Spaces:
Sleeping
Sleeping
Commit ·
8bd478d
1
Parent(s): 902a329
Private change
Browse files- app.py +68 -250
- requirements.txt +5 -2
app.py
CHANGED
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@@ -1,19 +1,37 @@
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import
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import random
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import torch
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from transformers import (
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AutoTokenizer, AutoModelForCausalLM,
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T5Tokenizer,
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T5ForConditionalGeneration,
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pipeline
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)
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cls_model_name = "yangy50/garbage-classification"
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classifier = pipeline("image-classification", model=cls_model_name)
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def classify_image(image):
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preds = classifier(image)
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results = {}
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for item in preds[:3]:
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label = item["label"]
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return results
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#
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tiny_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
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tokenizer = AutoTokenizer.from_pretrained(tiny_model)
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chat_model = AutoModelForCausalLM.from_pretrained(
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tiny_model,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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low_cpu_mem_usage=True
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pipe = pipeline(
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"text-generation",
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model=chat_model,
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tokenizer=tokenizer,
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device_map="auto",
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max_new_tokens=
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)
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def clean_chat_output(full_text):
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if "<|assistant|>" in full_text:
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full_text = full_text.split("<|assistant|>")[-1]
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lines = full_text.strip().split("\n")
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if lines[0].lower().startswith("item:"):
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lines = lines[1:]
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return "\n".join(lines).strip()
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def explain_recycling(class_label):
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system_msg = {
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"role": "system",
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@@ -67,18 +89,18 @@ def explain_recycling(class_label):
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"content": f"Item: {class_label}\nReturn the two bullet points now."
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}
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messages = [system_msg, user_msg]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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outputs = pipe(
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prompt,
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max_new_tokens=
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do_sample=True,
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top_p
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temperature=0.3
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)
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return clean_chat_output(raw)
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#
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},
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"glass": {
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"Recycling type": "Glass recycling",
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"Disposal": "Rinse glass bottles and jars, remove lids, and place them in a dedicated glass recycling container.",
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"Tips": "Avoid breaking the glass; broken pieces can be hazardous and may complicate processing.",
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"Extra": "Colored glass is often recycled separately; check local recycling rules."
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},
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"metal": {
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"Recycling type": "Metal recycling",
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"Disposal": "Rinse aluminum cans and steel containers, remove labels if possible, and put them in the metal recycling bin.",
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"Tips": "Crush cans to save space and facilitate transport.",
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"Extra": "Avoid contaminating with food waste to improve recycling efficiency."
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},
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"paper": {
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"Recycling type": "Paper recycling",
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"Disposal": "Sort newspapers, office paper, and magazines into the paper recycling bin; remove plastic coatings and staples.",
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"Tips": "Shred sensitive documents but keep shredded paper in a bag to avoid scattering.",
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"Extra": "Do not recycle wet or food-stained paper."
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},
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"plastic": {
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"Recycling type": "Plastic recycling",
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"Disposal": "Rinse plastic bottles, containers, and packaging, and place them in the plastic recycling bin.",
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"Tips": "Check recycling codes; some plastics (like #3 PVC) are not widely recyclable.",
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"Extra": "Remove caps and labels when possible to improve sorting efficiency."
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},
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"trash": {
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"Recycling type": "General waste / landfill",
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"Disposal": "Place non-recyclable items in the regular trash bin; avoid mixing with recyclables.",
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"Tips": "Separate hazardous materials like batteries, electronics, or chemicals for special disposal.",
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"Extra": "Try to reduce overall waste by reusing and composting when possible."
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}
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}
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waste_analyzation_v2 = {
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"cardboard": {
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"Recycling type": "Paper/Cardboard recycling",
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"Disposal": "Fold or cut cardboard into smaller flat pieces so it fits properly in recycling bins.",
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"Tips": "Keep cardboard free from oils or food stains to avoid contaminating the batch.",
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"Extra": "Cardboard with shiny or laminated surfaces may need separate processing."
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},
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"glass": {
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"Recycling type": "Glass recycling",
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"Disposal": "Remove lids and caps, lightly rinse, and place glass containers into the glass-only bin.",
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"Tips": "Do not place broken drinking glasses or ceramics with recyclable glass—they melt at different temperatures.",
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"Extra": "Some regions require taking glass to drop-off centers instead of curbside bins."
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},
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"metal": {
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"Recycling type": "Metal recycling",
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"Disposal": "Empty metal containers and place them in the metal bin; labels may stay on.",
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"Tips": "Keep metal separate from electronics or batteries, which require special facilities.",
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"Extra": "Large metal objects like appliances may require bulk recycling pickup."
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},
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"paper": {
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"Recycling type": "Paper recycling",
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"Disposal": "Place clean, dry paper products such as envelopes and notebooks into paper bins.",
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"Tips": "Do not recycle coated paper (e.g., laminated or plastic-lined pages).",
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"Extra": "Paper with heavy ink coverage might be processed separately."
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},
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"plastic": {
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"Recycling type": "Plastic recycling",
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"Disposal": "Clean and drain plastic bottles or tubs, then place them in the plastics bin.",
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"Tips": "Flatten plastic bottles to reduce volume unless your local guidelines say otherwise.",
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"Extra": "Some plastics like polystyrene foam require special drop-off locations."
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},
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"trash": {
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"Recycling type": "General waste / landfill",
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"Disposal": "Dispose of unrecyclable materials in regular waste; secure loose items in bags.",
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"Tips": "Separate hazardous or toxic waste like paint, solvents, or chemicals.",
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"Extra": "Reduce landfill impact by choosing reusable products whenever possible."
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}
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}
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waste_analyzation_v3 = {
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"cardboard": {
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"Recycling type": "Paper/Cardboard recycling",
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"Disposal": "Remove packing tape when possible and place cardboard in dry storage until pickup day.",
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"Tips": "Avoid leaving cardboard outdoors where rain could weaken fibers and ruin recyclability.",
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"Extra": "Food-contaminated cardboard can often be composted instead of recycled."
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},
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"glass": {
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"Recycling type": "Glass recycling",
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"Disposal": "Sort by color only if required locally; rinse lightly and drop into the proper bin.",
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"Tips": "Handle carefully to avoid breakage, as shattered glass is often not recyclable in curbside programs.",
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"Extra": "Glass jars with metal clasps or rubber seals may need partial disassembly before recycling."
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},
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"metal": {
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"Recycling type": "Metal recycling",
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"Disposal": "Ensure metal food cans are clean and empty before placing in the recycling bin.",
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"Tips": "Rinse cans briefly—no need for perfect cleaning—as this prevents pests and odors.",
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"Extra": "Metal lids from jars should be recycled separately from the glass container."
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},
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"paper": {
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"Recycling type": "Paper recycling",
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"Disposal": "Place paper in the appropriate bin; keep shredded paper in a paper bag if accepted.",
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"Tips": "Avoid mixing paper with wet waste like food scraps to maintain recyclability.",
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"Extra": "Sticky notes and small scraps may or may not be accepted depending on the facility."
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},
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"plastic": {
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"Recycling type": "Plastic recycling",
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"Disposal": "Recycle only plastics accepted by your local program; rinse and drain them completely.",
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"Tips": "Avoid recycling small plastic pieces under 2 inches—they may jam sorting machinery.",
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"Extra": "Hard-to-recycle plastics may be collected through special community programs."
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},
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"trash": {
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"Recycling type": "General waste / landfill",
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"Disposal": "Place all non-recyclables in the trash bin; tie bags securely to avoid leakage.",
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"Tips": "Do not place batteries, electronics, or sharp objects directly into household trash.",
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"Extra": "Consider composting organic waste to reduce household trash volume."
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}
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}
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analysis = [waste_analyzation_v3, waste_analyzation_v2, waste_analyzation]
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def classify_pipeline(image):
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global top_label
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predictions = classify_image(image)
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top_label = max(predictions, key=predictions.get)
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return predictions
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def analyze_pipeline():
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global top_label
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if top_label is None:
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return "Please classify an image first."
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explanation = explain_recycling(top_label)
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choice = random.randint(0, 2)
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info = analysis[choice][top_label]
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result = (
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f"• Recycling type: {info['Recycling type']}\n"
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f"• Disposal: {info['Disposal']}\n"
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return result
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# ------------------ GRADIO UI ------------------
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# =========================
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# CSS
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# =========================
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custom_css = """
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#main-title {
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text-align: center;
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color: #2E7D32;
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font-size: 34px;
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font-weight: 800;
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margin-bottom: 22px;
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}
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.gradio-container {
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background: linear-gradient(135deg, #E8F5E9 0%, #F1F8E9 100%);
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font-family: 'Segoe UI', sans-serif;
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}
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#explainbox textarea {
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background: #ffffff;
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height: 120px;
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border: 2px solid #A5D6A7;
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border-radius: 12px;
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padding: 12px;
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font-size: 15px;
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}
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.gr-button.primary {
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background: #43A047 !important;
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color: white !important;
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border-radius: 12px !important;
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padding: 12px 20px !important;
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font-size: 17px !important;
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box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.15);
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transition: 0.2s ease;
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}
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textarea, .gr-textbox textarea, #explainbox textarea {
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color: #1B5E20 !important;
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}
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#tips-box li {
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color: #2E7D32 !important;
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}
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.gr-button.primary:hover {
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background: #2E7D32 !important;
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transform: translateY(-2px);
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}
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"""
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# =========================
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#
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# =========================
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# INPUT ROW
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with gr.Row():
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with gr.Column(scale=1):
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img_input = gr.Image(
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type="pil",
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label="📸 Upload waste image",
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elem_id="upload-area"
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)
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with gr.Column(scale=1):
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gr.Markdown(
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"""
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<div id="tips-box" style="border:2px solid #A5D6A7; padding:16px; border-radius:14px; background:white;">
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<h3 style="color:#2E7D32;">🌍 Quick recycling tips:</h3>
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<ul>
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<li>Organic waste → green bin</li>
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<li>Plastic, metal waste → recycle</li>
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<li>Wash your glass waste!</li>
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<li>Battery, eletrical devices → non-metal container</li>
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</ul>
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</div>
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"""
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)
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# OUTPUTS
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cls_output = gr.Label(
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num_top_classes=3,
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label="🔍 Classifier Prediction (Top 3)"
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)
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analyze_btn = gr.Button("Classify Waste", variant="primary")
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inputs=img_input,
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outputs=cls_output
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)
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explain_output = gr.Textbox(
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label="🧩 Detailed Recycling & Disposal Advice",
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elem_id="explainbox",
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lines=6
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)
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# BUTTON
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analyze_result_btn = gr.Button("Analyze", variant="primary")
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analyze_result_btn.click(
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analyze_pipeline,
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inputs=None,
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outputs=explain_output
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)
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import base64
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from io import BytesIO
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from PIL import Image
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from fastapi import FastAPI
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from pydantic import BaseModel
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import random
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import torch
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from transformers import (
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AutoTokenizer, AutoModelForCausalLM,
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pipeline
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)
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# =========================
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# FASTAPI APP
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# =========================
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app = FastAPI()
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# =========================
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# API MODELS
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# =========================
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class APIImage(BaseModel):
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image_base64: str
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# =========================
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# LOAD CLASSIFIER
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# =========================
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cls_model_name = "yangy50/garbage-classification"
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classifier = pipeline("image-classification", model=cls_model_name)
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def classify_image(image):
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preds = classifier(image)
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results = {}
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for item in preds[:3]:
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label = item["label"]
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return results
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# =========================
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# LOAD CHAT MODEL
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# =========================
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tiny_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
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tokenizer = AutoTokenizer.from_pretrained(tiny_model)
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chat_model = AutoModelForCausalLM.from_pretrained(
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tiny_model,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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low_cpu_mem_usage=True
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)
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pipe = pipeline(
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"text-generation",
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model=chat_model,
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tokenizer=tokenizer,
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device_map="auto",
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max_new_tokens=80
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)
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+
def clean_chat_output(full_text):
|
| 66 |
if "<|assistant|>" in full_text:
|
| 67 |
full_text = full_text.split("<|assistant|>")[-1]
|
| 68 |
|
| 69 |
lines = full_text.strip().split("\n")
|
| 70 |
+
if lines and lines[0].lower().startswith("item:"):
|
| 71 |
lines = lines[1:]
|
| 72 |
|
| 73 |
return "\n".join(lines).strip()
|
| 74 |
|
| 75 |
+
|
| 76 |
def explain_recycling(class_label):
|
| 77 |
system_msg = {
|
| 78 |
"role": "system",
|
|
|
|
| 89 |
"content": f"Item: {class_label}\nReturn the two bullet points now."
|
| 90 |
}
|
| 91 |
messages = [system_msg, user_msg]
|
| 92 |
+
|
| 93 |
prompt = tokenizer.apply_chat_template(
|
| 94 |
+
messages,
|
| 95 |
+
tokenize=False,
|
| 96 |
add_generation_prompt=True
|
| 97 |
)
|
| 98 |
|
| 99 |
outputs = pipe(
|
| 100 |
prompt,
|
| 101 |
+
max_new_tokens=80,
|
| 102 |
do_sample=True,
|
| 103 |
+
top_p=0.9,
|
| 104 |
temperature=0.3
|
| 105 |
)
|
| 106 |
|
|
|
|
| 108 |
return clean_chat_output(raw)
|
| 109 |
|
| 110 |
|
| 111 |
+
# =========================
|
| 112 |
+
# WASTE ANALYSIS
|
| 113 |
+
# (your 3 versions)
|
| 114 |
+
# =========================
|
| 115 |
+
waste_analyzation = {...} # KEEP SAME CONTENTS
|
| 116 |
+
waste_analyzation_v2 = {...}
|
| 117 |
+
waste_analyzation_v3 = {...}
|
| 118 |
+
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|
| 119 |
analysis = [waste_analyzation_v3, waste_analyzation_v2, waste_analyzation]
|
| 120 |
|
| 121 |
+
top_label = None
|
| 122 |
+
|
| 123 |
+
|
| 124 |
+
# =========================
|
| 125 |
+
# CLASSIFICATION PIPELINES
|
| 126 |
+
# =========================
|
| 127 |
def classify_pipeline(image):
|
| 128 |
global top_label
|
|
|
|
| 129 |
predictions = classify_image(image)
|
| 130 |
+
top_label = max(predictions, key=predictions.get)
|
| 131 |
return predictions
|
| 132 |
|
| 133 |
+
|
| 134 |
def analyze_pipeline():
|
| 135 |
global top_label
|
| 136 |
|
| 137 |
if top_label is None:
|
| 138 |
return "Please classify an image first."
|
| 139 |
+
|
|
|
|
|
|
|
| 140 |
choice = random.randint(0, 2)
|
| 141 |
+
info = analysis[choice][top_label]
|
| 142 |
+
|
| 143 |
result = (
|
| 144 |
f"• Recycling type: {info['Recycling type']}\n"
|
| 145 |
f"• Disposal: {info['Disposal']}\n"
|
|
|
|
| 149 |
return result
|
| 150 |
|
| 151 |
|
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|
|
|
|
| 152 |
# =========================
|
| 153 |
+
# API ENDPOINTS
|
| 154 |
# =========================
|
| 155 |
+
@app.post("/api/classify")
|
| 156 |
+
def api_classify(data: APIImage):
|
| 157 |
+
img_bytes = base64.b64decode(data.image_base64)
|
| 158 |
+
image = Image.open(BytesIO(img_bytes)).convert("RGB")
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 159 |
|
| 160 |
+
preds = classify_pipeline(image)
|
| 161 |
+
return {"predictions": preds}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 162 |
|
| 163 |
|
| 164 |
+
@app.post("/api/analyze")
|
| 165 |
+
def api_analyze():
|
| 166 |
+
result = analyze_pipeline()
|
| 167 |
+
return {"result": result}
|
requirements.txt
CHANGED
|
@@ -1,8 +1,11 @@
|
|
| 1 |
-
huggingface_hub
|
| 2 |
gradio
|
| 3 |
numpy
|
| 4 |
Pillow
|
| 5 |
torch
|
| 6 |
transformers
|
| 7 |
sentencepiece
|
| 8 |
-
accelerate
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
gradio
|
| 2 |
numpy
|
| 3 |
Pillow
|
| 4 |
torch
|
| 5 |
transformers
|
| 6 |
sentencepiece
|
| 7 |
+
accelerate
|
| 8 |
+
|
| 9 |
+
fastapi
|
| 10 |
+
uvicorn
|
| 11 |
+
pydantic
|