Create app.py

app.py

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+import os
+import io
+import re
+import numpy as np
+import pytesseract
+from PIL import Image
+from typing import List
+from sentence_transformers import SentenceTransformer
+from langchain_community.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import SentenceTransformerEmbeddings
+from groq import Groq
+import gradio as gr
+import requests
+
+# Ensure the Tesseract OCR path is set correctly
+pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract'
+
+GROQ_API_KEY = "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"
+
+def preprocess_text(text):
+    try:
+        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
+    except Exception as e:
+        print("Failed to preprocess text:", e)
+        return ""
+
+def fetch_text_file_from_huggingface_space():
+    url = "https://huggingface.co/spaces/Luciferalive/goosev9/blob/main/extracted_text.txt"
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        text_content = response.text
+        print("Successfully downloaded the text file")
+        return text_content
+    except Exception as e:
+        print(f"Failed to download the text file: {e}")
+        return ""
+
+def create_vector_store(text_content):
+    embeddings = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_text(text_content)
+    if not texts:
+        print("No text chunks created.")
+        return None
+
+    vector_store = Chroma.from_texts(texts, embeddings, collection_name="insurance_cosine")
+    print("Vector DB Successfully Created!")
+    return vector_store
+
+def load_vector_store():
+    embeddings = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    try:
+        db = Chroma(embedding_function=embeddings, collection_name="insurance_cosine")
+        print("Vector DB Successfully Loaded!")
+        return db
+    except Exception as e:
+        print("Failed to load Vector DB:", e)
+        return None
+
+def answer_query(query):
+    try:
+        vector_store = load_vector_store()
+        if not vector_store:
+            return None
+
+        docs = vector_store.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]
+        all_docs_content = " ".join(docs_content)
+
+        client = Groq(api_key=GROQ_API_KEY)
+        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 says hi, or small talk, only respond 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 crisp and clear answers, don't write unnecessary stuff.
+                - Only answer the asked question, don't hallucinate or print any pre-information.
+                - After providing the answer, always ask for any other help needed in the next paragraph.
+                - Writing in 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}
+                """
+
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system", 
+                    "content": template.format(docs=all_docs_content, question=query)
+                },
+                {
+                    "role": "user",
+                    "content": query
+                }
+            ],
+            model="llama3-8b-8192",
+        )
+
+        answer = chat_completion.choices[0].message.content.strip()
+        return answer
+    except Exception as e:
+        print("An error occurred while getting the answer: ", str(e))
+        return None
+
+def process_query(query):
+    try:
+        response = answer_query(query)
+        if response:
+            return "Answer: " + response
+        else:
+            return "No answer found."
+    except Exception as e:
+        print("An error occurred while getting the answer: ", str(e))
+        return "An error occurred: " + str(e)
+
+
+# Set up the Gradio interface
+def launch_assistant():
+    text_content = fetch_text_file_from_huggingface_space()
+    if not text_content.strip():
+        print("No text content fetched.")
+        return
+
+    vector_store = create_vector_store(text_content)
+    if not vector_store:
+        print("Failed to create Vector DB.")
+        return
+
+    iface = gr.Interface(
+        fn=process_query,
+        inputs=gr.Textbox(lines=7, label="Enter your question"),
+        outputs="text",
+        title="Goose AI Assistant",
+        description="Ask a question and get an answer from the AI assistant."
+    )
+
+    iface.launch()
+
+launch_assistant()