Update app.py

app.py

@@ -1,64 +1,126 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+from pypdf import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from langchain.memory import ConversationBufferMemory
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Function to extract text from PDFs
+def extract_text_from_pdf(pdf_file):
+    try:
+        reader = PdfReader(pdf_file)
+        text = ""
+        for page in reader.pages:
+            extracted = page.extract_text()
+            if extracted:
+                text += extracted + "\n"
+        return text
+    except Exception as e:
+        return f"Error reading PDF: {e}"
 
+# Function to process PDFs and create vector store
+def process_pdfs(pdf_files):
+    documents = []
+    for pdf_file in pdf_files:
+        text = extract_text_from_pdf(pdf_file)
+        if text and not text.startswith("Error"):
+            documents.append(text)
+    
+    # Chunk documents
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=150,
+        length_function=len
+    )
+    chunks = []
+    for doc in documents:
+        splits = text_splitter.split_text(doc)
+        chunks.extend(splits)
+    
+    # Create embeddings and vector store
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = FAISS.from_texts(chunks, embeddings)
+    return vector_store
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Initialize LLM
+def initialize_llm():
+    model_name = "google/flan-t5-base"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    pipe = pipeline(
+        "text2text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        max_length=512,
+        temperature=0.7,
+        device=0 if torch.cuda.is_available() else -1
+    )
+    llm = HuggingFacePipeline(pipeline=pipe)
+    return llm
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Create RAG chain
+def create_rag_chain(vector_store, llm):
+    prompt_template = """Use the following pieces of context to answer the question. If you don't know the answer, say so. Do not make up information.
+{context}
+Question: {question}
+Answer: """
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        input_key="question",
+        output_key="answer",
+        max_len=4
+    )
+    chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vector_store.as_retriever(search_kwargs={"k": 5}),
+        return_source_documents=True,
+        chain_type_kwargs={"prompt": prompt, "memory": memory}
+    )
+    return chain
 
-    messages.append({"role": "user", "content": message})
+# Gradio interface function
+def rag_interface(pdf_files, question):
+    if not pdf_files:
+        return "Please upload at least one PDF file.", ""
+    
+    # Process PDFs and create vector store
+    vector_store = process_pdfs(pdf_files)
+    
+    # Initialize LLM and RAG chain
+    llm = initialize_llm()
+    rag_chain = create_rag_chain(vector_store, llm)
+    
+    # Get answer
+    result = rag_chain({"query": question})
+    answer = result["result"]
+    chat_history = rag_chain.combine_documents_chain.memory.chat_memory.messages
+    
+    # Format chat history
+    history_text = ""
+    for i in range(0, len(chat_history), 2):
+        if i + 1 < len(chat_history):
+            history_text += f"Q: {chat_history[i].content}\nA: {chat_history[i+1].content}\n\n"
+    
+    return answer, history_text
 
-    response = ""
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# RAG Question Answering System")
+    pdf_input = gr.File(label="Upload PDFs", file_count="multiple", file_types=[".pdf"])
+    question_input = gr.Textbox(label="Ask a question")
+    answer_output = gr.Textbox(label="Answer")
+    history_output = gr.Textbox(label="Chat History")
+    submit_button = gr.Button("Submit")
+    submit_button.click(
+        fn=rag_interface,
+        inputs=[pdf_input, question_input],
+        outputs=[answer_output, history_output]
+    )
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch(share=True)