; color: #000000; }
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.sidebar .sidebar-content { background-color: #d1e7dd; }
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.stTextInput textarea { color: #000000 !important; }
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.stButton>button {
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background-color: #21eeef;
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color: white;
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font-size: 16px;
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border-radius: 5px;
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}
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.report-container {
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background-color: #ffffff;
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border-radius: 15px;
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padding: 20px;
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margin-top: 20px;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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border-left: 5px solid #21eeef;
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}
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.report-text {
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font-family: 'Arial', sans-serif;
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font-size: 14px;
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line-height: 1.6;
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color: #2c3e50;
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}
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</style>
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"""
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def configure_application():
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"""Initialize application settings and styling"""
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st.set_page_config(**PAGE_CONFIG)
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st.markdown(CSS_STYLES, unsafe_allow_html=True)
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def initialize_api_client():
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"""Create and validate Groq API client"""
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load_dotenv()
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api_key = os.getenv("GROQ_API_KEY")
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if not api_key:
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st.error("API key not found. Please verify .env configuration.")
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st.stop()
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return Groq(api_key=api_key)
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def process_image_data(uploaded_file):
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"""Convert image to base64 encoded string"""
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try:
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image = Image.open(uploaded_file)
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buffer = io.BytesIO()
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image.save(buffer, format=image.format)
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return base64.b64encode(buffer.getvalue()).decode('utf-8'), image.format
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except Exception as e:
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st.error(f"Image processing error: {str(e)}")
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return None, None
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def generate_pdf_report(report_text):
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"""Generate PDF document from report text"""
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buffer = io.BytesIO()
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doc = SimpleDocTemplate(buffer, pagesize=letter)
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styles = getSampleStyleSheet()
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story = []
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title = Paragraph("<b>Rice Quality Report</b>", styles['Title'])
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story.append(title)
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story.append(Spacer(1, 12))
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content = Paragraph(report_text.replace('\n', '<br/>'), styles['BodyText'])
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story.append(content)
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doc.build(story)
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buffer.seek(0)
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return buffer
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def generate_rice_report(uploaded_file, client):
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"""Generate AI-powered rice quality analysis"""
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base64_image, img_format = process_image_data(uploaded_file)
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if not base64_image:
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return None
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image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
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try:
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response = client.chat.completions.create(
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model="llama-3.2-11b-vision-preview",
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messages=[{
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"role": "user",
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"content": [
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{"type": "text", "text": (
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"Analyze the rice grain image and provide a detailed report including:\n"
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"1. Rice type classification\n2. Quality assessment (broken grains %, discoloration %, impurities %)\n"
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"3. Foreign object detection\n4. Size and shape consistency\n5. Recommendations for processing or improvement"
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)},
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{"type": "image_url", "image_url": {"url": image_url}},
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]
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}],
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temperature=0.2,
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max_tokens=400,
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top_p=0.5
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)
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return response.choices[0].message.content
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except Exception as e:
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st.error(f"API communication error: {str(e)}")
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return None
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def display_main_interface():
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"""Render primary application interface"""
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st.title("πΎ Rice Quality Analyzer")
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st.subheader("AI-Powered Rice Grain Inspection")
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st.markdown("---")
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if st.session_state.get('analysis_result'):
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st.markdown("### π Analysis Report")
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st.markdown(
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f'<div class="report-container"><div class="report-text">{st.session_state.analysis_result}</div></div>',
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unsafe_allow_html=True
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)
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pdf_report = generate_pdf_report(st.session_state.analysis_result)
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st.download_button(
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label="π Download PDF Report",
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data=pdf_report,
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file_name="rice_quality_report.pdf",
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mime="application/pdf"
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)
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if st.button("Clear Analysis ποΈ"):
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st.session_state.pop('analysis_result', None)
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st.rerun()
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def render_sidebar(client):
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"""Create sidebar interface elements"""
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with st.sidebar:
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st.markdown("### Features")
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st.markdown("""
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- **Rice Type Classification** (e.g., Basmati, Jasmine, Indica)
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- **Quality Check** (Broken grains %, impurities %, discoloration %)
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- **Foreign Object Detection** (Husks, stones, debris)
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- **Grain Size & Shape Analysis**
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- **Processing Recommendations**
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""")
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st.markdown("---")
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st.subheader("Upload Rice Image")
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uploaded_file = st.file_uploader(
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"Select an image of rice grains",
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type=ALLOWED_FILE_TYPES
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)
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if uploaded_file:
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st.image(Image.open(uploaded_file), caption="Uploaded Image", use_column_width=True)
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if st.button("Analyze Rice Quality π"):
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with st.spinner("Processing image... This may take a few seconds."):
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report = generate_rice_report(uploaded_file, client)
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st.session_state.analysis_result = report
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st.rerun()
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def main():
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"""Primary application controller"""
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configure_application()
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groq_client = initialize_api_client()
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display_main_interface()
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render_sidebar(groq_client)
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if __name__ == "__main__":
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main()
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