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README.md

@@ -1,14 +1,13 @@
 ---
-title: Ccr Colorado
-emoji: 💬
+title: Fire And Smoke
+emoji: 🐢
 colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
+colorTo: red
+sdk: streamlit
+sdk_version: 1.28.2
 app_file: app.py
 pinned: false
-license: apache-2.0
-short_description: csrs in colorado
+license: mit
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,48 +1,114 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from PIL import Image
-import torch
-import pdfplumber
-
-# Load the model and tokenizer
-model_name = "deepseek-ai/Janus-1.3B"
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-def process_input(input_data):
-    if isinstance(input_data, str):
-        return handle_text(input_data)
-    elif isinstance(input_data, Image.Image):
-        return handle_image(input_data)
-    elif isinstance(input_data, dict) and "name" in input_data:
-        return handle_pdf(input_data["name"])
-    else:
-        return "Unsupported input type."
-
-def handle_text(text):
-    inputs = tokenizer(text, return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=100)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-def handle_image(image):
-    return "Image processing not implemented yet."
-
-def handle_pdf(pdf_path):
-    with pdfplumber.open(pdf_path) as pdf:
-        text = "\n".join([page.extract_text() for page in pdf.pages if page.extract_text()])
-    return handle_text(text)
-
-# Create Gradio app
-iface = gr.Interface(
-    fn=process_input,
-    inputs=[
-        gr.Textbox(label="Enter text"),
-        gr.Image(label="Upload image"),
-        gr.File(label="Upload PDF")
-    ],
-    outputs=gr.Textbox(),
-    title="Multimodal Chatbot",
-    description="Handles text, images, and PDFs with the same entry point."
+# Import required libraries
+import PIL
+import cv2
+import streamlit as st
+from ultralytics import YOLO
+import tempfile
+
+
+# Replace the relative path to your weight file
+model_path = 'https://huggingface.co/spaces/ankitkupadhyay/fire_and_smoke/blob/main/best.pt'
+
+# Setting page layout
+st.set_page_config(
+    page_title="WildfireWatch",  # Setting page title
+    page_icon="🔥",     # Setting page icon
+    layout="wide",      # Setting layout to wide
+    initial_sidebar_state="expanded"    # Expanding sidebar by default
 )
 
-iface.launch()
+# Creating sidebar
+with st.sidebar:
+    st.header("IMAGE/VIDEO UPLOAD")     # Adding header to sidebar
+    # Adding file uploader to sidebar for selecting images and videos
+    source_file = st.file_uploader(
+        "Choose an image or video...", type=("jpg", "jpeg", "png", 'bmp', 'webp', 'mp4'))
+
+    # Model Options
+    confidence = float(st.slider(
+        "Select Model Confidence", 25, 100, 40)) / 100
+
+# Creating main page heading
+st.title("WildfireWatch: Detecting Wildfire using AI")
+
+# Adding informative pictures and description about the motivation for the app
+col1, col2 = st.columns(2)
+with col1:
+    st.image("https://huggingface.co/spaces/ankitkupadhyay/fire_and_smoke/resolve/main/Fire_1.jpeg", use_column_width=True)
+with col2:
+    st.image("https://huggingface.co/spaces/ankitkupadhyay/fire_and_smoke/resolve/main/Fire_2.jpeg", use_column_width=True)
+
+st.markdown("""
+    Wildfires are a major environmental issue, causing substantial losses to ecosystems, human livelihoods, and potentially leading to loss of life. Early detection of wildfires can prevent these losses. Our application, WildfireWatch, uses state-of-the-art YOLOv8 model for real-time wildfire and smoke detection in images and videos.
+""")
+
+st.markdown("---")  # Adding a horizontal line
+
+st.header("Let's Detect Wildfire")
+
+# Creating two columns on the main page
+col1, col2 = st.columns(2)
+
+# Adding image to the first column if image is uploaded
+with col1:
+    if source_file:
+        # Check if the file is an image
+        if source_file.type.split('/')[0] == 'image':
+            # Opening the uploaded image
+            uploaded_image = PIL.Image.open(source_file)
+            # Adding the uploaded image to the page with a caption
+            st.image(source_file,
+                     caption="Uploaded Image",
+                     use_column_width=True
+                     )
+        else:
+            tfile = tempfile.NamedTemporaryFile(delete=False) 
+            tfile.write(source_file.read())
+            vidcap = cv2.VideoCapture(tfile.name)
+
+try:
+    model = YOLO(model_path)
+except Exception as ex:
+    st.error(
+        f"Unable to load model. Check the specified path: {model_path}")
+    st.error(ex)
+
+if st.sidebar.button('Let\'s Detect Wildfire'):
+    if source_file.type.split('/')[0] == 'image':
+        res = model.predict(uploaded_image,
+                            conf=confidence
+                            )
+        boxes = res[0].boxes
+        res_plotted = res[0].plot()[:, :, ::-1]
+        with col2:
+            st.image(res_plotted,
+                     caption='Detected Image',
+                     use_column_width=True
+                     )
+            try:
+                with st.expander("Detection Results"):
+                    for box in boxes:
+                        st.write(box.xywh)
+            except Exception as ex:
+                st.write("No image is uploaded yet!")
+    else:
+        # Open the video file
+        success, image = vidcap.read()
+        while success:
+            res = model.predict(image,
+                                conf=confidence
+                                )
+            boxes = res[0].boxes
+            res_plotted = res[0].plot()[:, :, ::-1]
+            with col2:
+                st.image(res_plotted,
+                         caption='Detected Frame',
+                         use_column_width=True
+                         )
+                try:
+                    with st.expander("Detection Results"):
+                        for box in boxes:
+                            st.write(box.xywh)
+                except Exception as ex:
+                    st.write("No video is uploaded yet!")
+            success, image = vidcap.read()

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05a5e990d1bcb3e4f2b6f38d48baffba4418baf65f3d426af0cecce43ecd4eab
+size 6236761

requirements.txt

@@ -1,5 +1,5 @@
-transformers
-torch
-gradio
-pillow
-pdfplumber
+streamlit==1.29.0
+torch==2.1.0
+Pillow==9.4.0
+opencv-python-headless==4.8.0.76
+ultralytics==8.0.221