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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)
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