Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModel, RagTokenizer, RagRetriever, RagSequenceForGeneration
+from pymilvus import connections, Collection, CollectionSchema, FieldSchema, DataType
+from dotenv import load_dotenv
+import os
+
+# Load environment variables
+load_dotenv()
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+
+# Initialize Milvus connection
+connections.connect("default", host="localhost", port="19530")
+
+# Define Milvus schema and collection
+fields = [
+    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
+    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=768)  # Adjust the dimension based on your model
+]
+schema = CollectionSchema(fields, "User Data Collection")
+collection = Collection(name="user_data", schema=schema)
+
+# Load Hugging Face models
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+tokenizer_rag = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
+retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom")
+model_rag = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq")
+
+# Define functions
+def generate_embedding(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).numpy().tolist()[0]
+
+def insert_data(user_id, embedding):
+    collection.insert([user_id, embedding])
+
+def retrieve_relevant_data(query):
+    query_embedding = generate_embedding(query)
+    search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
+    results = collection.search(query_embedding, "embedding", search_params)
+    return results
+
+def generate_cv(job_description, company_profile=None):
+    query = job_description
+    if company_profile:
+        query += f" Company profile: {company_profile}"
+    
+    relevant_data = retrieve_relevant_data(query)
+    context = " ".join([data.text for data in relevant_data])
+    
+    inputs = tokenizer_rag(query, return_tensors="pt")
+    context_inputs = tokenizer_rag(context, return_tensors="pt")
+    outputs = model_rag.generate(input_ids=inputs['input_ids'], context_input_ids=context_inputs['input_ids'])
+    return tokenizer_rag.decode(outputs[0], skip_special_tokens=True)
+
+# Streamlit UI
+st.title("Custom CV Generator")
+
+st.sidebar.header("Input Data")
+skills = st.sidebar.text_input("Enter your skills")
+experience = st.sidebar.text_input("Enter your experience")
+education = st.sidebar.text_input("Enter your education")
+job_description = st.sidebar.text_area("Enter job description")
+company_profile = st.sidebar.text_area("Enter company profile (optional)")
+
+if st.sidebar.button("Generate CV"):
+    # Insert user data (assuming single user for simplicity)
+    user_data = f"Skills: {skills}. Experience: {experience}. Education: {education}."
+    user_id = 1  # Example user ID
+    user_embedding = generate_embedding(user_data)
+    insert_data(user_id, user_embedding)
+    
+    # Generate CV
+    cv_text = generate_cv(job_description, company_profile)
+    st.write("Your Tailored CV:")
+    st.write(cv_text)