app.py

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+from src.helper import download_hugging_face_embeddings
+from langchain.vectorstores import FAISS
+from langchain.schema import Document
+from langchain.llms import CTransformers
+from langchain.chains import RetrievalQA
+from dotenv import load_dotenv
+import os
+
+app = Flask(__name__)
+load_dotenv()
+
+# Download embeddings model
+embeddings = download_hugging_face_embeddings()
+
+# Create Document objects with dummy texts and embeddings
+documents = [Document(page_content="dummy", embedding=embedding) for embedding in embeddings]
+
+# Initialize FAISS vector store with documents
+vector_store = FAISS.from_documents(documents, embeddings)
+
+# Initialize CTransformers model (LLAMA)
+llm = CTransformers(model="E:\\project\\Medical-Chatbot\\llama-2-7b-chat.ggmlv3.q4_0.bin", model_type="llama", config={'max_new_tokens': 512, 'temperature': 0.8})
+
+# Initialize RetrievalQA chain
+qa = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=vector_store.as_retriever(search_kwargs={'k': 2}),
+    return_source_documents=True
+)
+
+@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)