Update app.py

app.py

@@ -1,214 +1,332 @@
-import io
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Suppress TF logging
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Disable GPU
+import urllib.request 
+import fitz 
+import re
 import numpy as np
-import streamlit as st
-from pypdf import PdfReader
+import tensorflow_hub as hub
+import openai
+import gradio as gr
+from sklearn.neighbors import NearestNeighbors
 
-from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+def download_pdf(url, output_path):
+    urllib.request.urlretrieve(url, output_path)
 
 
-# -------------------- Config -------------------- #
+def preprocess(text):
+    text = text.replace('\n', ' ')
+    text = re.sub('\s+', ' ', text)
+    return text
 
-EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
-#LLM_MODEL_NAME = "google/gemma-2b-it"  # you can change this later
-LLM_MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
 
+def pdf_to_text(path, start_page=1, end_page=None):
+    doc = fitz.open(path)
+    total_pages = doc.page_count
 
+    if end_page is None:
+        end_page = total_pages
 
-# -------------------- Model loaders (cached) -------------------- #
+    text_list = []
 
-@st.cache_resource(show_spinner=True)
-def load_embedder():
-    return SentenceTransformer(EMBEDDING_MODEL_NAME)
+    for i in range(start_page-1, end_page):
+        text = doc.load_page(i).get_text("text")
+        text = preprocess(text)
+        text_list.append(text)
 
+    doc.close()
+    return text_list
 
-@st.cache_resource(show_spinner=True)
-def load_llm_pipeline():
-    """
-    Load a text-generation pipeline for the LLM.
-    Using device_map="auto" will use GPU if available.
-    """
-    tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_NAME)
-    model = AutoModelForCausalLM.from_pretrained(
-        LLM_MODEL_NAME,
-        device_map="auto",
-    )
-    gen_pipe = pipeline(
-        "text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=512,
-        do_sample=False,
-        temperature=0.1,
-        top_p=0.9,
-    )
-    return gen_pipe
-
-
-# -------------------- Helpers -------------------- #
-
-def extract_text_from_pdf(file) -> str:
-    """Extract all text from an uploaded PDF file."""
-    pdf_reader = PdfReader(file)
-    all_text = []
-    for page in pdf_reader.pages:
-        text = page.extract_text()
-        if text:
-            all_text.append(text)
-    return "\n".join(all_text)
 
-
-def chunk_text(text, chunk_size=800, overlap=200):
-    """Split long text into overlapping chunks (by words)."""
-    words = text.split()
+def text_to_chunks(texts, word_length=150, start_page=1):
+    text_toks = [t.split(' ') for t in texts]
+    page_nums = []
     chunks = []
-    start = 0
-    while start < len(words):
-        end = start + chunk_size
-        chunk = " ".join(words[start:end])
-        chunks.append(chunk)
-        start += chunk_size - overlap
+    
+    for idx, words in enumerate(text_toks):
+        for i in range(0, len(words), word_length):
+            chunk = words[i:i+word_length]
+            if (i+word_length) > len(words) and (len(chunk) < word_length) and (
+                len(text_toks) != (idx+1)):
+                text_toks[idx+1] = chunk + text_toks[idx+1]
+                continue
+            chunk = ' '.join(chunk).strip()
+            chunk = f'[Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"'
+            chunks.append(chunk)
     return chunks
 
 
-def embed_texts(texts, embedder: SentenceTransformer):
-    """Get embeddings for a list of texts."""
-    if not texts:
-        return np.array([])
-    embeddings = embedder.encode(texts, convert_to_numpy=True, show_progress_bar=False)
-    return embeddings.astype("float32")
-
-
-def cosine_sim_matrix(matrix, vector):
-    """Cosine similarity between each row in matrix and a single vector."""
-    if matrix.size == 0:
-        return np.array([])
-    matrix_norm = matrix / (np.linalg.norm(matrix, axis=1, keepdims=True) + 1e-10)
-    vector_norm = vector / (np.linalg.norm(vector) + 1e-10)
-    return np.dot(matrix_norm, vector_norm)
-
-
-def retrieve_relevant_chunks(question, chunks, chunk_embeddings, embedder, top_k=4):
-    """Find top_k most relevant chunks for the question."""
-    if len(chunks) == 0:
-        return []
-
-    q_emb = embed_texts([question], embedder)[0]
-    sims = cosine_sim_matrix(chunk_embeddings, q_emb)
-    top_idx = np.argsort(sims)[::-1][:top_k]
-    return [chunks[i] for i in top_idx]
-
-
-def build_prompt(question, context_chunks):
-    context = "\n\n---\n\n".join(context_chunks)
-    system_instruction = (
-        "You are a helpful assistant that answers questions "
-        "using ONLY the information provided in the document context.\n"
-        "If the answer is not in the context, say that you cannot find it in the document."
-    )
-
-    prompt = (
-        f"{system_instruction}\n\n"
-        f"Document context:\n{context}\n\n"
-        f"Question: {question}\n\n"
-        f"Answer:"
-    )
-    return prompt
-
-
-def answer_question(question, chunks, llm_pipe):
-    """Call the LLM with the question + retrieved context."""
-    prompt = build_prompt(question, chunks)
-
-    # For most HF instruction models, plain prompt works ok.
-    outputs = llm_pipe(
-        prompt,
-        num_return_sequences=1,
-        truncation=True,
+class SemanticSearch:
+    
+    def __init__(self):
+        self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
+        self.fitted = False
+    
+    
+    def fit(self, data, batch=1000, n_neighbors=5):
+        self.data = data
+        self.embeddings = self.get_text_embedding(data, batch=batch)
+        n_neighbors = min(n_neighbors, len(self.embeddings))
+        self.nn = NearestNeighbors(n_neighbors=n_neighbors)
+        self.nn.fit(self.embeddings)
+        self.fitted = True
+    
+    
+    def __call__(self, text, return_data=True):
+        inp_emb = self.use([text])
+        neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
+        
+        if return_data:
+            return [self.data[i] for i in neighbors]
+        else:
+            return neighbors
+    
+    
+    def get_text_embedding(self, texts, batch=1000):
+        embeddings = []
+        for i in range(0, len(texts), batch):
+            text_batch = texts[i:(i+batch)]
+            emb_batch = self.use(text_batch)
+            embeddings.append(emb_batch)
+        embeddings = np.vstack(embeddings)
+        return embeddings
+
+
+
+def load_recommender(path, start_page=1):
+    global recommender
+    texts = pdf_to_text(path, start_page=start_page)
+    chunks = text_to_chunks(texts, start_page=start_page)
+    recommender.fit(chunks)
+    return 'Corpus Loaded.'
+
+def generate_text(openAI_key, prompt, model="gpt-3.5-turbo"):
+    openai.api_key = openAI_key
+    temperature = 0.1
+    max_tokens = 256
+    top_p = 1
+    frequency_penalty = 0
+    presence_penalty = 0
+
+    if model == "text-davinci-003":
+        completions = openai.Completion.create(
+            engine=model,
+            prompt=prompt,
+            max_tokens=max_tokens,
+            n=1,
+            stop=None,
+            temperature=temperature,
+        )
+        message = completions.choices[0].text
+    else:
+        response = openai.ChatCompletion.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=temperature,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            frequency_penalty=frequency_penalty,
+            presence_penalty=presence_penalty,
+        )
+        message = response['choices'][0]['message']['content']
+    return message
+
+  
+def generate_answer(question, openAI_key, model):
+    topn_chunks = recommender(question)
+    prompt = 'search results:\n\n'
+    for c in topn_chunks:
+        prompt += c + '\n\n'
+        
+    prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\
+              "Cite each reference using [ Page Number] notation. "\
+              "Only answer what is asked. The answer should be short and concise. \n\nQuery: "
+    
+    prompt += f"{question}\nAnswer:"
+    answer = generate_text(openAI_key, prompt, model)
+    return answer
+
+
+def question_answer(chat_history, url, file, question, openAI_key, model):
+    try:
+        if openAI_key.strip()=='':
+            return '[ERROR]: Please enter your Open AI Key. Get your key here : https://platform.openai.com/account/api-keys'
+        if url.strip() == '' and file is None:
+            return '[ERROR]: Both URL and PDF is empty. Provide at least one.'
+        if url.strip() != '' and file is not None:
+            return '[ERROR]: Both URL and PDF is provided. Please provide only one (either URL or PDF).'
+        if model is None or model =='':
+            return '[ERROR]: You have not selected any model. Please choose an LLM model.'
+        if url.strip() != '':
+            glob_url = url
+            download_pdf(glob_url, 'corpus.pdf')
+            load_recommender('corpus.pdf')
+        else:
+            old_file_name = file.name
+            file_name = file.name
+            file_name = file_name[:-12] + file_name[-4:]
+            os.rename(old_file_name, file_name)
+            load_recommender(file_name)
+        if question.strip() == '':
+            return '[ERROR]: Question field is empty'
+        if model == "text-davinci-003" or model == "gpt-4" or model == "gpt-4-32k":
+            answer = generate_answer_text_davinci_003(question, openAI_key)
+        else:
+            answer = generate_answer(question, openAI_key, model)
+        chat_history.append([question, answer])
+        return chat_history
+    except openai.error.InvalidRequestError as e:
+        return f'[ERROR]: Either you do not have access to GPT4 or you have exhausted your quota!'
+
+
+
+def generate_text_text_davinci_003(openAI_key,prompt, engine="text-davinci-003"):
+    openai.api_key = openAI_key
+    completions = openai.Completion.create(
+        engine=engine,
+        prompt=prompt,
+        max_tokens=512,
+        n=1,
+        stop=None,
+        temperature=0.7,
     )
-    text = outputs[0]["generated_text"]
-
-    # Try to remove the prompt part if the model echoes it
-    if prompt in text:
-        text = text.split(prompt, 1)[-1].strip()
-
-    return text.strip()
-
-
-# -------------------- Streamlit UI -------------------- #
-
-st.set_page_config(page_title="Chat with your PDF (HuggingFace)", layout="wide")
-
-st.title("📄 Chat with your PDF (HuggingFace RAG)")
-
-st.markdown(
-    """
-Upload a PDF, let the app index it, and then ask questions.
-The model will answer based only on the document content (RAG).
-"""
-)
-
-with st.sidebar:
-    st.header("1. Upload and process PDF")
-    uploaded_pdf = st.file_uploader("Choose a PDF file", type=["pdf"])
-    process_button = st.button("Process Document")
-
-# Session state to keep doc data
-if "chunks" not in st.session_state:
-    st.session_state.chunks = []
-    st.session_state.embeddings = None
-
-# Load models (lazy)
-with st.spinner("Loading models (first time only)..."):
-    embedder = load_embedder()
-    llm_pipe = load_llm_pipeline()
+    message = completions.choices[0].text
+    return message
+
+
+def generate_answer_text_davinci_003(question,openAI_key):
+    topn_chunks = recommender(question)
+    prompt = ""
+    prompt += 'search results:\n\n'
+    for c in topn_chunks:
+        prompt += c + '\n\n'
+        
+    prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\
+              "Cite each reference using [ Page Number] notation (every result has this number at the beginning). "\
+              "Citation should be done at the end of each sentence. If the search results mention multiple subjects "\
+              "with the same name, create separate answers for each. Only include information found in the results and "\
+              "don't add any additional information. Make sure the answer is correct and don't output false content. "\
+              "If the text does not relate to the query, simply state 'Found Nothing'. Ignore outlier "\
+              "search results which has nothing to do with the question. Only answer what is asked. The "\
+              "answer should be short and concise. \n\nQuery: {question}\nAnswer: "
+    
+    prompt += f"Query: {question}\nAnswer:"
+    answer = generate_text_text_davinci_003(openAI_key, prompt,"text-davinci-003")
+    return answer
+
+# pre-defined questions
+questions = [
+    "What did the study investigate?",
+    "Can you provide a summary of this paper?",
+    "what are the methodologies used in this study?",
+    "what are the data intervals used in this study? Give me the start dates and end dates?",
+    "what are the main limitations of this study?",
+    "what are the main shortcomings of this study?",
+    "what are the main findings of the study?",
+    "what are the main results of the study?",
+    "what are the main contributions of this study?",
+    "what is the conclusion of this paper?",
+    "what are the input features used in this study?",
+    "what is the dependent variable in this study?",
+]
+
+
+recommender = SemanticSearch()
+
+title = 'PDF GPT Turbo'
+description = """ PDF GPT Turbo allows you to chat with your PDF files. It uses Google's Universal Sentence Encoder with Deep averaging network (DAN) to give hallucination free response by improving the embedding quality of OpenAI. It cites the page number in square brackets([Page No.]) and shows where the information is located, adding credibility to the responses."""
+
+with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo:
+    gr.Markdown(f'<center><h3>{title}</h3></center>')
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column():
+            # API Key and File Inputs
+            with gr.Accordion("API Key and PDF"):
+                openAI_key = gr.Textbox(label='Enter your OpenAI API key here', type='password')
+                url = gr.Textbox(label='Enter PDF URL here (Example: https://arxiv.org/pdf/1706.03762.pdf ; https://link.springer.com/content/pdf/10.1007/s10614-022-10325-8.pdf)')
+                gr.Markdown("<center><h4>OR<h4></center>")
+                file = gr.File(label='Upload your PDF/Research Paper/Book here', file_types=['.pdf'])
+            
+            # Model Selection
+            model = gr.Radio(
+                choices=[
+                    'gpt-4o-mini',
+                    'gpt-4o',
+                    'gpt-4',
+                ], 
+                label='Select Model', 
+                value='gpt-4o-mini'
+            )
 
-# Step 1: Process PDF
-if process_button:
-    if uploaded_pdf is None:
-        st.sidebar.error("Please upload a PDF first.")
-    else:
-        with st.spinner("Reading and indexing your PDF..."):
-            pdf_bytes = io.BytesIO(uploaded_pdf.read())
-            text = extract_text_from_pdf(pdf_bytes)
+            # Chat Interface
+            chatbot = gr.Chatbot(label="Chat History", type="messages")
+            msg = gr.Textbox(label="Enter your question here", lines=2)
+            submit_btn = gr.Button("Submit")
+            clear = gr.ClearButton([msg, chatbot])
+
+            # Example Questions
+            gr.Examples(
+                [[q] for q in questions],
+                inputs=[msg],
+                label="PRE-DEFINED QUESTIONS: Click on a question to auto-fill the input box",
+            )
 
-            if not text.strip():
-                st.error("Could not extract any text from this PDF.")
+    def respond(message, chat_history, url_value, file_value, key_value, model_value):
+        if message.strip() == "":
+            return "", chat_history  # Return empty message if no input
+
+        try:
+            # Ensure chat_history is initialized properly
+            if chat_history is None:
+                chat_history = []
+
+            if key_value.strip() == '':
+                chat_history.append({"role": "user", "content": message})
+                chat_history.append({"role": "assistant", "content": '[ERROR]: Please enter your OpenAI API key'})
+                return "", chat_history
+
+            if url_value.strip() == '' and file_value is None:
+                chat_history.append({"role": "user", "content": message})
+                chat_history.append({"role": "assistant", "content": '[ERROR]: Both URL and PDF are empty. Provide at least one'})
+                return "", chat_history
+
+            # Process PDF and generate answer
+            if url_value.strip() != '':
+                download_pdf(url_value, 'corpus.pdf')
+                load_recommender('corpus.pdf')
             else:
-                chunks = chunk_text(text)
-                embeddings = embed_texts(chunks, embedder)
+                old_file_name = file_value.name
+                file_name = old_file_name[:-12] + old_file_name[-4:]
+                os.rename(old_file_name, file_name)
+                load_recommender(file_name)
 
-                st.session_state.chunks = chunks
-                st.session_state.embeddings = embeddings
+            answer = generate_answer(message, key_value, model_value)
 
-                st.success(f"Done! Indexed {len(chunks)} chunks from the PDF.")
+            chat_history.append({"role": "user", "content": message})
+            chat_history.append({"role": "assistant", "content": answer})
 
-# Step 2: Ask questions
-st.header("2. Ask questions about your document")
+            return "", chat_history
 
-question = st.text_input("Type your question here")
+        except Exception as e:
+            chat_history.append({"role": "user", "content": message})
+            chat_history.append({"role": "assistant", "content": f'[ERROR]: {str(e)}'})
+            return "", chat_history
 
-if st.button("Get answer"):
-    if not st.session_state.chunks:
-        st.error("Please upload and process a PDF first.")
-    elif not question.strip():
-        st.error("Please type a question.")
-    else:
-        with st.spinner("Thinking with your document..."):
-            relevant_chunks = retrieve_relevant_chunks(
-                question,
-                st.session_state.chunks,
-                st.session_state.embeddings,
-                embedder,
-                top_k=4,
-            )
-            answer = answer_question(question, relevant_chunks, llm_pipe)
+    submit_btn.click(
+        respond, 
+        [msg, chatbot, url, file, openAI_key, model], 
+        [msg, chatbot]
+    )
 
-        st.subheader("Answer")
-        st.write(answer)
+    msg.submit(
+        respond, 
+        [msg, chatbot, url, file, openAI_key, model], 
+        [msg, chatbot]
+    )
 
-        with st.expander("Show relevant excerpts from the PDF"):
-            for i, ch in enumerate(relevant_chunks, start=1):
-                st.markdown(f"**Chunk {i}:**")
-                st.write(ch)
-                st.markdown("---")
+demo.launch()