Delete app.py

app.py

@@ -1,123 +0,0 @@
-import streamlit as st
-import numpy as np
-from PIL import Image
-import torch
-from transformers import AutoModelForImageClassification, AutoImageProcessor
-
-# Set page config
-st.set_page_config(page_title="Enhanced Solar Panel Fault Detector", layout="wide")
-
-# Fault descriptions dictionary
-FAULT_DESCRIPTIONS = {
-    "shadowy": {
-        "description": "Shadowing occurs when parts of the panel are obscured by objects, reducing energy output.",
-        "maintenance": "Remove obstructing objects (e.g., branches, debris) and consider repositioning panels to avoid shadows."
-    },
-    "cracked": {
-        "description": "Cracks on the panel surface can lead to water leakage and reduced performance.",
-        "maintenance": "Replace the damaged panel to prevent further degradation and ensure safety."
-    },
-    "dusty": {
-        "description": "Dust or dirt accumulation on the panel surface reduces sunlight absorption.",
-        "maintenance": "Clean panels regularly with water and a soft cloth to restore efficiency."
-    },
-    "hot_spot": {
-        "description": "Hot spots indicate localized overheating, often due to defects or debris.",
-        "maintenance": "Inspect for debris or defects; consult a technician for potential panel replacement."
-    }
-}
-
-# Load model and processor
-@st.cache_resource
-def load_model():
-    model_name = "efficientnet-b0"  # Placeholder; replace with actual Hugging Face model
-    processor = AutoImageProcessor.from_pretrained(model_name)
-    model = AutoModelForImageClassification.from_pretrained(model_name)
-    return processor, model
-
-processor, model = load_model()
-
-# Class labels (based on multi-class classification)
-labels = ["normal", "shadowy", "cracked", "dusty", "hot_spot"]
-
-# Function to process image and predict
-def predict_fault(image):
-    inputs = processor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probabilities = torch.softmax(outputs.logits, dim=1)
-        predicted_idx = torch.argmax(probabilities, dim=1).item()
-        confidence = probabilities[0][predicted_idx].item()
-    return labels[predicted_idx], confidence
-
-# Streamlit UI
-st.title("Enhanced Solar Panel Fault Detector")
-st.markdown("Upload solar panel images to detect faults such as shadowing, cracks, dust, or hot spots. Get detailed insights and maintenance suggestions.")
-
-# Tabs for single and batch upload
-tab1, tab2 = st.tabs(["Single Image Analysis", "Batch Image Analysis"])
-
-with tab1:
-    st.subheader("Single Image Analysis")
-    uploaded_file = st.file_uploader("Upload a solar panel image", type=["jpg", "png", "jpeg"])
-    
-    if uploaded_file:
-        image = Image.open(uploaded_file).convert("RGB")
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-        
-        with st.spinner("Analyzing..."):
-            fault_type, confidence = predict_fault(image)
-        
-        st.success(f"**Prediction**: {fault_type.capitalize()} (Confidence: {confidence:.2%})")
-        
-        # Display fault details
-        if fault_type != "normal":
-            fault_details = FAULT_DESCRIPTIONS.get(fault_type, {})
-            with st.expander("Fault Details"):
-                st.write(f"**Description**: {fault_details.get('description', 'No description available.')}")
-                st.write(f"**Maintenance Tips**: {fault_details.get('maintenance', 'No maintenance tips available.')}")
-        else:
-            st.info("No faults detected. The panel appears to be in good condition.")
-
-with tab2:
-    st.subheader("Batch Image Analysis")
-    uploaded_files = st.file_uploader("Upload multiple solar panel images", type=["jpg", "png", "jpeg"], accept_multiple_files=True)
-    
-    if uploaded_files:
-        results = []
-        for uploaded_file in uploaded_files:
-            image = Image.open(uploaded_file).convert("RGB")
-            fault_type, confidence = predict_fault(image)
-            results.append({
-                "filename": uploaded_file.name,
-                "fault_type": fault_type,
-                "confidence": confidence,
-                "image": image
-            })
-        
-        st.subheader("Batch Analysis Results")
-        for result in results:
-            col1, col2 = st.columns([1, 2])
-            with col1:
-                st.image(result["image"], caption=result["filename"], width=200)
-            with col2:
-                st.write(f"**File**: {result['filename']}")
-                st.write(f"**Prediction**: {result['fault_type'].capitalize()} (Confidence: {result['confidence']:.2%})")
-                if result["fault_type"] != "normal":
-                    fault_details = FAULT_DESCRIPTIONS.get(result["fault_type"], {})
-                    st.write(f"**Description**: {fault_details.get('description', 'No description available.')}")
-                    st.write(f"**Maintenance Tips**: {fault_details.get('maintenance', 'No maintenance tips available.')}")
-                else:
-                    st.write("No faults detected.")
-
-# Sidebar with additional info
-st.sidebar.title("About")
-st.sidebar.markdown("""
-This app uses a deep learning model to detect faults in solar panels, such as shadowing, cracks, dust, or hot spots.
-Enhanced features include:
-- Multi-class fault detection
-- Confidence scores
-- Detailed fault descriptions and maintenance tips
-- Batch image processing
-""")
-st.sidebar.markdown("[Source Code](https://huggingface.co/spaces/your-username/Solar_Panel_Fault_Detector_Enhanced)")