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import streamlit as st
import pandas as pd
from openai import OpenAI
import requests
import certifi
import tiktoken
from tiktoken import get_encoding
from pinecone import Pinecone, ServerlessSpec
import time

# Configuration
import os
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Configuration
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
PINECONE_API_KEY = "9c097a58-6008-409a-859a-668a002320f6"
INDEX_NAME = "gradient-cyber"
BATCH_SIZE = 100
MAX_RESULTS = 1000

# Initialize OpenAI
client = OpenAI(api_key=OPENAI_API_KEY)

# Initialize Pinecone
pc = Pinecone(api_key=PINECONE_API_KEY)

# Check if the index already exists before creating it
if INDEX_NAME not in pc.list_indexes().names():
    pc.create_index(
        name=INDEX_NAME,
        dimension=1536,
        metric='cosine',
        spec=ServerlessSpec(cloud='aws', region='us-east-1')
    )
index = pc.Index(INDEX_NAME)

# Define helper functions
def truncate_text(text, max_tokens):
    tokenizer = get_encoding("gpt2")
    tokens = tokenizer.encode(text)
    return tokenizer.decode(tokens[:max_tokens])

def generate_embedding(text):
    max_retries = 3
    for attempt in range(max_retries):
        try:
            response = client.embeddings.create(
                model="text-embedding-ada-002",
                input=text
            )
            return response.data[0].embedding, response.usage.total_tokens
        except Exception as e:
            if attempt == max_retries - 1:
                st.error(f"Error creating embedding after {max_retries} attempts: {str(e)}")
                return None, 0
            time.sleep(2 ** attempt)  # Exponential backoff

def upsert_in_batches(index, vectors, batch_size=100):
    batches = [vectors[i:i + batch_size] for i in range(0, len(vectors), batch_size)]
    for batch in batches:
        try:
            index.upsert(vectors=batch, namespace="ns1")
        except Exception as e:
            st.error(f"Error upserting batch: {e}")

def num_tokens_from_string(string: str, encoding_name: str = "cl100k_base") -> int:
    encoding = tiktoken.get_encoding(encoding_name)
    num_tokens = len(encoding.encode(string))
    return num_tokens

def semantic_similarity(text1, text2):
    embedding1, _ = generate_embedding(text1)
    embedding2, _ = generate_embedding(text2)
    if embedding1 is None or embedding2 is None:
        return 0
    return sum(a*b for a, b in zip(embedding1, embedding2))

def expand_query(original_query):
    try:
        expansion_prompt = f"Expand the following query into 3-5 related questions or terms: '{original_query}'"
        
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": expansion_prompt}],
            max_tokens=100,
            temperature=0.7
        )
        return original_query + " " + response.choices[0].message.content
    except Exception as e:
        st.error(f"Error in query expansion: {str(e)}")
        return original_query  # Return original query if expansion fails

def truncate_context(context, max_tokens=14000):
    encoding = tiktoken.get_encoding("cl100k_base")
    encoded = encoding.encode(context)
    truncated = encoded[:max_tokens]
    return encoding.decode(truncated)

# Streamlit UI for file upload
st.title("Gradient-cyber")
uploaded_file = st.file_uploader("Upload an Excel file", type=["xlsx"])

if uploaded_file is not None:
    st.write("File uploaded successfully!")
    # Load Excel file
    df = pd.read_excel(uploaded_file)
    st.write("Excel file loaded:")
    st.write(df.head())

    # Concatenate text from all columns for each row into a readable sentence
    def create_meaningful_sentence(row):
        return '. '.join([f"{col.replace('_', ' ')}: {row[col]}" for col in df.columns])

    df['combined_text'] = df.apply(create_meaningful_sentence, axis=1)
    st.write("Columns concatenated into meaningful sentences:")
    st.write(df[['combined_text']].head())

    vectors = []
    # Process each row in the DataFrame
    total_tokens_used = 0
    total_requests = 0
    for i, row in df.iterrows():
        text = row['combined_text']
        # Truncate text to fit within the model's maximum context length
        text = truncate_text(text, max_tokens=8192)
        embedding, tokens_used = generate_embedding(text)
        if embedding is not None:
            total_tokens_used += tokens_used
            total_requests += 1
            
            # Truncate text fields to reduce metadata size
            def truncate_field(field, max_length=500):
                return str(field)[:max_length] if not pd.isna(field) else ''

            # Prepare metadata with handling NaN values and converting to string
            metadata = {
                "ID": truncate_field(row['ID']),
                "eventDtgTime": truncate_field(row['eventDtgTime']),
                "alerts": truncate_field(row.get('alerts', '')),
                "displayTitle": truncate_field(row['displayTitle']),
                "instantAnalytics": truncate_field(row.get('instantAnalytics', '')),
                "detailedText": truncate_field(row.get('detailedText', '')),
                "msgPrecs": truncate_field(row.get('msgPrecs', '')),
                "unit": truncate_field(row.get('unit', '')),
                "size": truncate_field(row.get('size', '')),
                "embedHtml": truncate_field(row.get('embedHtml', '')),
                "dataSources": truncate_field(row.get('dataSources', '')),
                "snippetText": truncate_field(row.get('snippetText', '')),
                "contentLink": truncate_field(row.get('contentLink', '')),
                "description": truncate_field(row.get('description', '')),
                "imageDescription": truncate_field(row.get('imageDescription', '')),
                "reportSummary": truncate_field(row.get('reportSummary', '')),
                "authorName": truncate_field(row.get('authorName', '')),
                "timeReportCompleted": truncate_field(row.get('timeReportCompleted', '')),
                "attachment": truncate_field(row.get('attachment', '')),
                "latitude": truncate_field(row.get('latitude', '')),
                "securityLevels": truncate_field(row.get('securityLevels', '')),
                "imagereSourceLink": truncate_field(row.get('imagereSourceLink', '')),
                "eventDtg": truncate_field(row.get('eventDtg', '')),
                "status": truncate_field(row.get('status', '')),
                "users": truncate_field(row.get('users', '')),
                "name": truncate_field(row.get('name', '')),
                "sessions": truncate_field(row.get('sessions', '')),
                "fiscalStatus": truncate_field(row.get('fiscalStatus', '')),
                "sentimentSummary": truncate_field(row.get('sentimentSummary', '')),
                "sourceOrg": truncate_field(row.get('sourceOrg', '')),
                "dateCreated": truncate_field(row.get('dateCreated', '')),
                "active": truncate_field(row.get('active', '')),
                "responseSummary": truncate_field(row.get('responseSummary', '')),
                "comparisonCommunitiesCountries": truncate_field(row.get('comparisonCommunitiesCountries', '')),
                "activity": truncate_field(row.get('activity', '')),
                "applications": truncate_field(row.get('applications', '')),
                "url": truncate_field(row.get('url', '')),
                "timeZones": truncate_field(row.get('timeZones', '')),
                "location": truncate_field(row.get('location', '')),
                "longitude": truncate_field(row.get('longitude', '')),
                "dateModified": truncate_field(row.get('dateModified', '')),
                "pedigrees": truncate_field(row.get('pedigrees', '')),
                "gistComment": truncate_field(row.get('gistComment', '')),
                "tag": truncate_field(row.get('tag', '')),
                "geoCode": truncate_field(row.get('geoCode', '')),
                "time": truncate_field(row.get('time', '')),
                "timeReportRouted": truncate_field(row.get('timeReportRouted', '')),
                "rteToOrg": truncate_field(row.get('rteToOrg', '')),
                "copyReportToOrg": truncate_field(row.get('copyReportToOrg', '')),
                "sourceOrganization": truncate_field(row.get('sourceOrganization', '')),
                "coordinates": truncate_field(row.get('coordinates', '')),
                "image1": truncate_field(row.get('image1', '')),
                "image2": truncate_field(row.get('image2', '')),
                "image3": truncate_field(row.get('image3', '')),
                "image4": truncate_field(row.get('image4', '')),
                "image5": truncate_field(row.get('image5', '')),
                "numEmailsSent": truncate_field(row.get('numEmailsSent', '')),
                "lastEmailDate": truncate_field(row.get('lastEmailDate', '')),
                "reportDtg": truncate_field(row.get('reportDtg', '')),
                "metadata": truncate_field(row.get('metadata', '')),
                "eventOrganizations": truncate_field(row.get('eventOrganizations', '')),
                "classification": truncate_field(row.get('classification', '')),
                "assetIPs": truncate_field(row.get('assetIPs', '')),
                "sitrepTemplate": truncate_field(row.get('sitrepTemplate', '')),
                "industry": truncate_field(row.get('industry', '')),
                "networkSegmentList": truncate_field(row.get('networkSegmentList', '')),
                "approvedDate": truncate_field(row.get('approvedDate', '')),
                "incident": truncate_field(row.get('incident', '')),
                "sendEmail": truncate_field(row.get('sendEmail', '')),
                "newFormat": truncate_field(row.get('newFormat', '')),
                "duMapping": truncate_field(row.get('duMapping', '')),
                "jsonTag": truncate_field(row.get('jsonTag', '')),
                "createdFrom": truncate_field(row.get('createdFrom', '')),
                "integrationData": truncate_field(row.get('integrationData', '')),
                "mtti": truncate_field(row.get('mtti', '')),
                "mttd": truncate_field(row.get('mttd', '')),
                "mttr": truncate_field(row.get('mttr', '')),
                "oldEventDate": truncate_field(row.get('oldEventDate', '')),
                "org_event_name": truncate_field(row.get('org_event_name', '')),
                "combined_text": text  # Add combined text to metadata
            }
            
            vectors.append({'id': str(row['ID']), 'values': embedding, 'metadata': metadata})

    if vectors:
        upsert_in_batches(index, vectors, BATCH_SIZE)
        st.success(f"Data successfully uploaded to Pinecone.")
        st.info(f"Total tokens used: {total_tokens_used}")
        st.info(f"Total requests made: {total_requests}")
    else:
        st.warning("No embeddings were generated.")

# Query input and response
query = st.text_input("Enter your query:")

if query:
    try:
        expanded_query = expand_query(query)
        query_embedding, _ = generate_embedding(expanded_query)
        if query_embedding is not None:
            # Perform the search in Pinecone
            results = index.query(
                namespace="ns1",
                vector=query_embedding,
                top_k=50,
                include_metadata=True
            )

            # Semantic filtering
            filtered_results = sorted(
                results['matches'],
                key=lambda x: semantic_similarity(query, x['metadata']['combined_text']),
                reverse=True
            )[:10]  # Reduced from 20 to 15 to further limit context size

            # Prepare context for GPT
            context = "\n".join([
                f"ID: {match['id']}\n" +
                f"Event Date/Time: {match['metadata'].get('eventDtgTime', 'N/A')}\n" +
                f"Display Title: {match['metadata'].get('displayTitle', 'N/A')}\n" +
                f"Status: {match['metadata'].get('status', 'N/A')}\n" +
                f"Combined Text: {match['metadata'].get('combined_text', 'N/A')}\n" +
                "---"
                for match in filtered_results
            ])

            # Truncate the context
            truncated_context = truncate_context(context)

            # Prepare the prompt for GPT
            system_prompt = """Core Capabilities: Expert Knowledge on SITREPs: Understand and explain SITREP components "
                         "like threat analysis, incident summaries, and risk assessments. Flexible Query Handling: "
                         "Interpret and respond to diverse queries, supporting access to data by date, theme, severity, etc. "
                         "Data Retrieval: Provide specific information such as incident dates, threat actors, and mitigation strategies. "
                         "Offer summaries or detailed reports based on user needs. Analytical Engagement: Engage in discussions, offering insights and hypotheses. "
                         "Support user analysis of cyber threats and incidents. Interaction Guidelines: Understanding Queries: "
                         "Use NLP to interpret user queries and ask clarifying questions if needed. Providing Context: Give context to help users understand the relevance and implications of information. "
                         "Customizing Detail: Adjust detail levels based on user preferences, providing summaries or deep dives. "
                         "Allow users to specify the format of information (e.g., bullet points, detailed paragraphs, tables). "
                         "Encouraging Exploration: Suggest related queries and additional information. Provide thorough explanations to support learning and decision-making."""
            user_prompt = f"""Query: {query}
Relevant Information:
{truncated_context}
Provide a clear, concise, and comprehensive answer. Synthesize information from multiple entries if necessary. Cite specific details and examples when applicable. If information is missing, state what is known and what remains uncertain."""

            response = client.chat.completions.create(
                model="gpt-4o-mini",
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": user_prompt}
                ],
                max_tokens=1000,
                temperature=0.7
            )
            answer = response.choices[0].message.content
            st.write("Answer to your query:")
            st.write(answer)
        else:
            st.error("Failed to generate query embedding.")
    except Exception as e:
        st.error(f"Error processing query: {str(e)}")

	import streamlit as st
	import pandas as pd
	from openai import OpenAI
	import requests
	import certifi
	import tiktoken
	from tiktoken import get_encoding
	from pinecone import Pinecone, ServerlessSpec
	import time

	# Configuration
	import os
	from dotenv import load_dotenv

	# Load environment variables from .env file
	load_dotenv()

	# Configuration
	OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
	PINECONE_API_KEY = "9c097a58-6008-409a-859a-668a002320f6"
	INDEX_NAME = "gradient-cyber"
	BATCH_SIZE = 100
	MAX_RESULTS = 1000

	# Initialize OpenAI
	client = OpenAI(api_key=OPENAI_API_KEY)

	# Initialize Pinecone
	pc = Pinecone(api_key=PINECONE_API_KEY)

	# Check if the index already exists before creating it
	if INDEX_NAME not in pc.list_indexes().names():
	pc.create_index(
	name=INDEX_NAME,
	dimension=1536,
	metric='cosine',
	spec=ServerlessSpec(cloud='aws', region='us-east-1')
	)
	index = pc.Index(INDEX_NAME)

	# Define helper functions
	def truncate_text(text, max_tokens):
	tokenizer = get_encoding("gpt2")
	tokens = tokenizer.encode(text)
	return tokenizer.decode(tokens[:max_tokens])

	def generate_embedding(text):
	max_retries = 3
	for attempt in range(max_retries):
	try:
	response = client.embeddings.create(
	model="text-embedding-ada-002",
	input=text
	)
	return response.data[0].embedding, response.usage.total_tokens
	except Exception as e:
	if attempt == max_retries - 1:
	st.error(f"Error creating embedding after {max_retries} attempts: {str(e)}")
	return None, 0
	time.sleep(2 ** attempt) # Exponential backoff

	def upsert_in_batches(index, vectors, batch_size=100):
	batches = [vectors[i:i + batch_size] for i in range(0, len(vectors), batch_size)]
	for batch in batches:
	try:
	index.upsert(vectors=batch, namespace="ns1")
	except Exception as e:
	st.error(f"Error upserting batch: {e}")

	def num_tokens_from_string(string: str, encoding_name: str = "cl100k_base") -> int:
	encoding = tiktoken.get_encoding(encoding_name)
	num_tokens = len(encoding.encode(string))
	return num_tokens

	def semantic_similarity(text1, text2):
	embedding1, _ = generate_embedding(text1)
	embedding2, _ = generate_embedding(text2)
	if embedding1 is None or embedding2 is None:
	return 0
	return sum(a*b for a, b in zip(embedding1, embedding2))

	def expand_query(original_query):
	try:
	expansion_prompt = f"Expand the following query into 3-5 related questions or terms: '{original_query}'"

	response = client.chat.completions.create(
	model="gpt-4o-mini",
	messages=[{"role": "user", "content": expansion_prompt}],
	max_tokens=100,
	temperature=0.7
	)
	return original_query + " " + response.choices[0].message.content
	except Exception as e:
	st.error(f"Error in query expansion: {str(e)}")
	return original_query # Return original query if expansion fails

	def truncate_context(context, max_tokens=14000):
	encoding = tiktoken.get_encoding("cl100k_base")
	encoded = encoding.encode(context)
	truncated = encoded[:max_tokens]
	return encoding.decode(truncated)

	# Streamlit UI for file upload
	st.title("Gradient-cyber")
	uploaded_file = st.file_uploader("Upload an Excel file", type=["xlsx"])

	if uploaded_file is not None:
	st.write("File uploaded successfully!")
	# Load Excel file
	df = pd.read_excel(uploaded_file)
	st.write("Excel file loaded:")
	st.write(df.head())

	# Concatenate text from all columns for each row into a readable sentence
	def create_meaningful_sentence(row):
	return '. '.join([f"{col.replace('_', ' ')}: {row[col]}" for col in df.columns])

	df['combined_text'] = df.apply(create_meaningful_sentence, axis=1)
	st.write("Columns concatenated into meaningful sentences:")
	st.write(df[['combined_text']].head())

	vectors = []
	# Process each row in the DataFrame
	total_tokens_used = 0
	total_requests = 0
	for i, row in df.iterrows():
	text = row['combined_text']
	# Truncate text to fit within the model's maximum context length
	text = truncate_text(text, max_tokens=8192)
	embedding, tokens_used = generate_embedding(text)
	if embedding is not None:
	total_tokens_used += tokens_used
	total_requests += 1

	# Truncate text fields to reduce metadata size
	def truncate_field(field, max_length=500):
	return str(field)[:max_length] if not pd.isna(field) else ''

	# Prepare metadata with handling NaN values and converting to string
	metadata = {
	"ID": truncate_field(row['ID']),
	"eventDtgTime": truncate_field(row['eventDtgTime']),
	"alerts": truncate_field(row.get('alerts', '')),
	"displayTitle": truncate_field(row['displayTitle']),
	"instantAnalytics": truncate_field(row.get('instantAnalytics', '')),
	"detailedText": truncate_field(row.get('detailedText', '')),
	"msgPrecs": truncate_field(row.get('msgPrecs', '')),
	"unit": truncate_field(row.get('unit', '')),
	"size": truncate_field(row.get('size', '')),
	"embedHtml": truncate_field(row.get('embedHtml', '')),
	"dataSources": truncate_field(row.get('dataSources', '')),
	"snippetText": truncate_field(row.get('snippetText', '')),
	"contentLink": truncate_field(row.get('contentLink', '')),
	"description": truncate_field(row.get('description', '')),
	"imageDescription": truncate_field(row.get('imageDescription', '')),
	"reportSummary": truncate_field(row.get('reportSummary', '')),
	"authorName": truncate_field(row.get('authorName', '')),
	"timeReportCompleted": truncate_field(row.get('timeReportCompleted', '')),
	"attachment": truncate_field(row.get('attachment', '')),
	"latitude": truncate_field(row.get('latitude', '')),
	"securityLevels": truncate_field(row.get('securityLevels', '')),
	"imagereSourceLink": truncate_field(row.get('imagereSourceLink', '')),
	"eventDtg": truncate_field(row.get('eventDtg', '')),
	"status": truncate_field(row.get('status', '')),
	"users": truncate_field(row.get('users', '')),
	"name": truncate_field(row.get('name', '')),
	"sessions": truncate_field(row.get('sessions', '')),
	"fiscalStatus": truncate_field(row.get('fiscalStatus', '')),
	"sentimentSummary": truncate_field(row.get('sentimentSummary', '')),
	"sourceOrg": truncate_field(row.get('sourceOrg', '')),
	"dateCreated": truncate_field(row.get('dateCreated', '')),
	"active": truncate_field(row.get('active', '')),
	"responseSummary": truncate_field(row.get('responseSummary', '')),
	"comparisonCommunitiesCountries": truncate_field(row.get('comparisonCommunitiesCountries', '')),
	"activity": truncate_field(row.get('activity', '')),
	"applications": truncate_field(row.get('applications', '')),
	"url": truncate_field(row.get('url', '')),
	"timeZones": truncate_field(row.get('timeZones', '')),
	"location": truncate_field(row.get('location', '')),
	"longitude": truncate_field(row.get('longitude', '')),
	"dateModified": truncate_field(row.get('dateModified', '')),
	"pedigrees": truncate_field(row.get('pedigrees', '')),
	"gistComment": truncate_field(row.get('gistComment', '')),
	"tag": truncate_field(row.get('tag', '')),
	"geoCode": truncate_field(row.get('geoCode', '')),
	"time": truncate_field(row.get('time', '')),
	"timeReportRouted": truncate_field(row.get('timeReportRouted', '')),
	"rteToOrg": truncate_field(row.get('rteToOrg', '')),
	"copyReportToOrg": truncate_field(row.get('copyReportToOrg', '')),
	"sourceOrganization": truncate_field(row.get('sourceOrganization', '')),
	"coordinates": truncate_field(row.get('coordinates', '')),
	"image1": truncate_field(row.get('image1', '')),
	"image2": truncate_field(row.get('image2', '')),
	"image3": truncate_field(row.get('image3', '')),
	"image4": truncate_field(row.get('image4', '')),
	"image5": truncate_field(row.get('image5', '')),
	"numEmailsSent": truncate_field(row.get('numEmailsSent', '')),
	"lastEmailDate": truncate_field(row.get('lastEmailDate', '')),
	"reportDtg": truncate_field(row.get('reportDtg', '')),
	"metadata": truncate_field(row.get('metadata', '')),
	"eventOrganizations": truncate_field(row.get('eventOrganizations', '')),
	"classification": truncate_field(row.get('classification', '')),
	"assetIPs": truncate_field(row.get('assetIPs', '')),
	"sitrepTemplate": truncate_field(row.get('sitrepTemplate', '')),
	"industry": truncate_field(row.get('industry', '')),
	"networkSegmentList": truncate_field(row.get('networkSegmentList', '')),
	"approvedDate": truncate_field(row.get('approvedDate', '')),
	"incident": truncate_field(row.get('incident', '')),
	"sendEmail": truncate_field(row.get('sendEmail', '')),
	"newFormat": truncate_field(row.get('newFormat', '')),
	"duMapping": truncate_field(row.get('duMapping', '')),
	"jsonTag": truncate_field(row.get('jsonTag', '')),
	"createdFrom": truncate_field(row.get('createdFrom', '')),
	"integrationData": truncate_field(row.get('integrationData', '')),
	"mtti": truncate_field(row.get('mtti', '')),
	"mttd": truncate_field(row.get('mttd', '')),
	"mttr": truncate_field(row.get('mttr', '')),
	"oldEventDate": truncate_field(row.get('oldEventDate', '')),
	"org_event_name": truncate_field(row.get('org_event_name', '')),
	"combined_text": text # Add combined text to metadata
	}

	vectors.append({'id': str(row['ID']), 'values': embedding, 'metadata': metadata})

	if vectors:
	upsert_in_batches(index, vectors, BATCH_SIZE)
	st.success(f"Data successfully uploaded to Pinecone.")
	st.info(f"Total tokens used: {total_tokens_used}")
	st.info(f"Total requests made: {total_requests}")
	else:
	st.warning("No embeddings were generated.")

	# Query input and response
	query = st.text_input("Enter your query:")

	if query:
	try:
	expanded_query = expand_query(query)
	query_embedding, _ = generate_embedding(expanded_query)
	if query_embedding is not None:
	# Perform the search in Pinecone
	results = index.query(
	namespace="ns1",
	vector=query_embedding,
	top_k=50,
	include_metadata=True
	)

	# Semantic filtering
	filtered_results = sorted(
	results['matches'],
	key=lambda x: semantic_similarity(query, x['metadata']['combined_text']),
	reverse=True
	)[:10] # Reduced from 20 to 15 to further limit context size

	# Prepare context for GPT
	context = "\n".join([
	f"ID: {match['id']}\n" +
	f"Event Date/Time: {match['metadata'].get('eventDtgTime', 'N/A')}\n" +
	f"Display Title: {match['metadata'].get('displayTitle', 'N/A')}\n" +
	f"Status: {match['metadata'].get('status', 'N/A')}\n" +
	f"Combined Text: {match['metadata'].get('combined_text', 'N/A')}\n" +
	"---"
	for match in filtered_results
	])

	# Truncate the context
	truncated_context = truncate_context(context)

	# Prepare the prompt for GPT
	system_prompt = """Core Capabilities: Expert Knowledge on SITREPs: Understand and explain SITREP components "
	"like threat analysis, incident summaries, and risk assessments. Flexible Query Handling: "
	"Interpret and respond to diverse queries, supporting access to data by date, theme, severity, etc. "
	"Data Retrieval: Provide specific information such as incident dates, threat actors, and mitigation strategies. "
	"Offer summaries or detailed reports based on user needs. Analytical Engagement: Engage in discussions, offering insights and hypotheses. "
	"Support user analysis of cyber threats and incidents. Interaction Guidelines: Understanding Queries: "
	"Use NLP to interpret user queries and ask clarifying questions if needed. Providing Context: Give context to help users understand the relevance and implications of information. "
	"Customizing Detail: Adjust detail levels based on user preferences, providing summaries or deep dives. "
	"Allow users to specify the format of information (e.g., bullet points, detailed paragraphs, tables). "
	"Encouraging Exploration: Suggest related queries and additional information. Provide thorough explanations to support learning and decision-making."""
	user_prompt = f"""Query: {query}
	Relevant Information:
	{truncated_context}
	Provide a clear, concise, and comprehensive answer. Synthesize information from multiple entries if necessary. Cite specific details and examples when applicable. If information is missing, state what is known and what remains uncertain."""

	response = client.chat.completions.create(
	model="gpt-4o-mini",
	messages=[
	{"role": "system", "content": system_prompt},
	{"role": "user", "content": user_prompt}
	],
	max_tokens=1000,
	temperature=0.7
	)
	answer = response.choices[0].message.content
	st.write("Answer to your query:")
	st.write(answer)
	else:
	st.error("Failed to generate query embedding.")
	except Exception as e:
	st.error(f"Error processing query: {str(e)}")