test

Dockerfile

@@ -1,17 +1,14 @@
 # Use the official Python 3.10.9 image
 FROM python:3.10.9
 
-# Set working directory
-WORKDIR /app
-
-# Copy everything into container
+# Copy the current directory contents into the container at .
 COPY . .
 
-# Install Python dependencies
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# Set the working directory to /
+WORKDIR /
 
-# Expose the Flask port (default 5000, or whatever you use)
-EXPOSE 5000
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
 
-# Start the Flask app
-CMD ["python", "app.py"]
+# Start the FastAPI app on port 7860, the default port expected by Spaces
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

recommend_normal.py

@@ -28,7 +28,7 @@ def load_data():
 
 # Load movie data
 movies_data = load_data()
-vectorizer_path = hf_hub_download(repo_id=repo_id, filename="feature_vector.pkl", cache_dir=cache_dir)
+vectorizer_path = hf_hub_download(repo_id=repo_id, filename="model_vectorizer.pkl", cache_dir=cache_dir)
 similarity_path = hf_hub_download(repo_id=repo_id, filename="model_similarity.pkl", cache_dir=cache_dir)
 def recommend_movies(movie_name):
     # Add the movie to the user's history

recommendwithdesc.py

@@ -29,13 +29,14 @@ def load_data():
 movies_data = load_data()
 model_vectorizer = hf_hub_download(repo_id=repo_id, filename="model_vectorizer.pkl", cache_dir=cache_dir)
 similarity_path = hf_hub_download(repo_id=repo_id, filename="model_similarity.pkl", cache_dir=cache_dir)
+feature_vector = hf_hub_download(repo_id=repo_id, filename="feature_vector.pkl", cache_dir=cache_dir)
 with open(model_vectorizer, 'rb') as vec_file, open(similarity_path, 'rb') as sim_file:
         vectorizer = pickle.load(vec_file)
         similarity = pickle.load(sim_file)
 def recommend_movies_with_desc(query):
 # Transform the query into a feature vector using the same vectorizer
   feature_vecto = vectorizer.transform(query)
-  with open('feature_vector.pkl', 'rb') as feature:
+  with open(feature_vector, 'rb') as feature:
         feature_vectors = pickle.load(feature)
 
   # Calculate cosine similarity between the query vector and the feature vectors of the movies