the coin expert initial setup and build

README.md

@@ -12,3 +12,10 @@ short_description: Can identify valuable error coins and varieties.
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+# Coin Identification AI
+
+Upload a photo of a suspected error coin and tell us what you think it is. Coin Expert will fetch information, compare images, and help determine if your coin matches a known error or variety.
+
+Built with OpenAI Vision + Firebase.

app.py

@@ -1,7 +1,30 @@
 import gradio as gr
+from main import suspected_coin_name, user_image_path  # or refactor main logic into functions
 
-def greet(name):
-    return "Hello " + name + "!!"
+def analyze_coin(coin_name, image):
+    with open("uploaded_image.jpg", "wb") as f:
+        f.write(image.read())
+
+    # Assign file path to global variable for downstream logic
+    global user_image_path, suspected_coin_name
+    user_image_path = "uploaded_image.jpg"
+    suspected_coin_name = coin_name
+
+    # Import and re-run main logic
+    import main  # assumes main logic runs on import
+
+    return f"Uploaded {coin_name} and processed. See terminal or logs for detailed output."
+
+interface = gr.Interface(
+    fn=analyze_coin,
+    inputs=[
+        gr.Textbox(label="Enter the Coin Name (e.g. 1955 Lincoln Cent DDO-001)"),
+        gr.File(label="Upload a Coin Image")
+    ],
+    outputs="text",
+    title="Coin Expert - AI Coin Identifier",
+    description="Upload a photo of a suspected error coin and enter what you think it is. We'll try to match it and tell you if it's real."
+)
+
+interface.launch()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()

firebase_setup.py

@@ -0,0 +1,22 @@
+import os
+import firebase_admin
+from firebase_admin import credentials, storage
+
+def init_firebase():
+    if not firebase_admin._apps:
+        cred = credentials.Certificate({
+            "type": "service_account",
+            "project_id": os.getenv("FIREBASE_PROJECT_ID"),
+            "private_key_id": "ignored",
+            "private_key": os.getenv("FIREBASE_PRIVATE_KEY").replace("\\n", "\n"),
+            "client_email": os.getenv("FIREBASE_CLIENT_EMAIL"),
+            "client_id": "ignored",
+            "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+            "token_uri": "https://oauth2.googleapis.com/token",
+            "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+            "client_x509_cert_url": f"https://www.googleapis.com/robot/v1/metadata/x509/{os.getenv('FIREBASE_CLIENT_EMAIL').replace('@', '%40')}"
+        })
+
+        firebase_admin.initialize_app(cred, {
+            'storageBucket': f"{os.getenv('FIREBASE_PROJECT_ID')}.appspot.com"
+        })

main.py

@@ -0,0 +1,45 @@
+import os
+import openai
+import firebase_admin
+from firebase_admin import firestore
+from firebase_setup import init_firebase
+from utils.coin_tools import get_coin_data, save_coin_data
+from utils.image_tools import compare_images
+
+# Init Firebase
+init_firebase()
+db = firestore.client()
+
+# Init OpenAI
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# === USER INPUT (Simulated for now) ===
+user_image_path = "images/user_coin.jpg"
+suspected_coin_name = "1955 Lincoln Cent DDO-001"
+
+# === Step 1: Check if coin already exists in Firebase ===
+coin_doc = db.collection("coins").document(suspected_coin_name.replace(" ", "_")).get()
+
+if coin_doc.exists:
+    print(f"✅ Found {suspected_coin_name} in the database. Comparing now...")
+    ref_images = coin_doc.to_dict().get("reference_images", [])
+    result = compare_images(user_image_path, ref_images)
+    print(f"Match Confidence: {result['confidence']*100:.1f}%")
+else:
+    print(f"🔍 No match found in database. Performing web search...")
+
+    # === Step 2: Use web_search_preview tool to find data ===
+    response = openai.responses.create(
+        model="gpt-4.1",
+        tools=[{"type": "web_search_preview"}],
+        input=f"Find die marker info, image links, and estimated value for {suspected_coin_name}"
+    )
+
+    output_text = response.output[1]['content'][0]['text']
+    print("📄 AI Search Result:\n", output_text)
+
+    # === Step 3: Save AI result to Firebase ===
+    coin_data = get_coin_data(suspected_coin_name, output_text, user_image_path)
+    save_coin_data(suspected_coin_name, coin_data)
+
+    print(f"💾 Saved new coin info to Firebase under {suspected_coin_name}.")

requirements.txt

@@ -0,0 +1,6 @@
+openai
+firebase-admin
+requests
+pillow
+gradio
+python-dotenv

utils/coin_tools.py

@@ -0,0 +1,22 @@
+import os
+from firebase_admin import firestore, storage
+
+def get_coin_data(coin_name, description, image_path):
+    return {
+        "name": coin_name,
+        "description": description,
+        "reference_images": [upload_image_to_storage(image_path, coin_name)],
+        "source": "web_search_preview",
+    }
+
+def upload_image_to_storage(image_path, coin_name):
+    bucket = storage.bucket()
+    blob = bucket.blob(f"coin_references/{coin_name.replace(' ', '_')}.jpg")
+    blob.upload_from_filename(image_path)
+    blob.make_public()
+    return blob.public_url
+
+def save_coin_data(coin_name, data):
+    db = firestore.client()
+    doc_ref = db.collection("coins").document(coin_name.replace(" ", "_"))
+    doc_ref.set(data)

utils/image_tools.py

@@ -0,0 +1,39 @@
+import openai
+import requests
+
+# Use OpenAI Vision to compare a user-uploaded image to reference images
+def compare_images(user_image_path, reference_image_urls):
+    with open(user_image_path, "rb") as user_img:
+        user_bytes = user_img.read()
+
+    # Download the first reference image for comparison
+    if not reference_image_urls:
+        return {"confidence": 0.0, "reason": "No reference images available."}
+
+    ref_response = requests.get(reference_image_urls[0])
+    if ref_response.status_code != 200:
+        return {"confidence": 0.0, "reason": "Failed to fetch reference image."}
+
+    ref_bytes = ref_response.content
+
+    # Use GPT-4 Vision to compare both images
+    response = openai.chat.completions.create(
+        model="gpt-4-vision-preview",
+        messages=[
+            {"role": "system", "content": "You are an expert in identifying error coins."},
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Do these images look like the same error coin?"},
+                    {"type": "image", "image": user_bytes},
+                    {"type": "image", "image": ref_bytes}
+                ]
+            }
+        ],
+        max_tokens=300
+    )
+
+    result_text = response.choices[0].message.content
+    # Very simple scoring logic based on confidence language
+    confidence = 0.8 if "yes" in result_text.lower() else 0.3
+    return {"confidence": confidence, "summary": result_text}