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import streamlit as st
import PyPDF2
import openai
import faiss
import os
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from io import StringIO
# Function to extract text from a PDF file
def extract_text_from_pdf(pdf_file):
reader = PyPDF2.PdfReader(pdf_file)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
# Function to generate embeddings for a piece of text
def get_embeddings(text, model="text-embedding-ada-002"):
response = openai.Embedding.create(input=[text], model=model)
return response['data'][0]['embedding']
# Function to search for similar content
def search_similar(query_embedding, index, stored_texts, top_k=3):
distances, indices = index.search(np.array([query_embedding]), top_k)
results = [(stored_texts[i], distances[0][idx]) for idx, i in enumerate(indices[0])]
return results
# Function to generate code based on a prompt
def generate_code_from_prompt(prompt, model="gpt-4o-mini"):
response = openai.ChatCompletion.create(
model=model,
messages=[{"role": "user", "content": prompt}]
)
return response['choices'][0]['message']['content']
# Function to save code to a .txt file
def save_code_to_file(code, filename="generated_code.txt"):
with open(filename, "w") as f:
f.write(code)
# Function to generate AI-based study notes and summaries
def generate_summary(text):
prompt = f"Summarize the following text into key points:\n\n{text}"
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}]
)
return response['choices'][0]['message']['content']
# Function to fix bugs in code
def fix_code_bugs(buggy_code, model="gpt-4o-mini"):
prompt = f"The following code has bugs or issues. Please identify and fix the problems. If possible, provide explanations for the changes made.\n\nBuggy Code:\n{buggy_code}\n\nFixed Code:"
response = openai.ChatCompletion.create(
model=model,
messages=[{"role": "user", "content": prompt}]
)
return response['choices'][0]['message']['content']
# Function to generate AI-based mathematical solutions
def generate_math_solution(query):
prompt = f"Explain and solve the following mathematical problem step by step: {query}"
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}]
)
return response['choices'][0]['message']['content']
from PIL import Image # Required for local image files
# Streamlit app starts here
st.set_page_config(page_title="AI Assistance", page_icon=":robot:", layout="wide")
# Display a logo or icon
image = Image.open("14313824.png") # Path to your image file
st.image(image, width=200) # You can adjust the width as needed
# Streamlit app starts here
st.title("AI Assistance")
# Input OpenAI API key
openai_api_key = st.text_input("Enter your OpenAI API key:", type="password")
if openai_api_key:
openai.api_key = openai_api_key
# Sidebar to toggle between Course Query Assistant, Code Generator, Bug Fixer, etc.
st.sidebar.title("Select Mode")
mode = st.sidebar.radio("Choose an option", (
"Course Query Assistant",
"Code Generator",
"AI Chatbot Tutor",
"AI Study Notes & Summaries",
"Code Bug Fixer",
"Mathematics Assistant", # Added option for Math
"Biology Assistant", # Added option for Biology
"Chemistry Assistant", # Added option for Chemistry
"Physics Assistant", # Added option for Physics
"Voice Chat",
"Image Chat",
"English To Japanese",
"Text to Image Generator",
"Graph Tutorial",
"Text-To-Diagram-Generator"
))
# Add Contact information in the sidebar
st.sidebar.markdown("""
## Contact
For any questions or issues, please contact:
- **Email**: [[email protected]](mailto:[email protected])
- **GitHub**: [Click here to access the Github Profile](https://github.com/shukdevtroy)
- **WhatsApp**: [Click here to chat](https://wa.me/+8801719296601)
- **HuggingFace Profile**: [Click here to access the HuggingFace Profile](https://huggingface.co/shukdevdatta123)
""")
if mode == "Course Query Assistant":
st.header("Course Query Assistant")
# Display image/logo in the "Course Query Assistant" section (optional)
course_query_image = Image.open("Capture.PNG") # Ensure the file is in the correct directory
st.image(course_query_image, width=150) # Adjust the size as per preference
# Upload course materials
uploaded_files = st.file_uploader("Upload Course Materials (PDFs)", type=["pdf"], accept_multiple_files=True)
if uploaded_files:
st.write("Processing uploaded course materials...")
# Extract text and generate embeddings for all uploaded PDFs
course_texts = []
for uploaded_file in uploaded_files:
text = extract_text_from_pdf(uploaded_file)
course_texts.append(text)
# Combine all course materials into one large text
combined_text = " ".join(course_texts)
# Split combined text into smaller chunks for embedding (max tokens ~1000)
chunks = [combined_text[i:i+1000] for i in range(0, len(combined_text), 1000)]
# Generate embeddings for all chunks
embeddings = [get_embeddings(chunk) for chunk in chunks]
# Convert the list of embeddings into a NumPy array (shape: [num_chunks, embedding_size])
embeddings_np = np.array(embeddings).astype("float32")
# Create a FAISS index for similarity search
index = faiss.IndexFlatL2(len(embeddings_np[0])) # Use the length of the embedding vectors for the dimension
index.add(embeddings_np)
st.write("Course materials have been processed and indexed.")
# User query
query = st.text_input("Enter your question about the course materials:")
if query:
# Generate embedding for the query
query_embedding = get_embeddings(query)
# Search for similar chunks in the FAISS index
results = search_similar(query_embedding, index, chunks)
# Create the context for the GPT prompt
context = "\n".join([result[0] for result in results])
modified_prompt = f"Context: {context}\n\nQuestion: {query}\n\nProvide a detailed answer based on the context."
# Get the GPT-4 response
response = openai.ChatCompletion.create(
model="gpt-4o-mini", # Update to GPT-4 (or your desired model)
messages=[{"role": "user", "content": modified_prompt}]
)
# Get the response content
response_content = response['choices'][0]['message']['content']
# Display the response in Streamlit (Intelligent Reply)
st.write("### Intelligent Reply:")
st.write(response_content)
elif mode == "Code Generator":
st.header("Code Generator")
# Display image/logo in the "Course Query Assistant" section (optional)
codegen = Image.open("9802381.png") # Ensure the file is in the correct directory
st.image(codegen, width=150) # Adjust the size as per preference
# Code generation prompt input
code_prompt = st.text_area("Describe the code you want to generate:",
"e.g., Write a Python program that generates Fibonacci numbers.")
if st.button("Generate Code"):
if code_prompt:
with st.spinner("Generating code..."):
# Generate code using GPT-4
generated_code = generate_code_from_prompt(code_prompt)
# Clean the generated code to ensure only code is saved (removing comments or additional text)
clean_code = "\n".join([line for line in generated_code.splitlines() if not line.strip().startswith("#")])
# Save the clean code to a file
save_code_to_file(clean_code)
# Display the generated code
st.write("### Generated Code:")
st.code(clean_code, language="python")
# Provide a download link for the generated code
with open("generated_code.txt", "w") as f:
f.write(clean_code)
st.download_button(
label="Download Generated Code",
data=open("generated_code.txt", "rb").read(),
file_name="generated_code.txt",
mime="text/plain"
)
else:
st.error("Please provide a prompt to generate the code.")
elif mode == "AI Chatbot Tutor":
st.header("AI Chatbot Tutor")
# Display image/logo in the "Course Query Assistant" section (optional)
aitut = Image.open("910372.png") # Ensure the file is in the correct directory
st.image(aitut, width=150) # Adjust the size as per preference
# Chat interface for the AI tutor
chat_history = []
def chat_with_bot(query):
chat_history.append({"role": "user", "content": query})
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=chat_history
)
chat_history.append({"role": "assistant", "content": response['choices'][0]['message']['content']})
return response['choices'][0]['message']['content']
user_query = st.text_input("Ask a question:")
if user_query:
with st.spinner("Getting answer..."):
bot_response = chat_with_bot(user_query)
st.write(f"### AI Response: {bot_response}")
elif mode == "AI Study Notes & Summaries":
st.header("AI Study Notes & Summaries")
# Display image/logo in the "Course Query Assistant" section (optional)
aisum = Image.open("sum.png") # Ensure the file is in the correct directory
st.image(aisum, width=150) # Adjust the size as per preference
# Upload course materials for summarization
uploaded_files_for_summary = st.file_uploader("Upload Course Materials (PDFs) for Summarization", type=["pdf"], accept_multiple_files=True)
if uploaded_files_for_summary:
st.write("Generating study notes and summaries...")
# Extract text from PDFs
all_text = ""
for uploaded_file in uploaded_files_for_summary:
text = extract_text_from_pdf(uploaded_file)
all_text += text
# Generate summary using AI
summary = generate_summary(all_text)
# Display the summary
st.write("### AI-Generated Summary:")
st.write(summary)
elif mode == "Code Bug Fixer":
st.header("Code Bug Fixer")
# Display image/logo in the "Course Query Assistant" section (optional)
aibug = Image.open("bug.png") # Ensure the file is in the correct directory
st.image(aibug, width=150) # Adjust the size as per preference
# User input for buggy code
buggy_code = st.text_area("Enter your buggy code here:")
if st.button("Fix Code"):
if buggy_code:
with st.spinner("Fixing code..."):
# Fix bugs using GPT-4
fixed_code = fix_code_bugs(buggy_code)
# Display the fixed code
st.write("### Fixed Code:")
st.code(fixed_code, language="python")
# Provide a download link for the fixed code
with open("fixed_code.txt", "w") as f:
f.write(fixed_code)
st.download_button(
label="Download Fixed Code",
data=open("fixed_code.txt", "rb").read(),
file_name="fixed_code.txt",
mime="text/plain"
)
else:
st.error("Please enter some buggy code to fix.")
elif mode == "Mathematics Assistant":
st.header("Mathematics Assistant")
# Display image/logo in the "Mathematics Assistant" section (optional)
math_icon = Image.open("math_icon.PNG") # Ensure the file is in the correct directory
st.image(math_icon, width=150) # Adjust the size as per preference
# User input for math questions
math_query = st.text_input("Ask a mathematics-related question:")
if st.button("Solve Problem"):
if math_query:
with st.spinner("Generating solution..."):
# Generate the solution using GPT-4
solution = generate_math_solution(math_query)
# Render the solution with LaTeX for mathematical notations
formatted_solution = f"""
### Solution to the Problem
**Problem:** {math_query}
**Solution:**
{solution}
"""
st.markdown(formatted_solution)
else:
st.error("Please enter a math problem to solve.")
# **New Section: Biology Assistant**
elif mode == "Biology Assistant":
st.header("Biology Assistant")
# Display image/logo in the "Biology Assistant" section (optional)
bio_icon = Image.open("bio_icon.PNG") # Ensure the file is in the correct directory
st.image(bio_icon, width=150) # Adjust the size as per preference
# User input for biology questions
bio_query = st.text_input("Ask a biology-related question:")
if bio_query:
with st.spinner("Getting answer..."):
prompt = f"Answer the following biology question: {bio_query}"
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}]
)
answer = response['choices'][0]['message']['content']
st.write(f"### Answer: {answer}")
# **New Section: Chemistry Assistant**
elif mode == "Chemistry Assistant":
st.header("Chemistry Assistant")
# Display image/logo in the "Chemistry Assistant" section (optional)
chem_icon = Image.open("chem.PNG") # Ensure the file is in the correct directory
st.image(chem_icon, width=150) # Adjust the size as per preference
# User input for chemistry questions
chem_query = st.text_input("Ask a chemistry-related question:")
if chem_query:
with st.spinner("Getting answer..."):
prompt = f"Answer the following chemistry question: {chem_query}"
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}]
)
answer = response['choices'][0]['message']['content']
st.write(f"### Answer: {answer}")
# **New Section: Physics Assistant**
elif mode == "Physics Assistant":
st.header("Physics Assistant")
# Display image/logo in the "Physics Assistant" section (optional)
phys_icon = Image.open("physics_icon.PNG") # Ensure the file is in the correct directory
st.image(phys_icon, width=150) # Adjust the size as per preference
# User input for physics questions
phys_query = st.text_input("Ask a physics-related question:")
if phys_query:
with st.spinner("Getting answer..."):
prompt = f"Answer the following physics question: {phys_query}"
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}]
)
answer = response['choices'][0]['message']['content']
st.write(f"### Answer: {answer}")
# **New Section: Voice Chat**
elif mode == "Voice Chat":
st.header("Voice Chat")
# Display a description or instructions
st.write("Click the button below to go to the Voice Chat.")
# Display image/logo in the "Physics Assistant" section (optional)
gif = "200w.gif" # Ensure the file is in the correct directory
st.image(gif, use_container_width=50) # Adjust the size as per preference
# Button to navigate to the external voice chat link
if st.button("Go to Voice Chat"):
st.write("Redirecting to the voice chat...") # You can customize this message
st.markdown(f'<a href="https://shukdevdatta123-voicechat.hf.space" target="_blank">Go to Voice Chat</a>', unsafe_allow_html=True)
# **New Section: Image Chat**
elif mode == "Image Chat":
# Display image/logo in the "Physics Assistant" section (optional)
imgc = Image.open("i.jpg") # Ensure the file is in the correct directory
st.image(imgc, width=150) # Adjust the size as per preference
st.header("Image Chat")
# Display a description or instructions
st.write("Click the button below to go to the Image Chat.")
# Display image/logo in the "Physics Assistant" section (optional)
gif = "200w.gif" # Ensure the file is in the correct directory
st.image(gif, use_container_width=50) # Adjust the size as per preference
# Button to navigate to the external voice chat link
if st.button("Go to Image Chat"):
st.write("Redirecting to the image chat...") # You can customize this message
st.markdown(f'<a href="https://imagechat2278.streamlit.app/" target="_blank">Go to Image Chat</a>', unsafe_allow_html=True)
# Button to navigate to the alternative app (alternative)
if st.button("Go to Image Chat (Alternative App)"):
st.write("Redirecting to the alternative image chat...") # You can customize this message
st.markdown(f'<a href="https://imagechat.onrender.com/" target="_blank">Go to Image Chat (Alternative App)</a>', unsafe_allow_html=True)
# **New Section: English To Japanese**
elif mode == "English To Japanese":
st.header("English To Japanese")
# Display a description or instructions
st.write("Click the button below to go to the English To Japanese Translator.")
gif = "200w.gif" # Ensure the file is in the correct directory
st.image(gif, use_container_width=150) # Adjust the size as per preference
# Button to navigate to the external voice chat link
if st.button("Go to English To Japanese Translator"):
st.write("Redirecting to the English To Japanese Translator...") # You can customize this message
st.markdown(f'<a href="https://shukdevdatta123-engtojap-2-0.hf.space" target="_blank">Go to English To Japanese Translator</a>', unsafe_allow_html=True)
# **New Section: Text to Image Generator**
elif mode == "Text to Image Generator":
st.header("Text to Image Generator")
# Display a description or instructions
st.write("Click the button below to go to the Text to Image Generator.")
gif = "200w.gif" # Ensure the file is in the correct directory
st.image(gif, use_container_width=150) # Adjust the size as per preference
# Button to navigate to the external voice chat link
if st.button("Go to Text to Image Generator"):
st.write("Redirecting to the Text to Image Generator...") # You can customize this message
st.markdown(f'<a href="https://shukdevdatta123-image-generator-dall-e3.hf.space" target="_blank">Go to Text to Image Generator</a>', unsafe_allow_html=True)
# **New Section: Graph Tutorial**
elif mode == "Graph Tutorial":
st.header("Graph Tutorial")
# Display a description or instructions
st.write("Click the button below to go to Graph Tutorial.")
gif = "200w.gif" # Ensure the file is in the correct directory
st.image(gif, use_container_width=150) # Adjust the size as per preference
# Button to navigate to the external voice chat link
if st.button("Go to Graph Tutorial"):
st.write("Redirecting to Graph Tutorial...") # You can customize this message
st.markdown(f'<a href="https://shukdevdatta123-networkx-tutorial.hf.space" target="_blank">Go to Graph Tutorial</a>', unsafe_allow_html=True)
# **New Section: Text-To-Diagram-Generator**
elif mode == "Text-To-Diagram-Generator":
st.header("Text-To-Diagram-Generator")
# Display a description or instructions
st.write("Click the button below to go to Text-To-Diagram-Generator.")
gif = "200w.gif" # Ensure the file is in the correct directory
st.image(gif, use_container_width=150) # Adjust the size as per preference
# Button to navigate to the external voice chat link
if st.button("Go to Text-To-Diagram-Generator"):
st.write("Redirecting to Text-To-Diagram-Generator...") # You can customize this message
st.markdown(f'<a href="https://shukdevdatta123-text-2-diagram.hf.space" target="_blank">Go to Text-To-Diagram-Generator</a>', unsafe_allow_html=True) |