Update app.py

app.py

@@ -1,102 +1,102 @@
-from flask import Flask, render_template, jsonify, request
-from src.helper import download_hugging_face_embeddings
-
-from langchain_community.vectorstores import FAISS
-
-from langchain.prompts import PromptTemplate
-from langchain_groq import ChatGroq
-# from langchain.llms import CTransformers
-from langchain.chains import RetrievalQA
-from langchain_core.prompts import ChatPromptTemplate
-from dotenv import load_dotenv
-from src.prompt import *
-import os
-from store_index import create_vector_db
-app = Flask(__name__)
-
-# load_dotenv()
-
-# PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
-# PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV')
-
-
-# DATA_PATH = '/kaggle/input/book-pdf'
-# DB_FAISS_PATH = r'G:\Chatbot\data\vector'
-
-# Create vector database
-create_vector_db()
-print("###333")
-# Call the function directly in the cell
-
-'''#Initializing the Pinecone
-pinecone.init(api_key=PINECONE_API_KEY,
-              environment=PINECONE_API_ENV)
-
-index_name="medical-bot"'''
-
-embeddings = download_hugging_face_embeddings()
-# Load the FAISS vector database
-
-# also i change here
-# db = FAISS.load_local(DB_FAISS_PATH, embeddings)
-DB_FAISS_PATH = r'G:\Chatbot\data\vector'
-print("vector_base is loading from the folder")
-db = FAISS.load_local(DB_FAISS_PATH, embeddings,
-                      allow_dangerous_deserialization=True)
-# Loading the index
-# db = FAISS.load_local(r"G:\Chatbot\DB_FAISS_PATH",embeddings, allow_dangerous_deserialization=True)
-
-
-# docsearch=Pinecone.from_existing_index(index_name, embeddings)
-
-# Create a ChatPromptTemplate
-prompt = ChatPromptTemplate.from_template(prompt_template)
-
-chain_type_kwargs = {"prompt": prompt}
-
-'''PROMPT = PromptTemplate(template=prompt_template,
-                        input_variables=["context", "question"])
-
-chain_type_kwargs = {"prompt": PROMPT}'''
-
-'''llm = CTransformers(model=r"G:\Chatbot\model\llama-2-7b-chat.ggmlv3.q4_0.bin",
-                    model_type="llama",
-                    config={'max_new_tokens': 512,
-                            'temperature': 0.8})'''
-
-# Initialize the LLM
-# llm = ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192")
-# groq_api_key = ('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')
-# Load the GROQ and OpenAI API keys
-groq_api_key = os.getenv('GROQ_API_KEY')
-# os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
-# Initialize the LLM
-llm = ChatGroq(groq_api_key=groq_api_key,
-               model_name="Llama3-8b-8192")
-
-
-qa = RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever=db.as_retriever(search_kwargs={'k': 2}),
-    return_source_documents=True,
-    chain_type_kwargs=chain_type_kwargs)
-
-
-@app.route("/")
-def index():
-    return render_template('chat.html')
-
-
-@app.route("/get", methods=["GET", "POST"])
-def chat():
-    msg = request.form["msg"]
-    input = msg
-    print(input)
-    result = qa({"query": input})
-    print("Response : ", result["result"])
-    return str(result["result"])
-
-
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=8080, debug=True)
+from flask import Flask, render_template, jsonify, request
+from src.helper import download_hugging_face_embeddings
+
+from langchain_community.vectorstores import FAISS
+
+from langchain.prompts import PromptTemplate
+from langchain_groq import ChatGroq
+# from langchain.llms import CTransformers
+from langchain.chains import RetrievalQA
+from langchain_core.prompts import ChatPromptTemplate
+from dotenv import load_dotenv
+from src.prompt import *
+import os
+from store_index import create_vector_db
+app = Flask(__name__)
+
+# load_dotenv()
+
+# PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
+# PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV')
+
+
+# DATA_PATH = '/kaggle/input/book-pdf'
+# DB_FAISS_PATH = r'G:\Chatbot\data\vector'
+
+# Create vector database
+#create_vector_db()
+print("###333")
+# Call the function directly in the cell
+
+'''#Initializing the Pinecone
+pinecone.init(api_key=PINECONE_API_KEY,
+              environment=PINECONE_API_ENV)
+
+index_name="medical-bot"'''
+
+embeddings = download_hugging_face_embeddings()
+# Load the FAISS vector database
+
+# also i change here
+# db = FAISS.load_local(DB_FAISS_PATH, embeddings)
+DB_FAISS_PATH = r'data\vector'
+print("vector_base is loading from the folder")
+db = FAISS.load_local(DB_FAISS_PATH, embeddings,
+                      allow_dangerous_deserialization=True)
+# Loading the index
+# db = FAISS.load_local(r"G:\Chatbot\DB_FAISS_PATH",embeddings, allow_dangerous_deserialization=True)
+
+
+# docsearch=Pinecone.from_existing_index(index_name, embeddings)
+
+# Create a ChatPromptTemplate
+prompt = ChatPromptTemplate.from_template(prompt_template)
+
+chain_type_kwargs = {"prompt": prompt}
+
+'''PROMPT = PromptTemplate(template=prompt_template,
+                        input_variables=["context", "question"])
+
+chain_type_kwargs = {"prompt": PROMPT}'''
+
+'''llm = CTransformers(model=r"G:\Chatbot\model\llama-2-7b-chat.ggmlv3.q4_0.bin",
+                    model_type="llama",
+                    config={'max_new_tokens': 512,
+                            'temperature': 0.8})'''
+
+# Initialize the LLM
+# llm = ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192")
+# groq_api_key = ('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')
+# Load the GROQ and OpenAI API keys
+groq_api_key = os.getenv('GROQ_API_KEY')
+# os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
+# Initialize the LLM
+llm = ChatGroq(groq_api_key=groq_api_key,
+               model_name="Llama3-8b-8192")
+
+
+qa = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=db.as_retriever(search_kwargs={'k': 2}),
+    return_source_documents=True,
+    chain_type_kwargs=chain_type_kwargs)
+
+
+@app.route("/")
+def index():
+    return render_template('chat.html')
+
+
+@app.route("/get", methods=["GET", "POST"])
+def chat():
+    msg = request.form["msg"]
+    input = msg
+    print(input)
+    result = qa({"query": input})
+    print("Response : ", result["result"])
+    return str(result["result"])
+
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0", port=8080, debug=True)