import gradio as gr
import groq
import os
import tempfile
import uuid
from dotenv import load_dotenv
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
import fitz # PyMuPDF
import base64
from PIL import Image
import io
import requests
import json
import re
from datetime import datetime, timedelta
from pathlib import Path
import torch
import numpy as np
# Load environment variables
load_dotenv()
client = groq.Client(api_key=os.getenv("GROQ_TECH_API_KEY"))
# Initialize embeddings with error handling
try:
# Force CPU usage for embeddings
embeddings = HuggingFaceInstructEmbeddings(
model_name="hkunlp/instructor-base",
model_kwargs={"device": "cpu"} # Force CPU usage
)
except Exception as e:
print(f"Warning: Failed to load primary embeddings model: {e}")
try:
embeddings = HuggingFaceInstructEmbeddings(
model_name="all-MiniLM-L6-v2",
model_kwargs={"device": "cpu"} # Force CPU usage
)
except Exception as e:
print(f"Warning: Failed to load fallback embeddings model: {e}")
embeddings = None
# Directory to store FAISS indexes with better naming
FAISS_INDEX_DIR = "faiss_indexes_tech_cpu"
if not os.path.exists(FAISS_INDEX_DIR):
os.makedirs(FAISS_INDEX_DIR)
# Dictionary to store user-specific vectorstores
user_vectorstores = {}
# Custom CSS for Tech theme
custom_css = """
:root {
--primary-color: #4285F4;
--secondary-color: #34A853;
--accent-color: #EA4335;
--light-background: #F8F9FA;
--dark-text: #202124;
--white: #FFFFFF;
--border-color: #DADCE0;
--code-bg: #F1F3F4;
}
body {
background-color: var(--light-background);
font-family: 'Google Sans', 'Roboto', sans-serif;
}
.container {
max-width: 1200px !important;
margin: 0 auto !important;
padding: 10px;
}
.header {
background-color: var(--white);
border-bottom: 1px solid var(--border-color);
padding: 15px 0;
margin-bottom: 20px;
border-radius: 12px 12px 0 0;
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
}
.header-title {
color: var(--primary-color);
font-size: 1.8rem;
font-weight: 700;
text-align: center;
}
.header-subtitle {
color: var(--dark-text);
font-size: 1rem;
text-align: center;
margin-top: 5px;
}
.chat-container {
border-radius: 12px !important;
box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important;
background-color: var(--white) !important;
border: 1px solid var(--border-color) !important;
min-height: 500px;
}
.tool-container {
background-color: var(--white);
border-radius: 12px;
box-shadow: 0 4px 6px rgba(0,0,0,0.1);
padding: 15px;
margin-bottom: 20px;
}
.code-block {
background-color: var(--code-bg);
padding: 12px;
border-radius: 8px;
font-family: 'Roboto Mono', monospace;
overflow-x: auto;
margin: 10px 0;
border-left: 3px solid var(--primary-color);
}
"""
# Helper functions for code analysis
def detect_language(extension):
"""Detect programming language from file extension"""
extension_map = {
".py": "Python",
".js": "JavaScript",
".java": "Java",
".cpp": "C++",
".c": "C",
".cs": "C#",
".php": "PHP",
".rb": "Ruby",
".go": "Go",
".ts": "TypeScript"
}
return extension_map.get(extension.lower(), "Unknown")
def calculate_complexity_metrics(content, language):
"""Calculate code complexity metrics"""
lines = content.split('\n')
total_lines = len(lines)
blank_lines = len([line for line in lines if not line.strip()])
code_lines = total_lines - blank_lines
metrics = {
"language": language,
"total_lines": total_lines,
"code_lines": code_lines,
"blank_lines": blank_lines
}
return metrics
def generate_recommendations(metrics):
"""Generate code quality recommendations based on metrics"""
recommendations = []
if metrics.get("cyclomatic_complexity", 0) > 10:
recommendations.append("🔄 High cyclomatic complexity detected. Consider breaking down complex functions.")
if metrics.get("code_lines", 0) > 300:
recommendations.append("📏 File is quite large. Consider splitting it into multiple modules.")
if metrics.get("functions", 0) > 10:
recommendations.append("🔧 Large number of functions. Consider grouping related functions into classes.")
if metrics.get("comments", 0) / max(metrics.get("code_lines", 1), 1) < 0.1:
recommendations.append("📝 Low comment ratio. Consider adding more documentation.")
return "### Recommendations\n\n" + "\n\n".join(recommendations) if recommendations else ""
# Function to process PDF files
def process_pdf(pdf_file):
if pdf_file is None:
return None, "No file uploaded", {"page_images": [], "total_pages": 0, "total_words": 0}
try:
session_id = str(uuid.uuid4())
with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as temp_file:
temp_file.write(pdf_file)
pdf_path = temp_file.name
doc = fitz.open(pdf_path)
texts = [page.get_text() for page in doc]
page_images = []
for page in doc:
pix = page.get_pixmap()
img_bytes = pix.tobytes("png")
img_base64 = base64.b64encode(img_bytes).decode("utf-8")
page_images.append(img_base64)
total_pages = len(doc)
total_words = sum(len(text.split()) for text in texts)
doc.close()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.create_documents(texts)
vectorstore = FAISS.from_documents(chunks, embeddings)
index_path = os.path.join(FAISS_INDEX_DIR, session_id)
vectorstore.save_local(index_path)
user_vectorstores[session_id] = vectorstore
os.unlink(pdf_path)
pdf_state = {"page_images": page_images, "total_pages": total_pages, "total_words": total_words}
return session_id, f"✅ Successfully processed {len(chunks)} text chunks from your PDF", pdf_state
except Exception as e:
if "pdf_path" in locals() and os.path.exists(pdf_path):
os.unlink(pdf_path)
return None, f"Error processing PDF: {str(e)}", {"page_images": [], "total_pages": 0, "total_words": 0}
# Function to generate chatbot responses with Tech theme
def generate_response(message, session_id, model_name, history):
"""Generate chatbot responses with FAISS context enhancement"""
if not message:
return history
try:
context = ""
if embeddings and session_id and session_id in user_vectorstores:
try:
print(f"Performing similarity search with session: {session_id}")
vectorstore = user_vectorstores[session_id]
# Use a higher k value to get more relevant context
docs = vectorstore.similarity_search(message, k=5)
if docs:
# Format the context more clearly with source information
context = "\n\nRelevant code context from your files:\n\n"
for i, doc in enumerate(docs, 1):
source = doc.metadata.get("source", "Unknown")
language = doc.metadata.get("language", "Unknown")
context += f"--- Segment {i} from {source} ({language}) ---\n"
context += f"```\n{doc.page_content}\n```\n\n"
print(f"Found {len(docs)} relevant code segments for context.")
except Exception as e:
print(f"Warning: Failed to perform similarity search: {e}")
system_prompt = """You are a technical assistant specializing in software development and programming.
Provide clear, accurate responses with code examples when relevant.
Format code snippets with proper markdown code blocks and specify the language."""
if context:
system_prompt += f"\n\nUse this context from the uploaded code files to inform your answers:{context}"
# Add instruction to reference specific file parts
system_prompt += "\nWhen discussing code from the uploaded files, specifically reference the file name and segment number."
completion = client.chat.completions.create(
model=model_name,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": message}
],
temperature=0.7,
max_tokens=1024
)
response = completion.choices[0].message.content
# For proper chat history handling
if isinstance(history, list) and history and isinstance(history[0], dict):
# History is in message format
history.append({"role": "user", "content": message})
history.append({"role": "assistant", "content": response})
else:
# Fallback for other formats
history.append({"role": "user", "content": message})
history.append({"role": "assistant", "content": response})
return history
except Exception as e:
error_msg = f"Error generating response: {str(e)}"
# Handle different history formats
if isinstance(history, list):
if history and isinstance(history[0], dict):
history.append({"role": "user", "content": message})
history.append({"role": "assistant", "content": error_msg})
else:
history.append({"role": "user", "content": message})
history.append({"role": "assistant", "content": error_msg})
return history
# Functions to update PDF viewer
def update_pdf_viewer(pdf_state):
if not pdf_state["total_pages"]:
return 0, None, "No PDF uploaded yet"
try:
img_data = base64.b64decode(pdf_state["page_images"][0])
img = Image.open(io.BytesIO(img_data))
return pdf_state["total_pages"], img, f"**Total Pages:** {pdf_state['total_pages']}\n**Total Words:** {pdf_state['total_words']}"
except Exception as e:
print(f"Error decoding image: {e}")
return 0, None, "Error displaying PDF"
def update_image(page_num, pdf_state):
if not pdf_state["total_pages"] or page_num < 1 or page_num > pdf_state["total_pages"]:
return None
try:
img_data = base64.b64decode(pdf_state["page_images"][page_num - 1])
img = Image.open(io.BytesIO(img_data))
return img
except Exception as e:
print(f"Error decoding image: {e}")
return None
# GitHub API integration
def search_github_repos(query, sort="stars", order="desc", per_page=10):
"""Search for GitHub repositories"""
try:
github_token = os.getenv("GITHUB_TOKEN", "")
headers = {}
if github_token:
headers["Authorization"] = f"token {github_token}"
params = {
"q": query,
"sort": sort,
"order": order,
"per_page": per_page
}
response = requests.get(
"https://api.github.com/search/repositories",
headers=headers,
params=params
)
if response.status_code != 200:
print(f"GitHub API Error: {response.status_code} - {response.text}")
return []
data = response.json()
return data.get("items", [])
except Exception as e:
print(f"Error in GitHub search: {e}")
return []
# Stack Overflow API integration
def search_stackoverflow(query, sort="votes", site="stackoverflow", pagesize=10):
"""Search for questions on Stack Overflow"""
try:
params = {
"order": "desc",
"sort": sort,
"site": site,
"pagesize": pagesize,
"intitle": query
}
response = requests.get(
"https://api.stackexchange.com/2.3/search/advanced",
params=params
)
if response.status_code != 200:
print(f"Stack Exchange API Error: {response.status_code} - {response.text}")
return []
data = response.json()
# Process results to convert Unix timestamps to readable dates
for item in data.get("items", []):
if "creation_date" in item:
item["creation_date"] = datetime.fromtimestamp(item["creation_date"]).strftime("%Y-%m-%d")
return data.get("items", [])
except Exception as e:
print(f"Error in Stack Overflow search: {e}")
return []
def get_stackoverflow_answers(question_id, site="stackoverflow"):
"""Get answers for a specific question on Stack Overflow"""
try:
params = {
"order": "desc",
"sort": "votes",
"site": site,
"filter": "withbody" # Include the answer body in the response
}
response = requests.get(
f"https://api.stackexchange.com/2.3/questions/{question_id}/answers",
params=params
)
if response.status_code != 200:
print(f"Stack Exchange API Error: {response.status_code} - {response.text}")
return []
data = response.json()
# Process results
for item in data.get("items", []):
if "creation_date" in item:
item["creation_date"] = datetime.fromtimestamp(item["creation_date"]).strftime("%Y-%m-%d")
return data.get("items", [])
except Exception as e:
print(f"Error getting Stack Overflow answers: {e}")
return []
def explain_code(code):
"""Explain code using LLM"""
try:
system_prompt = "You are an expert programmer and code reviewer. Your task is to explain the provided code in a clear, concise manner. Include:"
system_prompt += "\n1. What the code does (high-level overview)"
system_prompt += "\n2. Key functions/components and their purposes"
system_prompt += "\n3. Potential issues or optimization opportunities"
system_prompt += "\n4. Any best practices that are followed or violated"
completion = client.chat.completions.create(
model="llama3-70b-8192", # Using more capable model for code explanation
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Explain this code:\n```\n{code}\n```"}
],
temperature=0.3,
max_tokens=1024
)
explanation = completion.choices[0].message.content
return f"**Code Explanation:**\n\n{explanation}"
except Exception as e:
return f"Error explaining code: {str(e)}"
def perform_repo_search(query, language, sort_by, min_stars):
"""Perform GitHub repository search with UI parameters"""
try:
if not query:
return "Please enter a search query"
# Build the search query with filters
search_query = query
if language and language != "any":
search_query += f" language:{language}"
if min_stars and min_stars != "0":
search_query += f" stars:>={min_stars}"
# Map sort_by to GitHub API parameters
sort_param = "stars"
if sort_by == "updated":
sort_param = "updated"
elif sort_by == "forks":
sort_param = "forks"
results = search_github_repos(search_query, sort=sort_param)
if not results:
return "No repositories found. Try different search terms."
# Format results as markdown
markdown = "## GitHub Repository Search Results\n\n"
for i, repo in enumerate(results, 1):
markdown += f"### {i}. [{repo['full_name']}]({repo['html_url']})\n\n"
if repo['description']:
markdown += f"{repo['description']}\n\n"
markdown += f"**Language:** {repo['language'] or 'Not specified'}\n"
markdown += f"**Stars:** {repo['stargazers_count']} | **Forks:** {repo['forks_count']} | **Watchers:** {repo['watchers_count']}\n"
markdown += f"**Created:** {repo['created_at'][:10]} | **Updated:** {repo['updated_at'][:10]}\n\n"
if repo.get('topics'):
markdown += f"**Topics:** {', '.join(repo['topics'])}\n\n"
if repo.get('license') and repo['license'].get('name'):
markdown += f"**License:** {repo['license']['name']}\n\n"
markdown += f"[View Repository]({repo['html_url']}) | [Clone URL]({repo['clone_url']})\n\n"
markdown += "---\n\n"
return markdown
except Exception as e:
return f"Error searching for repositories: {str(e)}"
def perform_stack_search(query, tag, sort_by):
"""Perform Stack Overflow search with UI parameters"""
try:
if not query:
return "Please enter a search query"
# Add tag to query if specified
if tag and tag != "any":
query_with_tag = f"{query} [tag:{tag}]"
else:
query_with_tag = query
# Map sort_by to Stack Exchange API parameters
sort_param = "votes"
if sort_by == "newest":
sort_param = "creation"
elif sort_by == "activity":
sort_param = "activity"
results = search_stackoverflow(query_with_tag, sort=sort_param)
if not results:
return "No questions found. Try different search terms."
# Format results as markdown
markdown = "## Stack Overflow Search Results\n\n"
for i, question in enumerate(results, 1):
markdown += f"### {i}. [{question['title']}]({question['link']})\n\n"
# Score and answer stats
markdown += f"**Score:** {question['score']} | **Answers:** {question['answer_count']}"
if question.get('is_answered'):
markdown += " ✓ (Accepted answer available)"
markdown += "\n\n"
# Tags
if question.get('tags'):
markdown += "**Tags:** "
for tag in question['tags']:
markdown += f"`{tag}` "
markdown += "\n\n"
# Asked info
markdown += f"**Asked:** {question['creation_date']} | **Views:** {question.get('view_count', 'N/A')}\n\n"
markdown += f"[View Question]({question['link']})\n\n"
markdown += "---\n\n"
return markdown
except Exception as e:
return f"Error searching Stack Overflow: {str(e)}"
# Modify the process_code_file function
def process_code_file(file_obj):
"""Process uploaded code files and store in FAISS index"""
if file_obj is None:
return None, "No file uploaded", {}
try:
# Handle both file objects and bytes objects
if isinstance(file_obj, bytes):
content = file_obj.decode('utf-8', errors='replace') # Added error handling
file_name = "uploaded_file"
file_extension = ".txt" # Default extension
else:
content = file_obj.read().decode('utf-8', errors='replace') # Added error handling
file_name = getattr(file_obj, 'name', 'uploaded_file')
file_extension = Path(file_name).suffix.lower()
language = detect_language(file_extension)
# Calculate metrics
metrics = calculate_complexity_metrics(content, language)
# Create vectorstore if embeddings are available
session_id = None
if embeddings:
try:
print(f"Creating FAISS index for {file_name}...")
# Improved chunking for code files
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=500, # Smaller chunks for code
chunk_overlap=50,
separators=["\n\n", "\n", " ", ""]
)
chunks = text_splitter.create_documents([content], metadatas=[{"filename": file_name, "language": language}])
# Add source metadata to help with retrieval
for i, chunk in enumerate(chunks):
chunk.metadata["chunk_id"] = i
chunk.metadata["source"] = file_name
# Create and store vectorstore
vectorstore = FAISS.from_documents(chunks, embeddings)
session_id = str(uuid.uuid4())
index_path = os.path.join(FAISS_INDEX_DIR, session_id)
vectorstore.save_local(index_path)
user_vectorstores[session_id] = vectorstore
# Add number of chunks to metrics for display
metrics["chunks"] = len(chunks)
print(f"Successfully created FAISS index with {len(chunks)} chunks.")
except Exception as e:
print(f"Warning: Failed to create vectorstore: {e}")
return session_id, f"✅ Successfully analyzed {file_name} and stored in FAISS index", metrics
except Exception as e:
return None, f"Error processing file: {str(e)}", {}
# Update the Gradio interface
with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
current_session_id = gr.State(None)
code_state = gr.State({})
gr.HTML("""
""")
with gr.Row(elem_classes="container"):
with gr.Column(scale=1, min_width=300):
file_input = gr.File(
label="Upload Code File",
file_types=[".py", ".js", ".java", ".cpp", ".c", ".cs", ".php", ".rb", ".go", ".ts"],
type="binary"
)
upload_button = gr.Button("Analyze Code", variant="primary")
file_status = gr.Markdown("No file uploaded yet")
model_dropdown = gr.Dropdown(
choices=["llama3-70b-8192", "mixtral-8x7b-32768", "gemma-7b-it"],
value="llama3-70b-8192",
label="Select Model"
)
# Developer Tools Section
gr.Markdown("### Developer Tools", elem_classes="tool-title")
with gr.Group(elem_classes="tool-container"): # Replace Box with Group
with gr.Tabs():
with gr.TabItem("GitHub Search"):
repo_query = gr.Textbox(label="Search Query", placeholder="Enter keywords to search for repositories")
with gr.Row():
language = gr.Dropdown(
choices=["any", "JavaScript", "Python", "Java", "C++", "TypeScript", "Go", "Rust", "PHP", "C#"],
value="any",
label="Language"
)
min_stars = gr.Dropdown(
choices=["0", "10", "50", "100", "1000", "10000"],
value="0",
label="Min Stars"
)
sort_by = gr.Dropdown(
choices=["stars", "forks", "updated"],
value="stars",
label="Sort By"
)
repo_search_btn = gr.Button("Search Repositories")
with gr.TabItem("Stack Overflow"):
stack_query = gr.Textbox(label="Search Query", placeholder="Enter your technical question")
with gr.Row():
tag = gr.Dropdown(
choices=["any", "python", "javascript", "java", "c++", "react", "node.js", "android", "ios", "sql"],
value="any",
label="Tag"
)
so_sort_by = gr.Dropdown(
choices=["votes", "newest", "activity"],
value="votes",
label="Sort By"
)
so_search_btn = gr.Button("Search Stack Overflow")
with gr.TabItem("Code Explainer"):
code_input = gr.Textbox(
label="Code to Explain",
placeholder="Paste your code here...",
lines=10
)
explain_btn = gr.Button("Explain Code")
with gr.Column(scale=2, min_width=600):
with gr.Tabs():
with gr.TabItem("Code Analysis"):
with gr.Column(elem_classes="code-viewer-container"):
code_metrics = gr.Markdown("No code analyzed yet", elem_classes="stats-box")
code_recommendations = gr.Markdown("", elem_classes="recommendations-box")
with gr.TabItem("GitHub Results"):
repo_results = gr.Markdown("Search for repositories to see results here")
with gr.TabItem("Stack Overflow Results"):
stack_results = gr.Markdown("Search for questions to see results here")
with gr.TabItem("Code Explanation"):
code_explanation = gr.Markdown("Paste your code and click 'Explain Code' to see an explanation here")
with gr.Row(elem_classes="container"):
with gr.Column(scale=2, min_width=600):
chatbot = gr.Chatbot(
height=500,
show_copy_button=True,
elem_classes="chat-container",
type="messages"
)
with gr.Row():
msg = gr.Textbox(
show_label=False,
placeholder="Ask about your code, type /github to search repos, or /stack to search Stack Overflow...",
scale=5
)
send_btn = gr.Button("Send", scale=1)
clear_btn = gr.Button("Clear Conversation")
# Update event handlers
upload_button.click(
lambda x: process_code_file(x),
inputs=[file_input],
outputs=[current_session_id, file_status, code_state]
).then(
lambda state: (
f"### Code Analysis Results\n\n"
f"**Language:** {state.get('language', 'Unknown')}\n"
f"**Total Lines:** {state.get('total_lines', 0)}\n"
f"**Code Lines:** {state.get('code_lines', 0)}\n"
f"**Comment Lines:** {state.get('comments', 0)}\n"
f"**Functions:** {state.get('functions', 0)}\n"
f"**Classes:** {state.get('classes', 0)}\n"
f"**Complexity Score:** {state.get('cyclomatic_complexity', 0)}\n"
),
inputs=[code_state],
outputs=[code_metrics]
).then(
lambda state: generate_recommendations(state),
inputs=[code_state],
outputs=[code_recommendations]
)
msg.submit(
generate_response,
inputs=[msg, current_session_id, model_dropdown, chatbot],
outputs=[chatbot]
).then(lambda: "", None, [msg])
send_btn.click(
generate_response,
inputs=[msg, current_session_id, model_dropdown, chatbot],
outputs=[chatbot]
).then(lambda: "", None, [msg])
clear_btn.click(
lambda: ([], None, "No file uploaded", {}, None),
None,
[chatbot, current_session_id, file_status, code_state, code_metrics]
)
# Tech tool handlers
repo_search_btn.click(
perform_repo_search,
inputs=[repo_query, language, sort_by, min_stars],
outputs=[repo_results]
)
so_search_btn.click(
perform_stack_search,
inputs=[stack_query, tag, so_sort_by],
outputs=[stack_results]
)
explain_btn.click(
explain_code,
inputs=[code_input],
outputs=[code_explanation]
)
# Add footer with attribution
gr.HTML("""
Created by Calvin Allen Crawford
""")
# Launch the app
if __name__ == "__main__":
demo.launch()