deleting google dependency

app.py

@@ -1,111 +1,110 @@
-import os
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-
-from langchain.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-  
-
-# Load a Hugging Face model (e.g., LLaMA or Falcon)
-model_name = "mixedbread-ai/mxbai-embed-2d-large-v1"  # Replace with your preferred model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
-
-
-
-def get_pdf_text(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-# chuck_size = 1000, chunk_overlap = 200 (for shorted PDFs)
-def get_text_chunks(text):
-  text_splitter= RecursiveCharacterTextSplitter(
-    chunk_size=10000,
-    chunk_overlap=1000,
-    # length_function=len
-  )
-  chunks=text_splitter.split_text(text)
-  return chunks
-
-# Converting into Vector data/store (can also be stored)
-def get_vector_store(text_chunks):
-  # embeddings = GoogleGenerativeAIEmbeddings(model='embedding-gecko-001')
-  # embeddings = GoogleGenerativeAIEmbeddings(model='models/embedding-001')
-  embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-  vector_store = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-  vector_store.save_local("faiss_index")
-  # return vector_store
-
-
-  
-def chat_with_huggingface(context, query):
-    prompt_template = """
-    Answer the query as detailed as possible from the provided context. 
-    If the answer is not in the context, just say, "Answer is not available in the provided documents". 
-    Context: {context}
-    Query: {query}
-    Answer:
-    """
-    inputs = tokenizer(prompt_template, return_tensors="pt").to(model.device)
-    outputs = model.generate(**inputs, max_length=500, temperature=0.3)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-def get_conversation_chain():
-    def huggingface_chain(inputs):
-        context = inputs["input_documents"][0].page_content  # Extract context from FAISS search
-        query = inputs["question"]
-        return {"output_text": chat_with_huggingface(context, query)}
-
-    return huggingface_chain
-
-def user_input(user_question):
-  # embeddings = GoogleGenerativeAIEmbeddings(model='embedding-gecko-001')
-  embeddings = GoogleGenerativeAIEmbeddings(model='models/embedding-001')
-
-  # Loading the embeddings
-  new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-  docs = new_db.similarity_search(user_question)
-
-  chain=get_conversation_chain()
-
-  response = chain(
-      {"input_documents": docs, "question": user_question}
-      , return_only_outputs=True)
-  
-  print(response)
-  st.write("Reply: ", response["output_text"])
-
-# Frontend page Processor
-def main():
-  st.set_page_config(page_title="PDF Chatbot")
-  st.header("PDF Chatbot made for Pooja")
-
-  user_question = st.text_input("Puchiye kuch apne documents se:")
-
-  if user_question:
-    user_input(user_question)
-
-  with st.sidebar:
-    st.title("Menu:")
-    pdf_docs = st.file_uploader(
-        "Apne PDFs yaha pe upload karo then click on 'Process'", accept_multiple_files=True)
-    if st.button("Submit & Process"):
-      with st.spinner("Ruko Padh raha hu..."):
-        raw_text = get_pdf_text(pdf_docs)
-        text_chunks = get_text_chunks(raw_text)
-        get_vector_store(text_chunks)
-        st.success("Saare documents padh liya. Ab swaal pucho 😤")
-
-
-if __name__ == '__main__':
+import os
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+  
+
+# Load a Hugging Face model (e.g., LLaMA or Falcon)
+model_name = "mixedbread-ai/mxbai-embed-2d-large-v1"  # Replace with your preferred model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+# chuck_size = 1000, chunk_overlap = 200 (for shorted PDFs)
+def get_text_chunks(text):
+  text_splitter= RecursiveCharacterTextSplitter(
+    chunk_size=10000,
+    chunk_overlap=1000,
+    # length_function=len
+  )
+  chunks=text_splitter.split_text(text)
+  return chunks
+
+# Converting into Vector data/store (can also be stored)
+def get_vector_store(text_chunks):
+  # embeddings = GoogleGenerativeAIEmbeddings(model='embedding-gecko-001')
+  # embeddings = GoogleGenerativeAIEmbeddings(model='models/embedding-001')
+  embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+  vector_store = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+  vector_store.save_local("faiss_index")
+  # return vector_store
+
+
+  
+def chat_with_huggingface(context, query):
+    prompt_template = """
+    Answer the query as detailed as possible from the provided context. 
+    If the answer is not in the context, just say, "Answer is not available in the provided documents". 
+    Context: {context}
+    Query: {query}
+    Answer:
+    """
+    inputs = tokenizer(prompt_template, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_length=500, temperature=0.3)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def get_conversation_chain():
+    def huggingface_chain(inputs):
+        context = inputs["input_documents"][0].page_content  # Extract context from FAISS search
+        query = inputs["question"]
+        return {"output_text": chat_with_huggingface(context, query)}
+
+    return huggingface_chain
+
+def user_input(user_question):
+  # embeddings = GoogleGenerativeAIEmbeddings(model='embedding-gecko-001')
+  embeddings = GoogleGenerativeAIEmbeddings(model='models/embedding-001')
+
+  # Loading the embeddings
+  new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+  docs = new_db.similarity_search(user_question)
+
+  chain=get_conversation_chain()
+
+  response = chain(
+      {"input_documents": docs, "question": user_question}
+      , return_only_outputs=True)
+  
+  print(response)
+  st.write("Reply: ", response["output_text"])
+
+# Frontend page Processor
+def main():
+  st.set_page_config(page_title="PDF Chatbot")
+  st.header("PDF Chatbot made for Pooja")
+
+  user_question = st.text_input("Puchiye kuch apne documents se:")
+
+  if user_question:
+    user_input(user_question)
+
+  with st.sidebar:
+    st.title("Menu:")
+    pdf_docs = st.file_uploader(
+        "Apne PDFs yaha pe upload karo then click on 'Process'", accept_multiple_files=True)
+    if st.button("Submit & Process"):
+      with st.spinner("Ruko Padh raha hu..."):
+        raw_text = get_pdf_text(pdf_docs)
+        text_chunks = get_text_chunks(raw_text)
+        get_vector_store(text_chunks)
+        st.success("Saare documents padh liya. Ab swaal pucho 😤")
+
+
+if __name__ == '__main__':
   main()