Update app.py

app.py

@@ -1,47 +1,27 @@
 import gradio as gr
-from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.llms import HuggingFaceHub
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.chains.question_answering import load_qa_chain
+from langchain.text_splitter import CharacterTextSplitter
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-
-def answer_question(pdf_file, question):
-    # Save uploaded PDF
-    pdf_path = pdf_file.name
 
-    # Load and split the PDF
-    loader = PyPDFLoader(pdf_path)
+def ask_question(query):
+    loader = PyPDFLoader("sample_document.pdf")  # ✅ Update this line
     documents = loader.load()
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    docs = text_splitter.split_documents(documents)
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    texts = text_splitter.split_documents(documents)
 
-    # Embeddings and vector store
     embeddings = HuggingFaceEmbeddings()
-    vectorstore = FAISS.from_documents(docs, embeddings)
+    db = FAISS.from_documents(texts, embeddings)
 
-    # LLM
-    llm = HuggingFaceHub(
-        repo_id="google/flan-t5-large",
-        task="text2text-generation",
-        model_kwargs={"temperature": 0.5, "max_length": 512}
-    )
+    retriever = db.as_retriever()
+    relevant_docs = retriever.get_relevant_documents(query)
 
-    # QA chain
+    llm = HuggingFaceHub(repo_id="google/flan-t5-base", model_kwargs={"temperature":0.5, "max_length":100})
     chain = load_qa_chain(llm, chain_type="stuff")
-    relevant_docs = vectorstore.similarity_search(question)
-    response = chain.run(input_documents=relevant_docs, question=question)
+    response = chain.run(input_documents=relevant_docs, question=query)
     return response
 
-# Gradio interface
-iface = gr.Interface(
-    fn=answer_question,
-    inputs=[
-        gr.File(label="Upload your PDF"),
-        gr.Textbox(label="Ask a question")
-    ],
-    outputs="text",
-    title="📄 PDF Q&A with AI"
-)
-
+iface = gr.Interface(fn=ask_question, inputs="text", outputs="text", title="MedAssist.AI")
 iface.launch()