Upload app.py

app.py

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+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+from sentence_transformers import CrossEncoder
+import json
+import gradio as gr
+import pickle
+import faiss
+import numpy as np
+
+
+# Step 1: Load saved chunks
+with open("chunks.pkl", "rb") as f:
+    chunks = pickle.load(f)
+
+# Step 2: Load FAISS index
+index = faiss.read_index("gitlab_index.faiss")
+
+
+#Loading the embedding model
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Load a generative model
+generator = pipeline("text2text-generation", model="google/flan-t5-base")
+
+def generate_answer(context, question):
+    prompt = f"""
+    You are a helpful chatbot that answers questions for GitLab employees and applicants.
+    Use only the provided context. Be concise and do not repeat sentences.
+    If the answer is not in the context, respond with "I don't know."
+    Context:
+    {context}
+
+    Question: {question}
+    Answer:"""
+    
+    response = generator(prompt, max_new_tokens=300, truncation=True)[0]["generated_text"]
+
+    return response.strip().split("Answer:")[-1].strip()
+
+
+
+cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+
+def rerank_chunks(query, candidate_chunks, top_k=3):
+    pairs = [[query, chunk] for chunk in candidate_chunks]
+    scores = cross_encoder.predict(pairs)
+    scored_chunks = sorted(zip(candidate_chunks, scores), key=lambda x: x[1], reverse=True)
+    return [chunk for chunk, _ in scored_chunks[:top_k]]
+
+
+
+def query_knowledge_base(query, top_k=10):
+    query_embedding = embedding_model.encode([query])
+    query_embedding = query_embedding / np.linalg.norm(query_embedding, axis=1, keepdims=True)  # Normalize
+
+    distances, indices = index.search(query_embedding, top_k)
+    initial_results = [chunks[i] for i in indices[0]]
+
+    return rerank_chunks(query, initial_results, top_k=2)
+
+
+def rag_chatbot(question):
+    try:
+        # Step 1: Embed the query and search the index
+        top_chunks = query_knowledge_base(question)
+
+        # Step 2: Combine top chunks into a single context string
+        context = " ".join(top_chunks)
+
+        result = generate_answer(context, question)
+
+        return result
+
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+
+
+# Gradio Interface
+
+def chat_interface_fn(message, history):
+    response = rag_chatbot(message)
+    return response  # Returning a response string
+
+
+gr.ChatInterface(
+    fn=chat_interface_fn,
+    chatbot=gr.Chatbot(type='messages'),
+    title="GitLab All-Remote Hiring Assistant",
+    description="Ask me about GitLab All-Remote Hiring!"
+).launch(share=True, debug=True)