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from flask import Flask, render_template, jsonify, request
from src.helper import load_pdf, text_split, download_hugging_face_embeddings
from langchain_community.llms import CTransformers
from transformers import AutoModel, AutoTokenizer
from dotenv import load_dotenv
import torch, os
import time
from llama_index.llms.huggingface import HuggingFaceLLM
from transformers import AutoModelForCausalLM
from langchain_community.chat_message_histories import ChatMessageHistory
from llama_index.core.prompts.prompts import SimpleInputPrompt
from transformers import pipeline
import replicate
app = Flask(__name__)
# Load environment variables
load_dotenv()
#the tokens and keys
PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV')
HF_TOKEN = os.environ.get("HF_TOKEN")
# Load data and embeddings
extracted_data = load_pdf("data/")
text_chunks = text_split(extracted_data)
#download embeddings
embeddings = download_hugging_face_embeddings()
assistant_prompt = "{query_str}"
query_wrapper_prompt = SimpleInputPrompt(assistant_prompt)
model_config = {'protected_namespaces': ()}
os.environ["REPLICATE_API_TOKEN"] = "r8_3eWT6qNBwq8r7zNknWKxsyNyOQ6WMGS2WWRay"
try:
# Setup CTransformers LLM
llm = replicate(
model="a16z-infra/llama7b-v2-chat:4f0a4744c7295c024a1de15e1a63c880d3da035fa1f49bfd344fe076074c8eea",
tokenizer = AutoTokenizer.from_pretrained,
auth_token=HF_TOKEN,
model_type="llama",
config={'max_new_tokens': 256, 'temperature': 0.3, 'top_k': 20},
force_download=True,
**model_config# Adjusted for performance
)
except EnvironmentError as e:
print(f"Error loading model: {e}")
model = None
# Flask routes
@app.route("/")
def index():
return render_template('bot.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)
if llm:
# 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) |