app.py

import os
import gradio as gr
import logging
from groq import Groq
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import PyPDF2
from sklearn.metrics.pairwise import cosine_similarity
from collections import Counter

# -------------------- Setup -------------------

logging.basicConfig(
    filename='query_logs.log',
    level=logging.INFO,
    format='%(asctime)s:%(levelname)s:%(message)s'
)

GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)
PDF_PATH = 'Generative_AI_Foundations_in_Python_Discover_key_techniques_and.pdf'
sentence_transformer_model = SentenceTransformer('all-MiniLM-L6-v2')
cache = {}

# --------------------- Vectorization Function ---------------------

def vectorize_text(sentences_with_pages):
    """Vectorize sentences using SentenceTransformer and create a FAISS index."""
    try:
        sentences = [item['sentence'] for item in sentences_with_pages]
        embeddings = sentence_transformer_model.encode(sentences, show_progress_bar=True)
        index = faiss.IndexFlatL2(embeddings.shape[1])
        index.add(np.array(embeddings))
        logging.info(f"Added {len(sentences)} sentences to the vector store.")
        return index, sentences_with_pages
    except Exception as e:
        logging.error(f"Error during vectorization: {str(e)}")
        return None, None

# --------------------- PDF Processing ---------------------

def read_pdf(file_path):
    if not os.path.exists(file_path):
        logging.error(f"PDF file not found at: {file_path}")
        return []

    sentences_with_pages = []
    with open(file_path, 'rb') as file:
        reader = PyPDF2.PdfReader(file)
        for page_num, page in enumerate(reader.pages):
            text = page.extract_text()
            if text:
                sentences = [sentence.strip() for sentence in text.split('\n') if sentence.strip()]
                for sentence in sentences:
                    sentences_with_pages.append({'sentence': sentence, 'page_number': page_num + 1})
    return sentences_with_pages

# Read and Vectorize PDF Content
sentences_with_pages = read_pdf(PDF_PATH)
vector_index, sentences_with_pages = vectorize_text(sentences_with_pages)

# --------------------- Query Handling ---------------------

def generate_query_embedding(query):
    return sentence_transformer_model.encode([query])

def is_query_relevant(distances, threshold=1.0):
    return distances[0][0] <= threshold

def generate_diverse_responses(prompt, n=3):
    responses = []
    for i in range(n):
        temperature = 0.7 + (i * 0.1)
        top_p = 0.9 - (i * 0.1)
        try:
            chat_completion = client.chat.completions.create(
                messages=[{"role": "user", "content": prompt}],
                model="llama3-8b-8192",
                temperature=temperature,
                top_p=top_p
            )
            responses.append(chat_completion.choices[0].message.content.strip())
        except Exception as e:
            logging.error(f"Error generating response: {str(e)}")
            responses.append("Error generating this response.")
    return responses

def aggregate_responses(responses):
    response_counter = Counter(responses)
    most_common_response, count = response_counter.most_common(1)[0]
    if count > 1:
        return most_common_response
    else:
        embeddings = sentence_transformer_model.encode(responses)
        avg_embedding = np.mean(embeddings, axis=0)
        similarities = cosine_similarity([avg_embedding], embeddings)[0]
        return responses[np.argmax(similarities)]

def generate_answer(query):
    if query in cache:
        logging.info(f"Cache hit for query: {query}")
        return cache[query]

    try:
        query_embedding = generate_query_embedding(query)
        D, I = vector_index.search(np.array(query_embedding), k=5)

        if is_query_relevant(D):
            relevant_items = [sentences_with_pages[i] for i in I[0]]
            combined_text = " ".join([item['sentence'] for item in relevant_items])
            page_numbers = sorted(set([item['page_number'] for item in relevant_items]))
            page_numbers_str = ', '.join(map(str, page_numbers))

            # Construct primary prompt
            prompt = f"""
Use the following context from "Generative AI Foundations" to answer the question. If additional explanation is needed, provide an example.

**Context (Pages {page_numbers_str}):**
{combined_text}

**User's question:**
{query}

**Remember to indicate the specific page numbers.**
"""
            primary_responses = generate_diverse_responses(prompt)
            primary_answer = aggregate_responses(primary_responses)
            
            # Construct additional prompt for explanations
            explanation_prompt = f"""
The user has a question about a complex topic. Could you provide an explanation or example for better understanding?

**User's question:**
{query}

**Primary answer:**
{primary_answer}
"""
            explanation_responses = generate_diverse_responses(explanation_prompt)
            explanation_answer = aggregate_responses(explanation_responses)

            # Combine primary answer and explanation
            full_response = f"{primary_answer}\n\n{explanation_answer}\n\n_From 'Generative AI Foundations,' pages {page_numbers_str}_"
            cache[query] = full_response
            logging.info(f"Generated response for query: {query}")
            return full_response

        else:
            # General knowledge fallback
            prompt = f"""
The user asked a question that is not covered in "Generative AI Foundations." Please provide a helpful answer using general knowledge.

**User's question:**
{query}
"""
            fallback_responses = generate_diverse_responses(prompt)
            fallback_answer = aggregate_responses(fallback_responses)
            cache[query] = fallback_answer
            return fallback_answer

    except Exception as e:
        logging.error(f"Error generating answer: {str(e)}")
        return "Sorry, an error occurred while generating the answer."

# --------------------- Gradio Interface ---------------------

def gradio_interface(user_query, history):
    response = generate_answer(user_query)
    history = history or []
    history.append({"role": "user", "content": user_query})
    history.append({"role": "assistant", "content": response})
    return history, history

# Create the Gradio interface
with gr.Blocks(css=".gradio-container {background-color: #f0f0f0}") as iface:
    gr.Markdown("""
    # **Generative AI Foundations Assistant**
    *Explore insights and get explanations with real-life examples from "Generative AI Foundations in Python".*
    """)

    chatbot = gr.Chatbot(height=500, type='messages')
    state = gr.State([])

    with gr.Row():
        txt = gr.Textbox(
            show_label=False,
            placeholder="Type your message here and press Enter",
            container=False
        )
        submit_btn = gr.Button("Send")

    def submit_message(user_query, history):
        history = history or []
        history.append({"role": "user", "content": user_query})
        return "", history

    def bot_response(history):
        user_query = history[-1]['content']
        response = generate_answer(user_query)
        history.append({"role": "assistant", "content": response})
        return history

    txt.submit(submit_message, [txt, state], [txt, state], queue=False).then(
        bot_response, state, chatbot
    )
    submit_btn.click(submit_message, [txt, state], [txt, state], queue=False).then(
        bot_response, state, chatbot
    )

    reset_btn = gr.Button("Reset Chat")
    reset_btn.click(lambda: ([], []), outputs=[chatbot, state], queue=False)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()