Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import requests
+import io
+import base64
+
+# --- CONFIGURATION ---
+# Insert your Groq API key
+GROQ_API_KEY = "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"
+GROQ_API_URL = "https://api.groq.com/v1/inference/yolov8"
+
+# --- Streamlit UI ---
+st.title("📸 Object Counter App")
+st.write("Upload an image and count the objects using YOLOv8 + Groq API!")
+
+uploaded_file = st.file_uploader("Upload an Image", type=['jpg', 'jpeg', 'png'])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Convert image to base64 for API
+    buffered = io.BytesIO()
+    image.save(buffered, format="JPEG")
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+
+    st.write("🔎 Detecting objects...")
+
+    # --- Groq API Request ---
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": "yolov8n",  # YOLOv8 Nano version (fast & small)
+        "inputs": {
+            "image": img_str
+        }
+    }
+
+    response = requests.post(GROQ_API_URL, json=payload, headers=headers)
+
+    if response.status_code == 200:
+        result = response.json()
+        detected_objects = result.get("detections", [])
+        count = len(detected_objects)
+
+        st.success(f"✅ Total objects detected: {count}")
+
+        # Show each detected object's class
+        object_names = [obj['class'] for obj in detected_objects]
+        st.write(f"Detected items: {object_names}")
+
+        # Optional: show bounding boxes if API supports (not visualized here)
+
+    else:
+        st.error(f"Error: {response.status_code} - {response.text}")
+
+---
+
+st.write("Made with ❤️ using YOLOv8 + Groq + Streamlit")