Spaces:
Sleeping
Sleeping
File size: 3,169 Bytes
93b9d0f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
import os
import time
from flask import Flask, render_template, jsonify, request
from src.helper import download_hugging_face_embeddings
from langchain.llms import Replicate
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.schema import Document
from langchain.text_splitter import CharacterTextSplitter
# Initialize Flask app
app = Flask(__name__)
# Load environment variables
load_dotenv()
# Define the load_pdf function
""" def load_pdf(file_path):
all_text = ""
with open(file_path, 'rb') as file:
reader = PdfReader(file)
for page in reader.pages:
all_text += page.extract_text() + "\n"
return all_text if all_text else None
"""
# Define the text_split function
""" def text_split(text):
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
document = Document(page_content=text)
return text_splitter.split_documents([document])
"""
# Load and process data
#pdf_file_path = "data/Gale Encyclopedia of Medicine Vol. 1 (A-B).pdf" # Update this path to your single PDF file
#extracted_data = load_pdf(pdf_file_path)
#if extracted_data is None:
#raise ValueError("The extracted data is None. Please check the load_pdf function.")
#print(f"Extracted Data: {extracted_data}")
# Split the extracted text into chunks
#text_chunks = text_split(extracted_data)
#if not text_chunks:
#raise ValueError("The text_chunks is None or empty. Please check the text_split function.")
#print(f"Text Chunks: {text_chunks}")
embeddings = download_hugging_face_embeddings()
if embeddings is None:
raise ValueError("The embeddings is None. Please check the download_hugging_face_embeddings function.")
print(f"Embeddings: {embeddings}")
# Setup CTransformers LLM
from langchain.llms import Replicate
os.environ["REPLICATE_API_TOKEN"]= "r8_PArS6Z8LxcRZld6NjVxhd8NJbIJXYDE25XiJd"
llm = Replicate(model="a16z-infra/llama7b-v2-chat:4f0a4744c7295c024a1de15e1a63c880d3da035fa1f49bfd344fe076074c8eea")
# Flask routes
@app.route("/")
def index():
return render_template('chat.html')
@app.route("/get", methods=["GET", "POST"])
def chat():
try:
msg = request.form["msg"]
input_text = msg
print(f"Received message: {input_text}")
# Display spinner
result = {"generated_text": "Thinking..."}
# Simulate processing delay
time.sleep(1)
# Retrieve response from the model
result = llm.generate([input_text])
print(f"LLMResult: {result}")
# Access the generated text from the result object
if result.generations and result.generations[0]:
generated_text = result.generations[0][0].text
else:
generated_text = "No response generated."
print(f"Response: {generated_text}")
return str(generated_text)
except Exception as e:
print(f"Error: {e}")
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':
app.run(host="0.0.0.0", port=8080, debug=True) |