-
-
-
Quality Assessment
-
- - Quality: {quality}
- - Confidence Score: {confidence:.2%}
- - Estimated Shelf Life: {shelf_life} days
-
-
+#
+#
+#
+#
Quality Assessment
+#
+# - Quality: {quality}
+# - Confidence Score: {confidence:.2%}
+# - Estimated Shelf Life: {shelf_life} days
+#
+#
-
-
Nutritional Information (per 100g)
-
-
- | Nutrient |
- Value |
-
- """
+#
+#
Nutritional Information (per 100g)
+#
+#
+# | Nutrient |
+# Value |
+#
+# """
- # Add nutrition data
- for nutrient, value in nutrition.items():
- html += f"| {nutrient} | {value} |
"
+# # Add nutrition data
+# for nutrient, value in nutrition.items():
+# html += f"| {nutrient} | {value} |
"
- html += """
-
+# html += """
+#
-
Storage Recommendations
-
-
- | Parameter |
- Recommendation |
-
- """
+# Storage Recommendations
+#
+#
+# | Parameter |
+# Recommendation |
+#
+# """
- # Add storage data
- for param, value in storage.items():
- html += f"| {param} | {value} |
"
+# # Add storage data
+# for param, value in storage.items():
+# html += f"| {param} | {value} |
"
- html += """
-
-
-
+# html += """
+#
+#
+#
-
Handling Tips
-
- - Wash thoroughly before consumption
- - Keep away from ethylene-producing fruits if sensitive
- - Check regularly for signs of decay
-
+#
Handling Tips
+#
+# - Wash thoroughly before consumption
+# - Keep away from ethylene-producing fruits if sensitive
+# - Check regularly for signs of decay
+#
-
-
-
- """
+#
+#
+#
+# """
- return html
+# return html
-def main():
- # Apply custom CSS styling
- apply_custom_css()
+# def main():
+# # Apply custom CSS styling
+# apply_custom_css()
- # Create header with logo
- st.image("https://via.placeholder.com/800x200.png?text=Advanced+Fruit+Monitoring+System", use_column_width=True, output_format="JPEG")
+# # Create header with logo
+# st.image("https://via.placeholder.com/800x200.png?text=Advanced+Fruit+Monitoring+System", use_column_width=True, output_format="JPEG")
- # Navigation
- selected = option_menu(
- menu_title=None,
- options=[t("dashboard"), t("webcam"), t("batch"), t("history"), t("settings")],
- icons=["house", "camera", "folder", "clock-history", "gear"],
- menu_icon="cast",
- default_index=0,
- orientation="horizontal",
- styles={
- "container": {"padding": "0!important", "background-color": "#fafafa" if not st.session_state.dark_mode else "#333333"},
- "icon": {"color": "orange", "font-size": "18px"},
- "nav-link": {"font-size": "16px", "text-align": "center", "margin": "0px", "--hover-color": "#eee" if not st.session_state.dark_mode else "#555555"},
- "nav-link-selected": {"background-color": "#4CAF50"},
- }
- )
+# # Navigation
+# selected = option_menu(
+# menu_title=None,
+# options=[t("dashboard"), t("webcam"), t("batch"), t("history"), t("settings")],
+# icons=["house", "camera", "folder", "clock-history", "gear"],
+# menu_icon="cast",
+# default_index=0,
+# orientation="horizontal",
+# styles={
+# "container": {"padding": "0!important", "background-color": "#fafafa" if not st.session_state.dark_mode else "#333333"},
+# "icon": {"color": "orange", "font-size": "18px"},
+# "nav-link": {"font-size": "16px", "text-align": "center", "margin": "0px", "--hover-color": "#eee" if not st.session_state.dark_mode else "#555555"},
+# "nav-link-selected": {"background-color": "#4CAF50"},
+# }
+# )
- # Dashboard
- if selected == t("dashboard"):
- st.title(t("title"))
+# # Dashboard
+# if selected == t("dashboard"):
+# st.title(t("title"))
- upload_col, preview_col = st.columns([1, 1])
+# upload_col, preview_col = st.columns([1, 1])
- with upload_col:
- uploaded_file = st.file_uploader(t("upload"), type=["jpg", "jpeg", "png"])
+# with upload_col:
+# uploaded_file = st.file_uploader(t("upload"), type=["jpg", "jpeg", "png"])
- # Image enhancement options
- with st.expander("Image Enhancement Options"):
- enhance_img = st.checkbox("Apply image enhancement", value=True)
+# # Image enhancement options
+# with st.expander("Image Enhancement Options"):
+# enhance_img = st.checkbox("Apply image enhancement", value=True)
- if enhance_img:
- st.caption("Enhancement includes contrast adjustment, color saturation, and sharpening")
+# if enhance_img:
+# st.caption("Enhancement includes contrast adjustment, color saturation, and sharpening")
- # Preview uploaded image
- if uploaded_file is not None:
- with preview_col:
- image_data = Image.open(uploaded_file)
- st.image(image_data, caption="Original Image", use_column_width=True)
+# # Preview uploaded image
+# if uploaded_file is not None:
+# with preview_col:
+# image_data = Image.open(uploaded_file)
+# st.image(image_data, caption="Original Image", use_column_width=True)
- # Analyze button
- if st.button(t("analyze"), use_container_width=True):
- with st.spinner("Analyzing fruit image..."):
- # Predict fruit type and quality
- predicted_name, predicted_quality, confidence, img_processed, img_resized = predict_fruit(
- np.array(image_data), enhance=enhance_img
- )
+# # Analyze button
+# if st.button(t("analyze"), use_container_width=True):
+# with st.spinner("Analyzing fruit image..."):
+# # Predict fruit type and quality
+# predicted_name, predicted_quality, confidence, img_processed, img_resized = predict_fruit(
+# np.array(image_data), enhance=enhance_img
+# )
- # Show results in a nice card layout
- st.markdown(f'
', unsafe_allow_html=True)
+# # Show results in a nice card layout
+# st.markdown(f'
', unsafe_allow_html=True)
- # Results in columns
- col1, col2, col3 = st.columns([1, 1, 1])
+# # Results in columns
+# col1, col2, col3 = st.columns([1, 1, 1])
- with col1:
- st.markdown(f"### {t('type')} {predicted_name}")
- st.markdown(f"### {t('quality')} {predicted_quality}")
- st.markdown(f"### {t('confidence')} {confidence:.2%}")
+# with col1:
+# st.markdown(f"### {t('type')} {predicted_name}")
+# st.markdown(f"### {t('quality')} {predicted_quality}")
+# st.markdown(f"### {t('confidence')} {confidence:.2%}")
- with col2:
- # Ripeness estimation
- if predicted_quality == "Good":
- ripeness = "Optimal ripeness"
- elif predicted_quality == "Mild":
- ripeness = "Slightly overripe"
- else:
- ripeness = "Overripe, not recommended for consumption"
+# with col2:
+# # Ripeness estimation
+# if predicted_quality == "Good":
+# ripeness = "Optimal ripeness"
+# elif predicted_quality == "Mild":
+# ripeness = "Slightly overripe"
+# else:
+# ripeness = "Overripe, not recommended for consumption"
- st.markdown(f"### {t('ripeness')}")
- st.markdown(ripeness)
+# st.markdown(f"### {t('ripeness')}")
+# st.markdown(ripeness)
- # Shelf life estimation
- shelf_life = shelf_life_estimates[predicted_name][predicted_quality]
- st.markdown(f"### {t('shelf_life')}")
- st.markdown(f"{shelf_life} days")
+# # Shelf life estimation
+# shelf_life = shelf_life_estimates[predicted_name][predicted_quality]
+# st.markdown(f"### {t('shelf_life')}")
+# st.markdown(f"{shelf_life} days")
- with col3:
- # Storage recommendations
- storage = storage_recommendations[predicted_name]
- st.markdown(f"### {t('storage')}")
- for key, value in storage.items():
- st.markdown(f"**{key}:** {value}")
+# with col3:
+# # Storage recommendations
+# storage = storage_recommendations[predicted_name]
+# st.markdown(f"### {t('storage')}")
+# for key, value in storage.items():
+# st.markdown(f"**{key}:** {value}")
- st.markdown('
', unsafe_allow_html=True)
+# st.markdown('
', unsafe_allow_html=True)
- # Nutritional information
- st.subheader(t('nutrition'))
+# # Nutritional information
+# st.subheader(t('nutrition'))
- # Get nutrition data for the predicted fruit
- nutrition = nutrition_data[predicted_name]
+# # Get nutrition data for the predicted fruit
+# nutrition = nutrition_data[predicted_name]
- # Display nutrition as a bar chart
- nutrition_df = pd.DataFrame({
- 'Nutrient': list(nutrition.keys()),
- 'Value': list(nutrition.values())
- })
+# # Display nutrition as a bar chart
+# nutrition_df = pd.DataFrame({
+# 'Nutrient': list(nutrition.keys()),
+# 'Value': list(nutrition.values())
+# })
- fig = px.bar(
- nutrition_df,
- x='Nutrient',
- y='Value',
- title=f"Nutritional Content of {predicted_name} (per 100g)",
- color='Value',
- color_continuous_scale=px.colors.sequential.Viridis
- )
- st.plotly_chart(fig, use_container_width=True)
+# fig = px.bar(
+# nutrition_df,
+# x='Nutrient',
+# y='Value',
+# title=f"Nutritional Content of {predicted_name} (per 100g)",
+# color='Value',
+# color_continuous_scale=px.colors.sequential.Viridis
+# )
+# st.plotly_chart(fig, use_container_width=True)
- # Damage detection with Detectron2
- if predicted_quality in ["Mild", "Rotten"]:
- st.subheader(t('damage'))
- try:
- predictor, cfg = load_detectron_model(predicted_name)
- outputs = predictor(cv2.cvtColor(np.array(img_processed), cv2.COLOR_RGB2BGR))
+# # Damage detection with Detectron2
+# if predicted_quality in ["Mild", "Rotten"]:
+# st.subheader(t('damage'))
+# try:
+# predictor, cfg = load_detectron_model(predicted_name)
+# outputs = predictor(cv2.cvtColor(np.array(img_processed), cv2.COLOR_RGB2BGR))
- if len(outputs["instances"]) > 0:
- v = Visualizer(np.array(img_processed), MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=0.8)
- out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
- st.image(out.get_image(), caption="Damage Detection Result", use_column_width=True)
- else:
- st.info(t('no_damage'))
- except Exception as e:
- st.error(f"Error in damage detection: {str(e)}")
+# if len(outputs["instances"]) > 0:
+# v = Visualizer(np.array(img_processed), MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=0.8)
+# out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+# st.image(out.get_image(), caption="Damage Detection Result", use_column_width=True)
+# else:
+# st.info(t('no_damage'))
+# except Exception as e:
+# st.error(f"Error in damage detection: {str(e)}")
- # Save analysis to history
- save_analysis(predicted_name, predicted_quality, confidence, img_processed)
+# # Save analysis to history
+# save_analysis(predicted_name, predicted_quality, confidence, img_processed)
- # Generate full report
- with st.expander("View Full Analysis Report", expanded=True):
- generate_report(
- predicted_name,
- predicted_quality,
- confidence,
- img_processed,
- nutrition_data[predicted_name],
- storage_recommendations[predicted_name],
- shelf_life_estimates[predicted_name][predicted_quality]
- )
+# # Generate full report
+# with st.expander("View Full Analysis Report", expanded=True):
+# generate_report(
+# predicted_name,
+# predicted_quality,
+# confidence,
+# img_processed,
+# nutrition_data[predicted_name],
+# storage_recommendations[predicted_name],
+# shelf_life_estimates[predicted_name][predicted_quality]
+# )
- else:
- # Show sample images when no file is uploaded
- st.markdown("### Sample Images")
- sample_col1, sample_col2, sample_col3 = st.columns(3)
+# else:
+# # Show sample images when no file is uploaded
+# st.markdown("### Sample Images")
+# sample_col1, sample_col2, sample_col3 = st.columns(3)
- with sample_col1:
- st.image("https://via.placeholder.com/200x200.png?text=Banana", caption="Banana Sample")
+# with sample_col1:
+# st.image("https://via.placeholder.com/200x200.png?text=Banana", caption="Banana Sample")
- with sample_col2:
- st.image("https://via.placeholder.com/200x200.png?text=Strawberry", caption="Strawberry Sample")
+# with sample_col2:
+# st.image("https://via.placeholder.com/200x200.png?text=Strawberry", caption="Strawberry Sample")
- with sample_col3:
- st.image("https://via.placeholder.com/200x200.png?text=Tomato", caption="Tomato Sample")
+# with sample_col3:
+# st.image("https://via.placeholder.com/200x200.png?text=Tomato", caption="Tomato Sample")
- # Instructions and features overview
- with st.expander("How to use this application", expanded=True):
- st.markdown("""
- ## Features Overview
+# # Instructions and features overview
+# with st.expander("How to use this application", expanded=True):
+# st.markdown("""
+# ## Features Overview
- This advanced fruit monitoring system allows you to:
+# This advanced fruit monitoring system allows you to:
- 1. **Upload Images** of fruits to analyze their type and quality
- 2. **Capture Images** directly from your webcam
- 3. **Batch Process** multiple fruit images at once
- 4. **Track History** of all your previous analyses
- 5. **Generate Reports** with detailed nutritional information
- 6. **Detect Damage** on fruits with quality issues
+# 1. **Upload Images** of fruits to analyze their type and quality
+# 2. **Capture Images** directly from your webcam
+# 3. **Batch Process** multiple fruit images at once
+# 4. **Track History** of all your previous analyses
+# 5. **Generate Reports** with detailed nutritional information
+# 6. **Detect Damage** on fruits with quality issues
- ## Getting Started
+# ## Getting Started
- 1. Upload a fruit image using the file uploader above
- 2. Click "Analyze Image" to process the image
- 3. View the results including fruit type, quality, and nutritional information
- 4. For fruits with quality issues, view the damage detection results
- 5. Download a comprehensive report for your records
- """)
+# 1. Upload a fruit image using the file uploader above
+# 2. Click "Analyze Image" to process the image
+# 3. View the results including fruit type, quality, and nutritional information
+# 4. For fruits with quality issues, view the damage detection results
+# 5. Download a comprehensive report for your records
+# """)
- # Webcam functionality
- elif selected == t("webcam"):
- st.title("Webcam Fruit Analysis")
+# # Webcam functionality
+# elif selected == t("webcam"):
+# st.title("Webcam Fruit Analysis")
- # Placeholder for webcam capture
- img_file_buffer = st.camera_input("Take a picture of a fruit")
+# # Placeholder for webcam capture
+# img_file_buffer = st.camera_input("Take a picture of a fruit")
- if img_file_buffer is not None:
- # Get bytes data
- image_data = Image.open(img_file_buffer)
+# if img_file_buffer is not None:
+# # Get bytes data
+# image_data = Image.open(img_file_buffer)
- if st.button("Analyze Captured Image", use_container_width=True):
- with st.spinner("Analyzing fruit from webcam..."):
- # Process image and make predictions
- predicted_name, predicted_quality, confidence, img_processed, img_resized = predict_fruit(np.array(image_data))
+# if st.button("Analyze Captured Image", use_container_width=True):
+# with st.spinner("Analyzing fruit from webcam..."):
+# # Process image and make predictions
+# predicted_name, predicted_quality, confidence, img_processed, img_resized = predict_fruit(np.array(image_data))
- # Display results
- st.success(f"Analysis complete! Detected {predicted_name} with {predicted_quality} quality ({confidence:.2%} confidence)")
+# # Display results
+# st.success(f"Analysis complete! Detected {predicted_name} with {predicted_quality} quality ({confidence:.2%} confidence)")
- # Results in columns
- col1, col2 = st.columns(2)
+# # Results in columns
+# col1, col2 = st.columns(2)
- with col1:
- st.image(img_processed, caption=f"Processed Image", width=300)
+# with col1:
+# st.image(img_processed, caption=f"Processed Image", width=300)
- with col2:
- st.markdown(f"### {t('type')} {predicted_name}")
- st.markdown(f"### {t('quality')} {predicted_quality}")
- st.markdown(f"### {t('confidence')} {confidence:.2%}")
+# with col2:
+# st.markdown(f"### {t('type')} {predicted_name}")
+# st.markdown(f"### {t('quality')} {predicted_quality}")
+# st.markdown(f"### {t('confidence')} {confidence:.2%}")
- # Shelf life estimation
- shelf_life = shelf_life_estimates[predicted_name][predicted_quality]
- st.markdown(f"### {t('shelf_life')}")
- st.markdown(f"{shelf_life} days")
+# # Shelf life estimation
+# shelf_life = shelf_life_estimates[predicted_name][predicted_quality]
+# st.markdown(f"### {t('shelf_life')}")
+# st.markdown(f"{shelf_life} days")
- # Save analysis to history
- save_analysis(predicted_name, predicted_quality, confidence, img_processed)
+# # Save analysis to history
+# save_analysis(predicted_name, predicted_quality, confidence, img_processed)
- # Generate simple report with option to view full report
- if st.button("View Detailed Report"):
- generate_report(
- predicted_name,
- predicted_quality,
- confidence,
- img_processed,
- nutrition_data[predicted_name],
- storage_recommendations[predicted_name],
- shelf_life_estimates[predicted_name][predicted_quality]
- )
+# # Generate simple report with option to view full report
+# if st.button("View Detailed Report"):
+# generate_report(
+# predicted_name,
+# predicted_quality,
+# confidence,
+# img_processed,
+# nutrition_data[predicted_name],
+# storage_recommendations[predicted_name],
+# shelf_life_estimates[predicted_name][predicted_quality]
+# )
- # Batch processing
- elif selected == t("batch"):
- st.title("Batch Fruit Analysis")
+# # Batch processing
+# elif selected == t("batch"):
+# st.title("Batch Fruit Analysis")
- st.write("Upload multiple fruit images for batch processing")
+# st.write("Upload multiple fruit images for batch processing")
- # Multiple file uploader
- uploaded_files = st.file_uploader("Upload multiple fruit images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
+# # Multiple file uploader
+# uploaded_files = st.file_uploader("Upload multiple fruit images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
- if uploaded_files:
- st.write(f"Uploaded {len(uploaded_files)} images")
+# if uploaded_files:
+# st.write(f"Uploaded {len(uploaded_files)} images")
- # Show thumbnails of uploaded images
- thumbnail_cols = st.columns(4)
- for i, uploaded_file in enumerate(uploaded_files[:8]): # Show first 8 images
- with thumbnail_cols[i % 4]:
- img = Image.open(uploaded_file)
- st.image(img, caption=f"Image {i+1}", width=150)
+# # Show thumbnails of uploaded images
+# thumbnail_cols = st.columns(4)
+# for i, uploaded_file in enumerate(uploaded_files[:8]): # Show first 8 images
+# with thumbnail_cols[i % 4]:
+# img = Image.open(uploaded_file)
+# st.image(img, caption=f"Image {i+1}", width=150)
- if len(uploaded_files) > 8:
- st.write(f"... and {len(uploaded_files) - 8} more")
+# if len(uploaded_files) > 8:
+# st.write(f"... and {len(uploaded_files) - 8} more")
- # Process button
- if st.button("Process All Images", use_container_width=True):
- # Progress bar
- progress_bar = st.progress(0)
+# # Process button
+# if st.button("Process All Images", use_container_width=True):
+# # Progress bar
+# progress_bar = st.progress(0)
- # Results container
- results = []
+# # Results container
+# results = []
- # Process each image
- for i, uploaded_file in enumerate(uploaded_files):
- img = Image.open(uploaded_file)
+# # Process each image
+# for i, uploaded_file in enumerate(uploaded_files):
+# img = Image.open(uploaded_file)
- # Update progress
- progress_bar.progress((i + 1) / len(uploaded_files))
+# # Update progress
+# progress_bar.progress((i + 1) / len(uploaded_files))
- # Process image
- with st.spinner(f"Processing image {i+1}/{len(uploaded_files)}..."):
- predicted_name, predicted_quality, confidence, img_processed, img_resized = predict_fruit(np.array(img))
+# # Process image
+# with st.spinner(f"Processing image {i+1}/{len(uploaded_files)}..."):
+# predicted_name, predicted_quality, confidence, img_processed, img_resized = predict_fruit(np.array(img))
- # Save result
- results.append({
- "image_idx": i,
- "filename": uploaded_file.name,
- "fruit_type": predicted_name,
- "quality": predicted_quality,
- "confidence": confidence,
- "image": img_processed
- })
+# # Save result
+# results.append({
+# "image_idx": i,
+# "filename": uploaded_file.name,
+# "fruit_type": predicted_name,
+# "quality": predicted_quality,
+# "confidence": confidence,
+# "image": img_processed
+# })
- # Save to history
- save_analysis(predicted_name, predicted_quality, confidence, img_processed)
+# # Save to history
+# save_analysis(predicted_name, predicted_quality, confidence, img_processed)
- # Show success message
- st.success(f"Successfully processed {len(uploaded_files)} images!")
+# # Show success message
+# st.success(f"Successfully processed {len(uploaded_files)} images!")
- # Display results in a table
- results_df = pd.DataFrame([
- {
- "Filename": r["filename"],
- "Fruit Type": r["fruit_type"],
- "Quality": r["quality"],
- "Confidence": f"{r['confidence']:.2%}"
- } for r in results
- ])
+# # Display results in a table
+# results_df = pd.DataFrame([
+# {
+# "Filename": r["filename"],
+# "Fruit Type": r["fruit_type"],
+# "Quality": r["quality"],
+# "Confidence": f"{r['confidence']:.2%}"
+# } for r in results
+# ])
- st.subheader("Batch Processing Results")
- st.dataframe(results_df, use_container_width=True)
+# st.subheader("Batch Processing Results")
+# st.dataframe(results_df, use_container_width=True)
- # Summary statistics
- st.subheader("Summary Statistics")
+# # Summary statistics
+# st.subheader("Summary Statistics")
- # Count fruits by type
- fruit_counts = pd.DataFrame(results).groupby("fruit_type").size().reset_index(name="count")
+# # Count fruits by type
+# fruit_counts = pd.DataFrame(results).groupby("fruit_type").size().reset_index(name="count")
- # Create pie chart
- fig = px.pie(
- fruit_counts,
- values="count",
- names="fruit_type",
- title="Distribution of Fruit Types",
- color_discrete_sequence=px.colors.qualitative.Plotly
- )
- st.plotly_chart(fig, use_container_width=True)
+# # Create pie chart
+# fig = px.pie(
+# fruit_counts,
+# values="count",
+# names="fruit_type",
+# title="Distribution of Fruit Types",
+# color_discrete_sequence=px.colors.qualitative.Plotly
+# )
+# st.plotly_chart(fig, use_container_width=True)
- # Count fruits by quality
- quality_counts = pd.DataFrame(results).groupby("quality").size().reset_index(name="count")
+# # Count fruits by quality
+# quality_counts = pd.DataFrame(results).groupby("quality").size().reset_index(name="count")
- # Create bar chart
- fig = px.bar(
- quality_counts,
- x="quality",
- y="count",
- title="Distribution of Fruit Quality",
- color="quality",
- color_discrete_map={"Good": "green", "Mild": "orange", "Rotten": "red"}
- )
- st.plotly_chart(fig, use_container_width=True)
+# # Create bar chart
+# fig = px.bar(
+# quality_counts,
+# x="quality",
+# y="count",
+# title="Distribution of Fruit Quality",
+# color="quality",
+# color_discrete_map={"Good": "green", "Mild": "orange", "Rotten": "red"}
+# )
+# st.plotly_chart(fig, use_container_width=True)
- # Export batch results
- csv = results_df.to_csv(index=False)
- st.download_button(
- label="Download Results as CSV",
- data=csv,
- file_name="batch_analysis_results.csv",
- mime="text/csv"
- )
+# # Export batch results
+# csv = results_df.to_csv(index=False)
+# st.download_button(
+# label="Download Results as CSV",
+# data=csv,
+# file_name="batch_analysis_results.csv",
+# mime="text/csv"
+# )
- # History view
- elif selected == t("history"):
- st.title("Analysis History")
+# # History view
+# elif selected == t("history"):
+# st.title("Analysis History")
- # Fetch historical data from database
- c = conn.cursor()
- c.execute("SELECT timestamp, fruit_type, quality, confidence_score, image_path FROM analysis_history ORDER BY timestamp DESC")
- history_data = c.fetchall()
+# # Fetch historical data from database
+# c = conn.cursor()
+# c.execute("SELECT timestamp, fruit_type, quality, confidence_score, image_path FROM analysis_history ORDER BY timestamp DESC")
+# history_data = c.fetchall()
- if not history_data:
- st.info("No analysis history available yet. Start by analyzing some fruit images!")
- else:
- # Convert to DataFrame for easier manipulation
- history_df = pd.DataFrame(history_data, columns=["Timestamp", "Fruit Type", "Quality", "Confidence", "Image Path"])
+# if not history_data:
+# st.info("No analysis history available yet. Start by analyzing some fruit images!")
+# else:
+# # Convert to DataFrame for easier manipulation
+# history_df = pd.DataFrame(history_data, columns=["Timestamp", "Fruit Type", "Quality", "Confidence", "Image Path"])
- # Display as interactive table
- st.dataframe(
- history_df[["Timestamp", "Fruit Type", "Quality", "Confidence"]].style.format({"Confidence": "{:.2%}"}),
- use_container_width=True
- )
+# # Display as interactive table
+# st.dataframe(
+# history_df[["Timestamp", "Fruit Type", "Quality", "Confidence"]].style.format({"Confidence": "{:.2%}"}),
+# use_container_width=True
+# )
- # Analytics on historical data
- st.subheader("Analytics")
+# # Analytics on historical data
+# st.subheader("Analytics")
- col1, col2 = st.columns(2)
+# col1, col2 = st.columns(2)
- with col1:
- # Fruit type distribution
- fruit_counts = history_df.groupby("Fruit Type").size().reset_index(name="Count")
- fig = px.pie(
- fruit_counts,
- values="Count",
- names="Fruit Type",
- title="Fruit Type Distribution",
- hole=0.4
- )
- st.plotly_chart(fig, use_container_width=True)
+# with col1:
+# # Fruit type distribution
+# fruit_counts = history_df.groupby("Fruit Type").size().reset_index(name="Count")
+# fig = px.pie(
+# fruit_counts,
+# values="Count",
+# names="Fruit Type",
+# title="Fruit Type Distribution",
+# hole=0.4
+# )
+# st.plotly_chart(fig, use_container_width=True)
- with col2:
- # Quality distribution
- quality_counts = history_df.groupby("Quality").size().reset_index(name="Count")
- fig = px.bar(
- quality_counts,
- x="Quality",
- y="Count",
- title="Quality Distribution",
- color="Quality",
- color_discrete_map={"Good": "green", "Mild": "orange", "Rotten": "red"}
- )
- st.plotly_chart(fig, use_container_width=True)
+# with col2:
+# # Quality distribution
+# quality_counts = history_df.groupby("Quality").size().reset_index(name="Count")
+# fig = px.bar(
+# quality_counts,
+# x="Quality",
+# y="Count",
+# title="Quality Distribution",
+# color="Quality",
+# color_discrete_map={"Good": "green", "Mild": "orange", "Rotten": "red"}
+# )
+# st.plotly_chart(fig, use_container_width=True)
- # Time series analysis
- st.subheader("Quality Trends Over Time")
+# # Time series analysis
+# st.subheader("Quality Trends Over Time")
- # Convert timestamp to datetime
- history_df["Timestamp"] = pd.to_datetime(history_df["Timestamp"], format="%Y%m%d_%H%M%S")
- history_df["Date"] = history_df["Timestamp"].dt.date
+# # Convert timestamp to datetime
+# history_df["Timestamp"] = pd.to_datetime(history_df["Timestamp"], format="%Y%m%d_%H%M%S")
+# history_df["Date"] = history_df["Timestamp"].dt.date
- # Group by date and quality
- time_quality = history_df.groupby(["Date", "Quality"]).size().reset_index(name="Count")
+# # Group by date and quality
+# time_quality = history_df.groupby(["Date", "Quality"]).size().reset_index(name="Count")
- # Create line chart
- fig = px.line(
- time_quality,
- x="Date",
- y="Count",
- color="Quality",
- title="Quality Trends Over Time",
- markers=True,
- color_discrete_map={"Good": "green", "Mild": "orange", "Rotten": "red"}
- )
- st.plotly_chart(fig, use_container_width=True)
+# # Create line chart
+# fig = px.line(
+# time_quality,
+# x="Date",
+# y="Count",
+# color="Quality",
+# title="Quality Trends Over Time",
+# markers=True,
+# color_discrete_map={"Good": "green", "Mild": "orange", "Rotten": "red"}
+# )
+# st.plotly_chart(fig, use_container_width=True)
- # Export history
- csv = history_df.to_csv(index=False)
- st.download_button(
- label="Export History as CSV",
- data=csv,
- file_name="fruit_analysis_history.csv",
- mime="text/csv"
- )
+# # Export history
+# csv = history_df.to_csv(index=False)
+# st.download_button(
+# label="Export History as CSV",
+# data=csv,
+# file_name="fruit_analysis_history.csv",
+# mime="text/csv"
+# )
- # Clear history button
- if st.button("Clear History"):
- if st.checkbox("I understand this will delete all analysis history"):
- c.execute("DELETE FROM analysis_history")
- conn.commit()
- st.session_state.history = []
- st.success("History cleared successfully!")
- st.experimental_rerun()
+# # Clear history button
+# if st.button("Clear History"):
+# if st.checkbox("I understand this will delete all analysis history"):
+# c.execute("DELETE FROM analysis_history")
+# conn.commit()
+# st.session_state.history = []
+# st.success("History cleared successfully!")
+# st.experimental_rerun()
- # Settings
- elif selected == t("settings"):
- st.title("Application Settings")
+# # Settings
+# elif selected == t("settings"):
+# st.title("Application Settings")
- # Settings sections
- st.subheader("User Interface")
+# # Settings sections
+# st.subheader("User Interface")
- # Dark mode toggle
- dark_mode = st.toggle("Dark Mode", value=st.session_state.dark_mode)
- if dark_mode != st.session_state.dark_mode:
- st.session_state.dark_mode = dark_mode
- st.experimental_rerun()
+# # Dark mode toggle
+# dark_mode = st.toggle("Dark Mode", value=st.session_state.dark_mode)
+# if dark_mode != st.session_state.dark_mode:
+# st.session_state.dark_mode = dark_mode
+# st.experimental_rerun()
- # Language selection
- language = st.selectbox(
- "Language",
- options=["English", "Spanish", "French"],
- index=["English", "Spanish", "French"].index(st.session_state.language)
- )
- if language != st.session_state.language:
- st.session_state.language = language
- st.experimental_rerun()
+# # Language selection
+# language = st.selectbox(
+# "Language",
+# options=["English", "Spanish", "French"],
+# index=["English", "Spanish", "French"].index(st.session_state.language)
+# )
+# if language != st.session_state.language:
+# st.session_state.language = language
+# st.experimental_rerun()
- # Model settings
- st.subheader("Model Settings")
+# # Model settings
+# st.subheader("Model Settings")
- # Confidence threshold
- confidence_threshold = st.slider(
- "Minimum Confidence Threshold",
- min_value=0.0,
- max_value=1.0,
- value=0.5,
- step=0.05,
- format="%.2f"
- )
+# # Confidence threshold
+# confidence_threshold = st.slider(
+# "Minimum Confidence Threshold",
+# min_value=0.0,
+# max_value=1.0,
+# value=0.5,
+# step=0.05,
+# format="%.2f"
+# )
- # Enhancement toggles
- st.subheader("Image Enhancement")
- enhance_contrast = st.checkbox("Auto-enhance Contrast", value=True)
- enhance_sharpness = st.checkbox("Auto-enhance Sharpness", value=True)
+# # Enhancement toggles
+# st.subheader("Image Enhancement")
+# enhance_contrast = st.checkbox("Auto-enhance Contrast", value=True)
+# enhance_sharpness = st.checkbox("Auto-enhance Sharpness", value=True)
- # Advanced settings
- with st.expander("Advanced Settings"):
- st.selectbox("Model Architecture", ["InceptionV3 (Current)", "EfficientNetB3", "ResNet50", "Vision Transformer"])
- st.number_input("Batch Size", min_value=1, max_value=64, value=16)
- st.checkbox("Enable GPU Acceleration (if available)", value=True)
+# # Advanced settings
+# with st.expander("Advanced Settings"):
+# st.selectbox("Model Architecture", ["InceptionV3 (Current)", "EfficientNetB3", "ResNet50", "Vision Transformer"])
+# st.number_input("Batch Size", min_value=1, max_value=64, value=16)
+# st.checkbox("Enable GPU Acceleration (if available)", value=True)
- # Database management
- st.subheader("Database Management")
- if st.button("Export Database"):
- # Get all data from database
- c = conn.cursor()
- c.execute("SELECT * FROM analysis_history")
- all_data = c.fetchall()
+# # Database management
+# st.subheader("Database Management")
+# if st.button("Export Database"):
+# # Get all data from database
+# c = conn.cursor()
+# c.execute("SELECT * FROM analysis_history")
+# all_data = c.fetchall()
- # Convert to DataFrame
- all_df = pd.DataFrame(all_data, columns=["ID", "Timestamp", "Fruit Type", "Quality", "Confidence", "Image Path"])
+# # Convert to DataFrame
+# all_df = pd.DataFrame(all_data, columns=["ID", "Timestamp", "Fruit Type", "Quality", "Confidence", "Image Path"])
- # Convert to CSV
- csv = all_df.to_csv(index=False)
+# # Convert to CSV
+# csv = all_df.to_csv(index=False)
- # Download button
- st.download_button(
- label="Download Database as CSV",
- data=csv,
- file_name="fruit_analysis_database.csv",
- mime="text/csv"
- )
+# # Download button
+# st.download_button(
+# label="Download Database as CSV",
+# data=csv,
+# file_name="fruit_analysis_database.csv",
+# mime="text/csv"
+# )
- # About section
- st.subheader("About")
- st.markdown("""
- ### Advanced Fruit Monitoring System
- Version 2.0
+# # About section
+# st.subheader("About")
+# st.markdown("""
+# ### Advanced Fruit Monitoring System
+# Version 2.0
- This application uses deep learning to analyze fruits for:
- - Fruit type identification
- - Quality assessment
- - Damage detection and segmentation
- - Nutritional information
- - Storage recommendations
+# This application uses deep learning to analyze fruits for:
+# - Fruit type identification
+# - Quality assessment
+# - Damage detection and segmentation
+# - Nutritional information
+# - Storage recommendations
- Built with Streamlit, TensorFlow, PyTorch, and Detectron2.
- """)
-
-if __name__ == "__main__":
- main()
-
-
-
-
+# Built with Streamlit, TensorFlow, PyTorch, and Detectron2.
+# """)
+# if __name__ == "__main__":
+# main()
-# import streamlit as st
-# import numpy as np
-# import cv2
-# import warnings
-# import os
-# from pathlib import Path
-# from PIL import Image
-# import tensorflow as tf
-# from tensorflow.keras.models import load_model
-# from tensorflow.keras.preprocessing import image
-# from detectron2.engine import DefaultPredictor
-# from detectron2.config import get_cfg
-# from detectron2.utils.visualizer import Visualizer
-# from detectron2.data import MetadataCatalog
-# # Suppress warnings
-# warnings.filterwarnings("ignore")
-# tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
-# # Configuration
-# MODEL_CONFIG = {
-# 'name_model': 'name_model_inception.h5',
-# 'quality_model': 'type_model_inception.h5',
-# 'input_size': (224, 224),
-# 'score_threshold': 0.5
-# }
-# LABEL_MAPS = {
-# 'name': {
-# 0: "Banana", 1: "Cucumber", 2: "Grape", 3: "Kaki", 4: "Papaya",
-# 5: "Peach", 6: "Pear", 7: "Peeper", 8: "Strawberry", 9: "Watermelon",
-# 10: "tomato"
-# },
-# 'quality': {0: "Good", 1: "Mild", 2: "Rotten"}
-# }
-# @st.cache_resource
-# def load_classification_models():
-# """Load and cache the classification models."""
-# try:
-# model_name = load_model(MODEL_CONFIG['name_model'])
-# model_quality = load_model(MODEL_CONFIG['quality_model'])
-# return model_name, model_quality
-# except Exception as e:
-# st.error(f"Error loading classification models: {str(e)}")
-# return None, None
-# @st.cache_resource
-# def load_detectron_model(fruit_name: str):
-# """Load and cache the Detectron2 model for damage detection."""
-# try:
-# cfg = get_cfg()
-# config_path = Path(f"{fruit_name.lower()}_config.yaml")
-# model_path = Path(f"{fruit_name}_model.pth")
+# # import streamlit as st
+# # import numpy as np
+# # import cv2
+# # import warnings
+# # import os
+# # from pathlib import Path
+# # from PIL import Image
+# # import tensorflow as tf
+# # from tensorflow.keras.models import load_model
+# # from tensorflow.keras.preprocessing import image
+# # from detectron2.engine import DefaultPredictor
+# # from detectron2.config import get_cfg
+# # from detectron2.utils.visualizer import Visualizer
+# # from detectron2.data import MetadataCatalog
+
+# # # Suppress warnings
+# # warnings.filterwarnings("ignore")
+# # tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+# # # Configuration
+# # MODEL_CONFIG = {
+# # 'name_model': 'name_model_inception.h5',
+# # 'quality_model': 'type_model_inception.h5',
+# # 'input_size': (224, 224),
+# # 'score_threshold': 0.5
+# # }
+
+# # LABEL_MAPS = {
+# # 'name': {
+# # 0: "Banana", 1: "Cucumber", 2: "Grape", 3: "Kaki", 4: "Papaya",
+# # 5: "Peach", 6: "Pear", 7: "Peeper", 8: "Strawberry", 9: "Watermelon",
+# # 10: "tomato"
+# # },
+# # 'quality': {0: "Good", 1: "Mild", 2: "Rotten"}
+# # }
+
+# # @st.cache_resource
+# # def load_classification_models():
+# # """Load and cache the classification models."""
+# # try:
+# # model_name = load_model(MODEL_CONFIG['name_model'])
+# # model_quality = load_model(MODEL_CONFIG['quality_model'])
+# # return model_name, model_quality
+# # except Exception as e:
+# # st.error(f"Error loading classification models: {str(e)}")
+# # return None, None
+
+# # @st.cache_resource
+# # def load_detectron_model(fruit_name: str):
+# # """Load and cache the Detectron2 model for damage detection."""
+# # try:
+# # cfg = get_cfg()
+# # config_path = Path(f"{fruit_name.lower()}_config.yaml")
+# # model_path = Path(f"{fruit_name}_model.pth")
-# if not config_path.exists() or not model_path.exists():
-# raise FileNotFoundError(f"Model files not found for {fruit_name}")
+# # if not config_path.exists() or not model_path.exists():
+# # raise FileNotFoundError(f"Model files not found for {fruit_name}")
-# cfg.merge_from_file(str(config_path))
-# cfg.MODEL.WEIGHTS = str(model_path)
-# cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = MODEL_CONFIG['score_threshold']
-# cfg.MODEL.DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+# # cfg.merge_from_file(str(config_path))
+# # cfg.MODEL.WEIGHTS = str(model_path)
+# # cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = MODEL_CONFIG['score_threshold']
+# # cfg.MODEL.DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
-# return DefaultPredictor(cfg), cfg
-# except Exception as e:
-# st.error(f"Error loading Detectron2 model: {str(e)}")
-# return None, None
-
-# def preprocess_image(img: np.ndarray) -> tuple:
-# """Preprocess the input image for model prediction."""
-# try:
-# # Convert to PIL Image if necessary
-# if isinstance(img, np.ndarray):
-# img = Image.fromarray(img.astype('uint8'), 'RGB')
+# # return DefaultPredictor(cfg), cfg
+# # except Exception as e:
+# # st.error(f"Error loading Detectron2 model: {str(e)}")
+# # return None, None
+
+# # def preprocess_image(img: np.ndarray) -> tuple:
+# # """Preprocess the input image for model prediction."""
+# # try:
+# # # Convert to PIL Image if necessary
+# # if isinstance(img, np.ndarray):
+# # img = Image.fromarray(img.astype('uint8'), 'RGB')
-# # Resize and prepare for model input
-# img_resized = img.resize(MODEL_CONFIG['input_size'])
-# img_array = image.img_to_array(img_resized)
-# img_expanded = np.expand_dims(img_array, axis=0)
-# img_normalized = img_expanded / 255.0
+# # # Resize and prepare for model input
+# # img_resized = img.resize(MODEL_CONFIG['input_size'])
+# # img_array = image.img_to_array(img_resized)
+# # img_expanded = np.expand_dims(img_array, axis=0)
+# # img_normalized = img_expanded / 255.0
-# return img_normalized, img_resized
-# except Exception as e:
-# st.error(f"Error preprocessing image: {str(e)}")
-# return None, None
-
-# def predict_fruit(img: np.ndarray) -> tuple:
-# """Predict fruit type and quality."""
-# model_name, model_quality = load_classification_models()
-# if model_name is None or model_quality is None:
-# return None, None, None
+# # return img_normalized, img_resized
+# # except Exception as e:
+# # st.error(f"Error preprocessing image: {str(e)}")
+# # return None, None
+
+# # def predict_fruit(img: np.ndarray) -> tuple:
+# # """Predict fruit type and quality."""
+# # model_name, model_quality = load_classification_models()
+# # if model_name is None or model_quality is None:
+# # return None, None, None
-# img_normalized, img_resized = preprocess_image(img)
-# if img_normalized is None:
-# return None, None, None
+# # img_normalized, img_resized = preprocess_image(img)
+# # if img_normalized is None:
+# # return None, None, None
-# try:
-# # Make predictions
-# pred_name = model_name.predict(img_normalized)
-# pred_quality = model_quality.predict(img_normalized)
+# # try:
+# # # Make predictions
+# # pred_name = model_name.predict(img_normalized)
+# # pred_quality = model_quality.predict(img_normalized)
-# # Get predicted labels
-# predicted_name = LABEL_MAPS['name'][np.argmax(pred_name, axis=1)[0]]
-# predicted_quality = LABEL_MAPS['quality'][np.argmax(pred_quality, axis=1)[0]]
+# # # Get predicted labels
+# # predicted_name = LABEL_MAPS['name'][np.argmax(pred_name, axis=1)[0]]
+# # predicted_quality = LABEL_MAPS['quality'][np.argmax(pred_quality, axis=1)[0]]
-# return predicted_name, predicted_quality, img_resized
-# except Exception as e:
-# st.error(f"Error making predictions: {str(e)}")
-# return None, None, None
-
-# def detect_damage(img: Image, fruit_name: str) -> np.ndarray:
-# """Detect and visualize damage in the fruit image."""
-# predictor, cfg = load_detectron_model(fruit_name)
-# if predictor is None or cfg is None:
-# return None
+# # return predicted_name, predicted_quality, img_resized
+# # except Exception as e:
+# # st.error(f"Error making predictions: {str(e)}")
+# # return None, None, None
+
+# # def detect_damage(img: Image, fruit_name: str) -> np.ndarray:
+# # """Detect and visualize damage in the fruit image."""
+# # predictor, cfg = load_detectron_model(fruit_name)
+# # if predictor is None or cfg is None:
+# # return None
-# try:
-# outputs = predictor(cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR))
-# v = Visualizer(np.array(img), MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=0.8)
-# out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-# return out.get_image()
-# except Exception as e:
-# st.error(f"Error in damage detection: {str(e)}")
-# return None
-
-# def main():
-# st.set_page_config(page_title="Fruit Quality Analysis", layout="wide")
+# # try:
+# # outputs = predictor(cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR))
+# # v = Visualizer(np.array(img), MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=0.8)
+# # out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+# # return out.get_image()
+# # except Exception as e:
+# # st.error(f"Error in damage detection: {str(e)}")
+# # return None
+
+# # def main():
+# # st.set_page_config(page_title="Fruit Quality Analysis", layout="wide")
-# st.title("Automated Fruits Monitoring System")
-# st.write("Upload an image of a fruit to detect its type, quality, and potential damage.")
+# # st.title("Automated Fruits Monitoring System")
+# # st.write("Upload an image of a fruit to detect its type, quality, and potential damage.")
-# uploaded_file = st.file_uploader("Choose a fruit image...", type=["jpg", "jpeg", "png"])
+# # uploaded_file = st.file_uploader("Choose a fruit image...", type=["jpg", "jpeg", "png"])
-# if uploaded_file is not None:
-# # Create two columns for layout
-# col1, col2 = st.columns(2)
+# # if uploaded_file is not None:
+# # # Create two columns for layout
+# # col1, col2 = st.columns(2)
-# # Display uploaded image
-# image = Image.open(uploaded_file)
-# col1.image(image, caption="Uploaded Image", use_column_width=True)
+# # # Display uploaded image
+# # image = Image.open(uploaded_file)
+# # col1.image(image, caption="Uploaded Image", use_column_width=True)
-# if col1.button("Analyze"):
-# with st.spinner("Analyzing image..."):
-# predicted_name, predicted_quality, img_resized = predict_fruit(np.array(image))
+# # if col1.button("Analyze"):
+# # with st.spinner("Analyzing image..."):
+# # predicted_name, predicted_quality, img_resized = predict_fruit(np.array(image))
-# if predicted_name and predicted_quality:
-# # Display results
-# col2.markdown("### Analysis Results")
-# col2.markdown(f"**Fruit Type:** {predicted_name}")
-# col2.markdown(f"**Quality:** {predicted_quality}")
+# # if predicted_name and predicted_quality:
+# # # Display results
+# # col2.markdown("### Analysis Results")
+# # col2.markdown(f"**Fruit Type:** {predicted_name}")
+# # col2.markdown(f"**Quality:** {predicted_quality}")
-# # Check if damage detection is needed
-# if (predicted_name.lower() in LABEL_MAPS['name'].values() and
-# predicted_quality in ["Mild", "Rotten"]):
+# # # Check if damage detection is needed
+# # if (predicted_name.lower() in LABEL_MAPS['name'].values() and
+# # predicted_quality in ["Mild", "Rotten"]):
-# col2.markdown("### Damage Detection")
-# damage_image = detect_damage(img_resized, predicted_name)
+# # col2.markdown("### Damage Detection")
+# # damage_image = detect_damage(img_resized, predicted_name)
-# if damage_image is not None:
-# col2.image(damage_image, caption="Detected Damage Regions",
-# use_column_width=True)
+# # if damage_image is not None:
+# # col2.image(damage_image, caption="Detected Damage Regions",
+# # use_column_width=True)
-# # Add download button for the damage detection result
-# col2.download_button(
-# label="Download Analysis Result",
-# data=cv2.imencode('.png', damage_image)[1].tobytes(),
-# file_name=f"{predicted_name}_damage_analysis.png",
-# mime="image/png"
-# )
-
-# if __name__ == "__main__":
-# main()
\ No newline at end of file
+# # # Add download button for the damage detection result
+# # col2.download_button(
+# # label="Download Analysis Result",
+# # data=cv2.imencode('.png', damage_image)[1].tobytes(),
+# # file_name=f"{predicted_name}_damage_analysis.png",
+# # mime="image/png"
+# # )
+
+# # if __name__ == "__main__":
+# # main()
\ No newline at end of file