Upload 3 files

main.py

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+from flask import Flask, request, jsonify
+from langchain_community.llms import LlamaCpp
+import os 
+app = Flask(__name__)
+
+n_gpu_layers = 0
+n_batch = 1024
+
+
+llm = LlamaCpp(
+    model_path="Phi-3-mini-4k-instruct-q4.gguf",  # path to GGUF file
+    temperature=0.1,
+    n_gpu_layers=n_gpu_layers,
+    n_batch=n_batch,
+    verbose=True,
+    n_ctx=4096
+)
+file_size = os.stat('Phi-3-mini-4k-instruct-q4.gguf')
+print("model size ====> :", file_size.st_size, "bytes")
+
+
+@app.route('/', methods=['POST'])
+def get_skills():
+    cv_body = request.json.get('cv_body')
+
+    # Simple inference example
+    output = llm(
+        f"<|user|>\n{cv_body}<|end|>\n<|assistant|>Can you list the skills mentioned in the CV?<|end|>",
+        max_tokens=256,  # Generate up to 256 tokens
+        stop=["<|end|>"], 
+        echo=True,  # Whether to echo the prompt
+    )
+
+    return jsonify({'skills': output})
+
+if __name__ == '__main__':
+    app.run()
+from flask import Flask, request, jsonify
+import nltk
+from gensim.models import Word2Vec
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+import matplotlib.pyplot as plt
+import io
+import base64
+
+nltk.download('punkt')
+
+app = Flask(__name__)
+
+texts = [
+    "This is a sample text.",
+    "Another example of text.",
+    "More texts to compare."
+]
+
+tokenized_texts = [nltk.word_tokenize(text.lower()) for text in texts]
+
+word_embeddings_model = Word2Vec(sentences=tokenized_texts, vector_size=100, window=5, min_count=1, workers=4)
+
+def text_embedding(text):
+    words = nltk.word_tokenize(text.lower())
+    embeddings = [word_embeddings_model.wv[word] for word in words if word in word_embeddings_model.wv]
+    if embeddings:
+        return np.mean(embeddings, axis=0)
+    else:
+        return np.zeros(word_embeddings_model.vector_size)
+
+@app.route('/process', methods=['POST'])
+def process():
+    data = request.get_json()
+    input_text = data.get('input_text', '')
+
+    if not input_text:
+        return jsonify({'error': 'No input text provided'}), 400
+
+    input_embedding = text_embedding(input_text)
+    text_embeddings = [text_embedding(text) for text in texts]
+
+    similarities = cosine_similarity([input_embedding], text_embeddings).flatten()
+    similarities_percentages = [similarity * 100 for similarity in similarities]
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    texts_for_plotting = [f"Text {i+1}" for i in range(len(texts))]
+    ax.bar(texts_for_plotting, similarities_percentages)
+    ax.set_ylabel('Similarity (%)')
+    ax.set_xlabel('Texts')
+    ax.set_title('Similarity of Input Text with other texts')
+    plt.xticks(rotation=45, ha='right')
+    plt.tight_layout()
+
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    img_base64 = base64.b64encode(buf.read()).decode('utf-8')
+    plt.close()
+
+    sorted_indices = np.argsort(similarities)[::-1]
+    similar_texts = [(similarities[idx] * 100, texts[idx]) for idx in sorted_indices[:3]]
+
+    response = {
+        'similarities': similarities_percentages,
+        'plot': img_base64,
+        'most_similar_texts': similar_texts
+    }
+
+    return jsonify(response)
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8080, debug=True)

main.yml

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+name: Python application
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python 3.x
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+
+    - name: Run the app
+      run: python app.py

requirements.txt

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+flask
+langchain
+matplotlib
+numpy
+gensim
+scikit-learn
+llama-cpp-python
+huggingface-hub
+langchain-experimental
+scipy==1.10.1
+gunicorn
+langchain-community
+nltk