Create app.py

app.py

@@ -0,0 +1,151 @@
+import streamlit as st
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain_community.llms import HuggingFaceEndpoint
+from pdfminer.high_level import extract_text
+import docx2txt
+import io
+import re
+from typing import List
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import SentenceTransformerEmbeddings
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import os
+import boto3
+
+# AWS access credentials
+access_key = 'AKIAUI7N373AFR74QX5H'
+secret_key = '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'
+
+# S3 bucket details
+bucket_name = 'sentinelx-prod'
+prefix = 'LOTO/Documents/LOTOFormDocuments/'
+
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+def extract_text_from_pdf(pdf_content):
+    return extract_text(io.BytesIO(pdf_content))
+
+def extract_text_from_doc(doc_content):
+    return docx2txt.process(io.BytesIO(doc_content))
+
+def preprocess_text(text):
+    text = text.replace('\n', ' ').replace('\r', ' ')
+    text = re.sub(r'[^\x00-\x7F]+', ' ', text)
+    text = text.lower()
+    text = re.sub(r'[^\w\s]', '', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text
+
+def process_files(file_contents: List[bytes]):
+    all_text = ""
+    for file_content in file_contents:
+        if file_content.startswith(b'%PDF'):
+            extracted_text = extract_text_from_pdf(file_content)
+        else:
+            extracted_text = extract_text_from_doc(file_content)
+        preprocessed_text = preprocess_text(extracted_text)
+        all_text += preprocessed_text + " "
+    return all_text
+
+def compute_cosine_similarity_scores(query, retrieved_docs):
+    model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
+    query_embedding = model.encode(query, convert_to_tensor=True)
+    doc_embeddings = model.encode(retrieved_docs, convert_to_tensor=True)
+    cosine_scores = np.dot(doc_embeddings, query_embedding.T)
+    readable_scores = [{"doc": doc, "score": float(score)} for doc, score in zip(retrieved_docs, cosine_scores.flatten())]
+    return readable_scores
+
+def answer_query_with_similarity(query):
+    try:
+        # Fetch files from S3
+        s3 = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key)
+        objects = s3.list_objects_v2(Bucket=bucket_name, Prefix=prefix)
+
+        file_contents = []
+        for obj in objects.get('Contents', []):
+            if not obj['Key'].endswith('/'):  # Skip directories
+                response = s3.get_object(Bucket=bucket_name, Key=obj['Key'])
+                file_content = response['Body'].read()
+                file_contents.append(file_content)
+
+        all_text = process_files(file_contents)
+
+        embeddings = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        texts = text_splitter.split_text(all_text)
+
+        vector_store = Chroma.from_texts(texts, embeddings, collection_metadata={"hnsw:space": "cosine"}, persist_directory="stores/insurance_cosine")
+        load_vector_store = Chroma(persist_directory="stores/insurance_cosine", embedding_function=embeddings)
+        print("Vector DB Successfully Created!")
+
+        db3 = Chroma(persist_directory=f"stores/insurance_cosine", embedding_function=embeddings)
+        docs = db3.similarity_search(query)
+        print(f"\n\nDocuments retrieved: {len(docs)}")
+
+        if not docs:
+            print("No documents match the query.")
+            return None
+
+        docs_content = [doc.page_content for doc in docs]
+        for i, content in enumerate(docs_content, start=1):
+            print(f"\nDocument {i}: {content}...")
+
+        cosine_similarity_scores = compute_cosine_similarity_scores(query, docs_content)
+        for score in cosine_similarity_scores:
+            print(f"\nDocument Score: {score['score']}")
+
+        all_docs_content = " ".join(docs_content)
+
+        template = """
+                ### [INST] Instruction:You are an AI assistant named Goose. Your purpose is to provide accurate, relevant, and helpful information to users in a friendly, warm, and supportive manner, similar to ChatGPT. When responding to queries, please keep the following guidelines in mind:
+                When someone say hi, or small talk, o only response in a sentence.
+                Retrieve relevant information from your knowledge base to formulate accurate and informative responses.
+                Always maintain a positive, friendly, and encouraging tone in your interactions with users.
+                Strictly write the crisp and clear answers, dont write unnecesary stuff.
+                Only answer to the asked question, don't hellucinate of print any pre information.
+                After providing the answer, always ask a for any other help needed in the next paragraph
+                Writing in the bullet format is our top preference
+                Remember, your goal is to be a reliable, friendly, and supportive AI assistant that provides accurate information while creating a positive user experience, just like ChatGPT. Adapt your communication style to best suit each user's needs and preferences.
+                ### Docs : {docs}
+                ### Question : {question}
+                """
+        prompt = PromptTemplate.from_template(template.format(docs=all_docs_content, question=query))
+
+        repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+        llm = HuggingFaceEndpoint(repo_id=repo_id, temperature=0.1, token=HUGGINGFACEHUB_API_TOKEN,
+                                  top_p=0.15,
+                                  max_new_tokens=256,
+                                  repetition_penalty=1.1
+                                  )
+        llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+        answer = llm_chain.run(question=query)
+        cleaned_answer = answer.split("Answer:")[-1].strip()
+        print(f"\n\nAnswer: {cleaned_answer}")
+
+        return cleaned_answer
+    except Exception as e:
+        print("An error occurred while getting the answer: ", str(e))
+        return None
+
+def main():
+    st.title("Document Query App")
+
+    query = st.text_input("Enter your query:")
+
+    if st.button("Get Answer"):
+        if query:
+            response = answer_query_with_similarity(query)
+            if response:
+                st.write("Answer:", response)
+            else:
+                st.write("No answer found.")
+        else:
+            st.write("Please provide a query.")
+
+if __name__ == "__main__":
+    main()