updated

app.py

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+# app.py
+
+import torch
+import pickle
+import gradio as gr
+from model import NCFModel
+import numpy as np
+
+# Load encoders and user_positive_items
+with open('user_encoder.pkl', 'rb') as f:
+    user_encoder = pickle.load(f)
+
+with open('item_encoder.pkl', 'rb') as f:
+    item_encoder = pickle.load(f)
+
+with open('user_positive_items.pkl', 'rb') as f:
+    user_positive_items = pickle.load(f)
+
+# Load the trained model
+class NCFModelWrapper:
+    def __init__(self, model_path, num_users, num_items, embedding_size=50, device='cpu'):
+        self.device = torch.device(device)
+        self.model = NCFModel(num_users, num_items, embedding_size=embedding_size).to(self.device)
+        self.model.load_state_dict(torch.load(model_path, map_location=self.device))
+        self.model.eval()
+    
+    def predict(self, user, item):
+        with torch.no_grad():
+            user = torch.tensor([user], dtype=torch.long).to(self.device)
+            item = torch.tensor([item], dtype=torch.long).to(self.device)
+            output = self.model(user, item)
+            score = torch.sigmoid(output).item()
+        return score
+
+# Determine number of users and items from encoders
+num_users = len(user_encoder.classes_)
+num_items = len(item_encoder.classes_)
+
+# Initialize the model
+model = NCFModelWrapper(
+    model_path='best_ncf_model.pth',
+    num_users=num_users,
+    num_items=num_items,
+    embedding_size=50,  # Ensure this matches your trained model
+    device='cpu'  # Change to 'cuda' if GPU is available and desired
+)
+
+def recommend(user_id, num_recommendations=5):
+    """
+    Given a user ID, recommend top N items.
+    """
+    try:
+        user = user_encoder.transform([user_id])[0]
+    except:
+        return f"User ID '{user_id}' not found."
+    
+    # Get items the user has interacted with
+    pos_items = user_positive_items.get(user, set())
+    
+    # Get all possible items
+    all_items = set(range(num_items))
+    
+    # Candidate items are those not interacted with
+    candidate_items = list(all_items - pos_items)
+    
+    # Predict scores for candidate items
+    scores = []
+    for item in candidate_items:
+        score = model.predict(user, item)
+        scores.append((item, score))
+    
+    # Sort items based on score
+    scores.sort(key=lambda x: x[1], reverse=True)
+    
+    # Get top N recommendations
+    top_items = scores[:num_recommendations]
+    recommendations = []
+    for item_id, score in top_items:
+        original_item_id = item_encoder.inverse_transform([item_id])[0]
+        recommendations.append(f"Item ID: {original_item_id} (Score: {score:.4f})")
+    
+    return "\n".join(recommendations)
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=recommend,
+    inputs=[
+        gr.inputs.Textbox(lines=1, placeholder="Enter User ID", label="User ID"),
+        gr.inputs.Slider(minimum=1, maximum=20, step=1, default=5, label="Number of Recommendations")
+    ],
+    outputs="text",
+    title="Neural Collaborative Filtering Recommendation System",
+    description="Enter a User ID to receive personalized item recommendations.",
+    examples=[
+        ["user_1", 5],
+        ["user_2", 10],
+        ["user_3", 7]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()

item_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d3794f0651bde4879ca75543c83892f98cfc80e5c59c1a01b866bf15ec1144b
+size 2058499

model.py

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+# src/model.py
+
+import torch
+import torch.nn as nn
+
+class NCFModel(nn.Module):
+    def __init__(self, num_users, num_items, embedding_size=50):
+        """
+        Initialize the NCF model with embedding layers and fully connected layers.
+        
+        Args:
+            num_users (int): Total number of unique users.
+            num_items (int): Total number of unique items.
+            embedding_size (int): Size of the embedding vectors.
+        """
+        super(NCFModel, self).__init__()
+        self.user_embedding = nn.Embedding(num_users, embedding_size)
+        self.item_embedding = nn.Embedding(num_items, embedding_size)
+        
+        self.fc1 = nn.Linear(embedding_size * 2, 128)
+        self.dropout1 = nn.Dropout(0.5)
+        self.fc2 = nn.Linear(128, 64)
+        self.dropout2 = nn.Dropout(0.5)
+        self.output_layer = nn.Linear(64, 1)
+    
+    def forward(self, user, item):
+        """
+        Forward pass through the model.
+        
+        Args:
+            user (torch.LongTensor): Tensor of user IDs.
+            item (torch.LongTensor): Tensor of item IDs.
+        
+        Returns:
+            torch.Tensor: Output logits indicating interaction likelihood.
+        """
+        user_emb = self.user_embedding(user)
+        item_emb = self.item_embedding(item)
+        x = torch.cat([user_emb, item_emb], dim=1)
+        x = torch.relu(self.fc1(x))
+        x = self.dropout1(x)
+        x = torch.relu(self.fc2(x))
+        x = self.dropout2(x)
+        x = self.output_layer(x)  # No sigmoid here; handled in loss function
+        return x.squeeze()

requirements.txt

@@ -0,0 +1,183 @@
+absl-py==2.1.0
+aiohappyeyeballs==2.4.3
+aiohttp==3.10.9
+aiosignal==1.3.1
+anyio==4.5.0
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==2.4.1
+astunparse==1.6.3
+async-lru==2.0.4
+async-timeout==4.0.3
+attrs==24.2.0
+babel==2.16.0
+backcall==0.2.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+Brotli @ file:///C:/b/abs_3d36mno480/croot/brotli-split_1714483178642/work
+cachetools==5.5.0
+certifi @ file:///C:/b/abs_1fw_exq1si/croot/certifi_1725551736618/work/certifi
+cffi==1.17.1
+charset-normalizer @ file:///croot/charset-normalizer_1721748349566/work
+colorama==0.4.6
+comm==0.2.2
+contourpy==1.1.1
+cycler==0.12.1
+datasets==3.0.1
+debugpy==1.8.6
+decorator==5.1.1
+defusedxml==0.7.1
+dill==0.3.8
+exceptiongroup==1.2.2
+executing==2.1.0
+fastjsonschema==2.20.0
+filelock @ file:///C:/b/abs_f2gie28u58/croot/filelock_1700591233643/work
+flatbuffers==24.3.25
+fonttools==4.54.1
+fqdn==1.5.1
+frozenlist==1.4.1
+fsspec==2024.6.1
+gast==0.4.0
+gmpy2 @ file:///C:/ci/gmpy2_1645456279018/work
+google-auth==2.35.0
+google-auth-oauthlib==1.0.0
+google-pasta==0.2.0
+grpcio==1.66.2
+h11==0.14.0
+h5py==3.11.0
+httpcore==1.0.6
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+idna==3.10
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+ipykernel==6.29.5
+ipython==8.12.3
+isoduration==20.11.0
+jedi==0.19.1
+Jinja2 @ file:///C:/b/abs_92fccttino/croot/jinja2_1716993447201/work
+joblib==1.4.2
+json5==0.9.25
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+jupyter-events==0.10.0
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+jupyter_client==8.6.3
+jupyter_core==5.7.2
+jupyter_server==2.14.2
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+jupyterlab==4.2.5
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+jupyterlab_server==2.27.3
+kaggle==1.6.17
+keras==2.13.1
+kiwisolver==1.4.7
+libclang==18.1.1
+Markdown==3.7
+MarkupSafe @ file:///C:/b/abs_ecfdqh67b_/croot/markupsafe_1704206030535/work
+matplotlib==3.7.5
+matplotlib-inline==0.1.7
+mistune==3.0.2
+mkl-fft @ file:///C:/b/abs_19i1y8ykas/croot/mkl_fft_1695058226480/work
+mkl-random @ file:///C:/b/abs_edwkj1_o69/croot/mkl_random_1695059866750/work
+mkl-service==2.4.0
+mpmath @ file:///C:/b/abs_7833jrbiox/croot/mpmath_1690848321154/work
+multidict==6.1.0
+multiprocess==0.70.16
+nbclient==0.10.0
+nbconvert==7.16.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx @ file:///C:/b/abs_e6gi1go5op/croot/networkx_1690562046966/work
+notebook_shim==0.2.4
+numpy @ file:///C:/Users/dev-admin/mkl/numpy_and_numpy_base_1682982345978/work
+oauthlib==3.2.2
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+packaging==24.1
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+pandocfilters==1.5.1
+parso==0.8.4
+pickleshare==0.7.5
+pillow @ file:///C:/b/abs_32o8er3uqp/croot/pillow_1721059447598/work
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+platformdirs==4.3.6
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+pure_eval==0.2.3
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+pyasn1_modules==0.4.1
+pycparser==2.22
+Pygments==2.18.0
+pyparsing==3.1.4
+PySocks @ file:///C:/ci/pysocks_1605287845585/work
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+python-json-logger==2.0.7
+python-slugify==8.0.4
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+pywinpty==2.0.13
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+pyzmq==26.2.0
+referencing==0.35.1
+regex==2024.9.11
+requests @ file:///C:/b/abs_9frifg92q2/croot/requests_1721410901096/work
+requests-oauthlib==2.0.0
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.20.0
+rsa==4.9
+safetensors==0.4.5
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+scipy==1.10.1
+seaborn==0.13.2
+Send2Trash==1.8.3
+six==1.16.0
+sniffio==1.3.1
+soupsieve==2.6
+stack-data==0.6.3
+sympy @ file:///C:/b/abs_4e4p71hdj_/croot/sympy_1724938208509/work
+tensorboard==2.13.0
+tensorboard-data-server==0.7.2
+tensorflow==2.13.0
+tensorflow-estimator==2.13.0
+tensorflow-intel==2.13.0
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==2.4.0
+terminado==0.18.1
+text-unidecode==1.3
+threadpoolctl==3.5.0
+tinycss2==1.3.0
+tokenizers==0.20.0
+tomli==2.0.2
+torch==2.4.1+cu118
+torchaudio==2.4.1+cu118
+torchvision==0.19.1+cu118
+tornado==6.4.1
+tqdm==4.66.5
+traitlets==5.14.3
+transformers==4.45.1
+types-python-dateutil==2.9.0.20241003
+typing_extensions @ file:///C:/b/abs_0as9mdbkfl/croot/typing_extensions_1715268906610/work
+tzdata==2024.2
+uri-template==1.3.0
+urllib3 @ file:///C:/b/abs_9a_f8h_bn2/croot/urllib3_1727769836930/work
+wcwidth==0.2.13
+webcolors==24.8.0
+webencodings==0.5.1
+websocket-client==1.8.0
+Werkzeug==3.0.4
+win-inet-pton @ file:///C:/ci/win_inet_pton_1605306167264/work
+wrapt==1.16.0
+xxhash==3.5.0
+yarl==1.13.1
+zipp==3.20.2
+torch==2.0.1
+gradio==3.35.0
+scikit-learn==1.2.2
+numpy==1.25.2

user_encoder.pkl

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+oid sha256:e864f77c5736419d426f902fc721d8357e6ea80e3af1f1ad0db8a7d205e43c89
+size 12938253

user_positive_items.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e7b2dca91fb75790d1660abbda79f1e201aabfb3a90b157417345eb009c42ad
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