Update app.py

app.py

@@ -1,36 +1,47 @@
-from google.colab import drive
-drive.mount("/content/drive")
-!pip install langchain sentence-transformers chromadb llama-cpp-python langchain_community pypdf
+import os
 from langchain_community.document_loaders import PyPDFDirectoryLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import SentenceTransformerEmbeddings
 from langchain.vectorstores import Chroma
 from langchain_community.llms import LlamaCpp
-from langchain.chains import RetrievalQA, LLMChain
-loader=PyPDFDirectoryLoader("/content/drive/MyDrive/BioMistral/Data")
-docs=loader.load()
-text_splitter=RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
-chunks=text_splitter.split_documents(docs)
-import os
-import os
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+import gradio as gr
+
+# Environment variable for Hugging Face API token
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN")
 
+# Paths for PDFs and model (upload these to the Hugging Face Space)
+PDF_DIR = "./Data"  # Replace with the path where you upload your PDFs
+MODEL_PATH = "./BioMistral-7B.Q4_K_M.gguf"  # Replace with the model's path in the Space
+
+# Load and process PDF documents
+loader = PyPDFDirectoryLoader(PDF_DIR)
+docs = loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
+chunks = text_splitter.split_documents(docs)
+
+# Create embeddings and vector store
 embeddings = SentenceTransformerEmbeddings(model_name="NeuML/pubmedbert-base-embeddings")
 vectorstore = Chroma.from_documents(chunks, embeddings)
-query="What are the major risk factors of heart disease?"
-search_results=vectorstore.similarity_search(query)
-search_results
-retriever=vectorstore.as_retriever(search_kwargs={"k":5})
-retriever.get_relevant_documents(query)
-llm=LlamaCpp(
-    model_path="/content/drive/MyDrive/BioMistral/BioMistral-7B.Q4_K_M.gguf",
+
+# Retriever for querying
+retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+
+# Initialize the LLM
+llm = LlamaCpp(
+    model_path=MODEL_PATH,
     temperature=0.2,
     max_tokens=2048,
     top_p=1
 )
-template="""
+
+# Define the prompt template
+template = """
 <|context|>
-You are an Medical Assistant that follows the instruction and generate the accurate response based on the query and the context provided.
+You are a Medical Assistant that follows instructions and generates accurate responses based on the query and the context provided.
 Please be truthful and give direct answers.
 </s>
 <|user|>
@@ -38,32 +49,18 @@ Please be truthful and give direct answers.
 </s>
 <|assistant|>
 """
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.schema.output_parser import StrOutputParser
-from langchain.prompts import ChatPromptTemplate
-prompt=ChatPromptTemplate.from_template(template)
-rag_chain=(
-    {"context":retriever,"query":RunnablePassthrough()}
+
+prompt = ChatPromptTemplate.from_template(template)
+
+# Define the RAG chain
+rag_chain = (
+    {"context": retriever, "query": RunnablePassthrough()}
     | prompt
     | llm
     | StrOutputParser()
-  )
-response=rag_chain.invoke("query")
-response
-import sys
-while True:
-  user_input=input(f"Input query: ")
-  if user_input=='exit':
-    print("Exiting...")
-    sys.exit()
-  if user_input=="":
-    continue
-  result=rag_chain.invoke(user_input)
-  print("Answer: ",result)
-!pip install gradio
-import gradio as gr
+)
 
-# Define a function to handle queries
+# Define a function for the Gradio UI
 def chatbot_ui(user_query):
     if not user_query.strip():
         return "Please enter a valid query."
@@ -75,11 +72,11 @@ def chatbot_ui(user_query):
 
 # Create the Gradio interface
 interface = gr.Interface(
-    fn=chatbot_ui,  # Function to process the query
+    fn=chatbot_ui,
     inputs=gr.Textbox(label="Enter your medical query:", placeholder="Ask a medical question here..."),
     outputs=gr.Textbox(label="Chatbot Response"),
     title="Medical Assistant Chatbot",
-    description="A chatbot made for heart patients.",
+    description="A chatbot designed for heart patients, providing accurate and reliable medical information.",
     examples=[
         ["What are the symptoms of diabetes?"],
         ["Explain the risk factors of heart disease."],
@@ -87,5 +84,6 @@ interface = gr.Interface(
     ]
 )
 
-# Launch the Gradio interface
-interface.launch(share=True)
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()