Update app.py

app.py

@@ -12,7 +12,6 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnablePassthrough
 
 
-
 # Initialize the FAISS vector store
 vector_store = None
 
@@ -39,47 +38,87 @@ Question: {question}
 Answer:
 """
 
+# Function to handle PDF upload and indexing
+def index_pdf(pdf):
+    global vector_store
+    
+    # Load the PDF
+    loader = PyPDFLoader(pdf.name)
+    documents = loader.load()
 
-def greet(name):
-    return f"Hello, {name}!"
+    # Split the documents into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(documents)
+
+    # Embed the chunks 
+    embeddings = HuggingFaceEmbeddings(model_name="bert-base-uncased", encode_kwargs={"normalize_embeddings": True})
+
+    # Store the embeddings in the vector store
+    vector_store = FAISS.from_documents(texts, embeddings)
+
+    return "PDF indexed successfully!"
+
+def load_sample_pdf():
+    global vector_store
+    
+    # Load the PDF
+    loader = PyPDFLoader(sample_filename)
+    documents = loader.load()
+
+    # Split the documents into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(documents)
 
+    # Embed the chunks 
+    embeddings = HuggingFaceEmbeddings(model_name="bert-base-uncased", encode_kwargs={"normalize_embeddings": True})
 
+    # Store the embeddings in the vector store
+    vector_store = FAISS.from_documents(texts, embeddings)
 
-# def format_docs(docs):
-#     return "\n\n".join(doc.page_content for doc in docs)
+    return "Sample PDF indexed successfully!"
+
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
     
 
-# def generate_response(query, history, model, temperature, max_tokens, top_p, seed):
-#     if vector_store is None:
-#         return "Please upload and index a PDF at the Indexing tab."
+def generate_response(query, history, model, temperature, max_tokens, top_p, seed):
+    if vector_store is None:
+        return "Please upload and index a PDF at the Indexing tab."
 
-#     if seed == 0:
-#         seed = random.randint(1, 100000)
+    if seed == 0:
+        seed = random.randint(1, 100000)
 
-#     retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 16})
-#     llm = ChatGroq(groq_api_key=os.environ.get("GROQ_API_KEY"), model=model)
-#     custom_rag_prompt = PromptTemplate.from_template(template)
+    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 16})
+    llm = ChatGroq(groq_api_key=os.environ.get("GROQ_API_KEY"), model=model)
+    custom_rag_prompt = PromptTemplate.from_template(template)
    
-#     rag_chain = (
-#         {"context": retriever | format_docs, "question": RunnablePassthrough()}
-#         | custom_rag_prompt
-#         | llm
-#         | StrOutputParser()
-#     )
-
-#     response = rag_chain.invoke(query)
+    rag_chain = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | custom_rag_prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    response = rag_chain.invoke(query)
    
-#     return response
+    return response
+
 
 
+additional_inputs = [
+    gr.Dropdown(choices=["llama-3.1-70b-versatile", "llama-3.1-8b-instant", "llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma2-9b-it", "gemma-7b-it"], value="llama-3.1-70b-versatile", label="Model"),
+    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature", info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."),
+    gr.Slider(minimum=1, maximum=8000, step=1, value=8000, label="Max Tokens", info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b, 132k for llama 3.1."),
+    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P", info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."),
+    gr.Number(precision=0, value=0, label="Seed", info="A starting point to initiate generation, use 0 for random")
+]
+
+
+def greet(name):
+    return f"Hello, {name}!"
+
 
-# additional_inputs = [
-#     gr.Dropdown(choices=["llama-3.1-70b-versatile", "llama-3.1-8b-instant", "llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma2-9b-it", "gemma-7b-it"], value="llama-3.1-70b-versatile", label="Model"),
-#     gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature", info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."),
-#     gr.Slider(minimum=1, maximum=8000, step=1, value=8000, label="Max Tokens", info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b, 132k for llama 3.1."),
-#     gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P", info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."),
-#     gr.Number(precision=0, value=0, label="Seed", info="A starting point to initiate generation, use 0 for random")
-# ]
 
 with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
     # Set the title and description for the app.