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{
"cells": [
{
"cell_type": "markdown",
"id": "3baa95af-73a1-4d3c-a562-f90777f1f0c0",
"metadata": {},
"source": [
"# Text Data Analysis AI Assistant with Gradio\n",
" - Intelligent Customer Feedback Analysis System with Multiple AI APIs"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "31a6bbea-df57-40ed-afd3-4df75cc86d0a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package brown to /Users/fola-ai/nltk_data...\n",
"[nltk_data] Package brown is already up-to-date!\n",
"[nltk_data] Downloading package punkt_tab to /Users/fola-\n",
"[nltk_data] ai/nltk_data...\n",
"[nltk_data] Package punkt_tab is already up-to-date!\n",
"[nltk_data] Downloading package wordnet to /Users/fola-ai/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n",
"[nltk_data] Downloading package averaged_perceptron_tagger_eng to\n",
"[nltk_data] /Users/fola-ai/nltk_data...\n",
"[nltk_data] Unzipping taggers/averaged_perceptron_tagger_eng.zip.\n",
"[nltk_data] Downloading package conll2000 to /Users/fola-\n",
"[nltk_data] ai/nltk_data...\n",
"[nltk_data] Unzipping corpora/conll2000.zip.\n",
"[nltk_data] Downloading package movie_reviews to /Users/fola-\n",
"[nltk_data] ai/nltk_data...\n",
"[nltk_data] Unzipping corpora/movie_reviews.zip.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished.\n"
]
}
],
"source": [
"# ===== IMPORTS SECTION =====\n",
"# Core libraries\n",
"import os\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Environment and API\n",
"from dotenv import load_dotenv\n",
"from anthropic import Anthropic\n",
"\n",
"# Additional AI APIs\n",
"try:\n",
" from openai import OpenAI\n",
"except ImportError:\n",
" OpenAI = None\n",
" \n",
"try:\n",
" from groq import Groq\n",
"except ImportError:\n",
" Groq = None\n",
" \n",
"try:\n",
" import google.generativeai as genai\n",
"except ImportError:\n",
" genai = None\n",
"\n",
"# Data processing\n",
"import pandas as pd\n",
"import numpy as np\n",
"from datetime import datetime, timedelta\n",
"import json\n",
"import gc # For garbage collection\n",
"\n",
"# Natural Language Processing\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.stem import WordNetLemmatizer\n",
"from textblob import TextBlob\n",
"import re\n",
"from collections import Counter\n",
"\n",
"# Machine Learning\n",
"from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
"from sklearn.decomposition import LatentDirichletAllocation\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"\n",
"# Visualization\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Web interface\n",
"import gradio as gr\n",
"\n",
"# Download required NLTK data\n",
"nltk.download('punkt', quiet=True)\n",
"nltk.download('punkt_tab', quiet=True) # New tokenizer format\n",
"nltk.download('stopwords', quiet=True)\n",
"nltk.download('wordnet', quiet=True)\n",
"nltk.download('averaged_perceptron_tagger', quiet=True)\n",
"nltk.download('omw-1.4', quiet=True) # For WordNet lemmatizer\n",
"nltk.download('brown', quiet=True) # Required for TextBlob\n",
"\n",
"# Download TextBlob corpora\n",
"try:\n",
" from textblob import download_corpora\n",
" download_corpora.main()\n",
"except:\n",
" # Alternative method if the above doesn't work\n",
" import subprocess\n",
" import sys\n",
" try:\n",
" subprocess.run([sys.executable, \"-m\", \"textblob.download_corpora\"], \n",
" capture_output=True, text=True, timeout=30)\n",
" except:\n",
" print(\"Warning: Could not download TextBlob corpora. Sentiment analysis may not work properly.\")\n",
" print(\"Please run: python -m textblob.download_corpora\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "db7c1e72-7960-4968-9a72-0f62ca7140d9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bded62da-82ab-4e17-bbf5-3edfe1b39398",
"metadata": {},
"outputs": [],
"source": [
"# ===== SMART COLUMN DETECTOR =====\n",
"class SmartColumnDetector:\n",
" \"\"\"Intelligently detect and extract relevant columns from uploaded data\"\"\"\n",
" \n",
" def __init__(self):\n",
" # Keywords for detecting different column types\n",
" self.text_keywords = ['comment', 'feedback', 'review', 'description', 'text', \n",
" 'response', 'opinion', 'message', 'notes', 'remarks']\n",
" self.id_keywords = ['id', 'identifier', 'key', 'number', 'code', 'ref', \n",
" 'reference', 'index', 'uuid']\n",
" self.product_keywords = ['product', 'item', 'model', 'variant', 'type', \n",
" 'category', 'brand', 'name', 'sku']\n",
" self.date_keywords = ['date', 'time', 'created', 'updated', 'timestamp']\n",
" \n",
" def detect_column_types(self, df):\n",
" \"\"\"Detect column types based on column names and content\"\"\"\n",
" detected = {\n",
" 'text_columns': [],\n",
" 'id_columns': [],\n",
" 'product_columns': [],\n",
" 'date_columns': [],\n",
" 'other_columns': []\n",
" }\n",
" \n",
" for col in df.columns:\n",
" col_lower = col.lower()\n",
" \n",
" # Check for text columns\n",
" if any(keyword in col_lower for keyword in self.text_keywords):\n",
" detected['text_columns'].append(col)\n",
" # Check for ID columns\n",
" elif any(keyword in col_lower for keyword in self.id_keywords):\n",
" detected['id_columns'].append(col)\n",
" # Check for product columns\n",
" elif any(keyword in col_lower for keyword in self.product_keywords):\n",
" detected['product_columns'].append(col)\n",
" # Check for date columns\n",
" elif any(keyword in col_lower for keyword in self.date_keywords):\n",
" detected['date_columns'].append(col)\n",
" else:\n",
" # Analyze content to determine type\n",
" sample = df[col].dropna().head(100)\n",
" if len(sample) > 0:\n",
" # Check if mostly text\n",
" if df[col].dtype == 'object':\n",
" avg_length = sample.astype(str).str.len().mean()\n",
" if avg_length > 50: # Likely text content\n",
" detected['text_columns'].append(col)\n",
" elif avg_length < 20 and df[col].nunique() / len(df) > 0.5:\n",
" detected['id_columns'].append(col)\n",
" else:\n",
" detected['product_columns'].append(col)\n",
" else:\n",
" detected['other_columns'].append(col)\n",
" \n",
" return detected\n",
" \n",
" def extract_relevant_data(self, df):\n",
" \"\"\"Extract only relevant columns and create optimized dataset\"\"\"\n",
" detected = self.detect_column_types(df)\n",
" \n",
" # Create new dataframe with relevant columns\n",
" extracted_data = pd.DataFrame()\n",
" \n",
" # Add unique identifier\n",
" if detected['id_columns'] and len(detected['id_columns']) > 0:\n",
" extracted_data['unique_id'] = df[detected['id_columns'][0]]\n",
" else:\n",
" extracted_data['unique_id'] = range(1, len(df) + 1)\n",
" \n",
" # Add product information\n",
" if detected['product_columns'] and len(detected['product_columns']) > 0:\n",
" # Convert to list if needed and limit to 2 product columns\n",
" product_cols = list(detected['product_columns'])[:2]\n",
" for col in product_cols:\n",
" extracted_data[f'product_{col}'] = df[col]\n",
" \n",
" # Combine text columns\n",
" if detected['text_columns'] and len(detected['text_columns']) > 0:\n",
" text_cols = list(detected['text_columns']) # Ensure it's a list\n",
" text_data = []\n",
" for idx in df.index:\n",
" combined_text = ' '.join([\n",
" str(df.loc[idx, col]) \n",
" for col in text_cols \n",
" if col in df.columns and pd.notna(df.loc[idx, col])\n",
" ])\n",
" text_data.append(combined_text)\n",
" extracted_data['combined_text'] = text_data\n",
" else:\n",
" # If no text columns detected, create empty combined_text\n",
" extracted_data['combined_text'] = [''] * len(df)\n",
" \n",
" # Add date columns\n",
" if detected['date_columns'] and len(detected['date_columns']) > 0:\n",
" extracted_data['date'] = pd.to_datetime(df[detected['date_columns'][0]], errors='coerce')\n",
" \n",
" return extracted_data, detected"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "626af7bf-b4cf-4259-b409-18e5225555aa",
"metadata": {},
"outputs": [],
"source": [
"# ===== ENHANCED TEXT PROCESSOR =====\n",
"class EnhancedTextProcessor:\n",
" \"\"\"Enhanced text preprocessing with actionable insights extraction\"\"\"\n",
"\n",
" def __init__(self):\n",
" self.lemmatizer = WordNetLemmatizer()\n",
" self.stop_words = set(stopwords.words('english'))\n",
" \n",
" # Initialize actionable insights dictionary with common customer feedback phrases\n",
" self.actionable_dictionary = {\n",
" 'improve speed': ['slow', 'faster', 'quick', 'speed up', 'takes too long', 'waiting'],\n",
" 'better quality': ['poor quality', 'cheap', 'breaks', 'defective', 'flimsy', 'weak'],\n",
" 'enhance ui': ['confusing', 'hard to use', 'complicated', 'not intuitive', 'difficult to navigate'],\n",
" 'fix bugs': ['bug', 'error', 'crash', 'freeze', 'not working', 'glitch', 'broken'],\n",
" 'add features': ['missing', 'need', 'want', 'should have', 'would be nice', 'lacks'],\n",
" 'improve support': ['no response', 'unhelpful', 'rude', 'poor service', 'bad support'],\n",
" 'better packaging': ['damaged', 'poor packaging', 'arrived broken', 'not protected'],\n",
" 'clearer docs': ['unclear', 'no instructions', 'confusing manual', 'hard to understand'],\n",
" 'reduce price': ['expensive', 'overpriced', 'too costly', 'not worth', 'cheaper'],\n",
" 'faster delivery': ['late', 'delayed', 'slow shipping', 'took forever', 'still waiting'],\n",
" 'better communication': ['no updates', 'not informed', 'lack of communication', 'no tracking'],\n",
" 'improve reliability': ['unreliable', 'stops working', 'inconsistent', 'sometimes works'],\n",
" 'enhance performance': ['slow performance', 'laggy', 'sluggish', 'not responsive'],\n",
" 'better design': ['ugly', 'poor design', 'looks cheap', 'not attractive', 'outdated look'],\n",
" 'more options': ['limited options', 'no variety', 'need more choices', 'only one option']\n",
" }\n",
"\n",
" def clean_text(self, text):\n",
" \"\"\"Clean and normalize text\"\"\"\n",
" if pd.isna(text) or text == '':\n",
" return \"\"\n",
"\n",
" text = str(text).lower()\n",
" text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
" text = ' '.join(text.split())\n",
" return text\n",
"\n",
" def extract_actionable_insights(self, text):\n",
" \"\"\"Extract actionable insights using dictionary matching\"\"\"\n",
" if pd.isna(text) or text == '':\n",
" return \"\"\n",
" \n",
" text_lower = text.lower()\n",
" found_insights = []\n",
" \n",
" # Check each actionable item against the text\n",
" for action, keywords in self.actionable_dictionary.items():\n",
" for keyword in keywords:\n",
" if keyword in text_lower:\n",
" found_insights.append(action)\n",
" break # Only add each action once\n",
" \n",
" # Return top 3 most relevant insights\n",
" if found_insights:\n",
" return ', '.join(found_insights[:3])\n",
" return \"\"\n",
"\n",
" def extract_specific_topics(self, text):\n",
" \"\"\"Extract specific topics from text using keyword extraction\"\"\"\n",
" if pd.isna(text) or text == '' or len(text) < 10:\n",
" return ['', '', '']\n",
" \n",
" # Clean text first\n",
" text_lower = text.lower()\n",
" \n",
" # Remove stopwords for better topic extraction\n",
" words = word_tokenize(text_lower)\n",
" filtered_words = [w for w in words if w not in self.stop_words and len(w) > 3]\n",
" \n",
" # Extract noun phrases and important terms\n",
" blob = TextBlob(text)\n",
" noun_phrases = blob.noun_phrases\n",
" \n",
" # Combine noun phrases with high-frequency meaningful words\n",
" topics = []\n",
" \n",
" # Add noun phrases (these are usually good topics)\n",
" for phrase in noun_phrases[:5]: # Limit to top 5 noun phrases\n",
" if len(phrase.split()) <= 3: # Only short phrases\n",
" topics.append(phrase)\n",
" \n",
" # Add frequent meaningful words if we don't have enough topics\n",
" if len(topics) < 3:\n",
" word_freq = Counter(filtered_words)\n",
" for word, _ in word_freq.most_common(5):\n",
" if word not in str(topics): # Avoid duplicates\n",
" topics.append(word)\n",
" if len(topics) >= 3:\n",
" break\n",
" \n",
" # Ensure we always return 3 items (empty string if not enough topics)\n",
" topics = topics[:3]\n",
" while len(topics) < 3:\n",
" topics.append('')\n",
" \n",
" return topics\n",
"\n",
" def determine_topic(self, text):\n",
" \"\"\"Legacy method kept for compatibility - returns first specific topic\"\"\"\n",
" topics = self.extract_specific_topics(text)\n",
" return topics[0] if topics[0] else 'General'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b2eb5f17-7400-4591-8c0e-de7645b87c72",
"metadata": {},
"outputs": [],
"source": [
"# ===== SEARCH ENGINE =====\n",
"class TextSearchEngine:\n",
" \"\"\"Advanced search functionality for text data with semantic capabilities\"\"\"\n",
" \n",
" def __init__(self):\n",
" self.vectorizer = TfidfVectorizer(\n",
" max_features=1000,\n",
" ngram_range=(1, 3), # Include unigrams, bigrams, and trigrams for better matching\n",
" stop_words='english',\n",
" use_idf=True,\n",
" smooth_idf=True,\n",
" sublinear_tf=True # Apply sublinear tf scaling\n",
" )\n",
" self.tfidf_matrix = None\n",
" self.data = None\n",
" \n",
" # Synonym dictionary for semantic search\n",
" self.synonyms = {\n",
" 'fast': ['quick', 'rapid', 'speedy', 'swift', 'prompt'],\n",
" 'slow': ['sluggish', 'delayed', 'laggy', 'lengthy', 'prolonged'],\n",
" 'good': ['excellent', 'great', 'wonderful', 'fantastic', 'amazing', 'positive'],\n",
" 'bad': ['poor', 'terrible', 'awful', 'negative', 'horrible', 'disappointing'],\n",
" 'problem': ['issue', 'bug', 'error', 'defect', 'fault', 'glitch'],\n",
" 'help': ['support', 'assistance', 'aid', 'service'],\n",
" 'price': ['cost', 'fee', 'charge', 'rate', 'payment', 'expensive', 'cheap'],\n",
" 'quality': ['standard', 'grade', 'condition', 'caliber'],\n",
" 'delivery': ['shipping', 'dispatch', 'arrival', 'transport'],\n",
" 'easy': ['simple', 'straightforward', 'effortless', 'user-friendly'],\n",
" 'hard': ['difficult', 'complex', 'complicated', 'challenging'],\n",
" 'broken': ['damaged', 'defective', 'faulty', 'malfunctioning'],\n",
" 'love': ['like', 'enjoy', 'appreciate', 'adore'],\n",
" 'hate': ['dislike', 'despise', 'detest'],\n",
" 'feature': ['function', 'capability', 'option', 'characteristic'],\n",
" 'customer': ['client', 'buyer', 'purchaser', 'consumer', 'user']\n",
" }\n",
" \n",
" def expand_query_with_synonyms(self, query):\n",
" \"\"\"Expand search query with synonyms for better semantic matching\"\"\"\n",
" query_words = query.lower().split()\n",
" expanded_terms = []\n",
" \n",
" for word in query_words:\n",
" # Add the original word\n",
" expanded_terms.append(word)\n",
" \n",
" # Add synonyms if available\n",
" if word in self.synonyms:\n",
" expanded_terms.extend(self.synonyms[word])\n",
" \n",
" # Check if word is a synonym of something else\n",
" for key, syns in self.synonyms.items():\n",
" if word in syns:\n",
" expanded_terms.append(key)\n",
" expanded_terms.extend([s for s in syns if s != word])\n",
" \n",
" # Remove duplicates while preserving order\n",
" seen = set()\n",
" unique_terms = []\n",
" for term in expanded_terms:\n",
" if term not in seen:\n",
" unique_terms.append(term)\n",
" seen.add(term)\n",
" \n",
" return ' '.join(unique_terms)\n",
" \n",
" def build_index(self, df, text_column):\n",
" \"\"\"Build search index from text data\"\"\"\n",
" self.data = df.copy()\n",
" texts = df[text_column].fillna('').tolist()\n",
" \n",
" # Add other searchable columns to improve search\n",
" if 'topic_1' in df.columns:\n",
" texts = [f\"{text} {df.iloc[i]['topic_1']} {df.iloc[i]['topic_2']} {df.iloc[i]['topic_3']}\" \n",
" for i, text in enumerate(texts)]\n",
" if 'actionable_insights' in df.columns:\n",
" texts = [f\"{texts[i]} {df.iloc[i]['actionable_insights']}\" \n",
" for i in range(len(texts))]\n",
" \n",
" self.tfidf_matrix = self.vectorizer.fit_transform(texts)\n",
" \n",
" def search(self, query, top_k=10):\n",
" \"\"\"Enhanced search with semantic understanding\"\"\"\n",
" if self.tfidf_matrix is None:\n",
" return pd.DataFrame()\n",
" \n",
" # Expand query with synonyms\n",
" expanded_query = self.expand_query_with_synonyms(query)\n",
" \n",
" # Vectorize both original and expanded queries\n",
" query_vector = self.vectorizer.transform([query])\n",
" expanded_vector = self.vectorizer.transform([expanded_query])\n",
" \n",
" # Calculate similarities for both\n",
" similarities_orig = cosine_similarity(query_vector, self.tfidf_matrix).flatten()\n",
" similarities_exp = cosine_similarity(expanded_vector, self.tfidf_matrix).flatten()\n",
" \n",
" # Combine scores (weighted average - original query gets more weight)\n",
" combined_similarities = (0.7 * similarities_orig + 0.3 * similarities_exp)\n",
" \n",
" # Get top results\n",
" top_indices = combined_similarities.argsort()[-top_k:][::-1]\n",
" top_scores = combined_similarities[top_indices]\n",
" \n",
" # Filter results with score > 0.05 (lower threshold for better recall)\n",
" valid_indices = [idx for idx, score in zip(top_indices, top_scores) if score > 0.05]\n",
" \n",
" if valid_indices:\n",
" results = self.data.iloc[valid_indices].copy()\n",
" results['search_score'] = [combined_similarities[idx] for idx in valid_indices]\n",
" \n",
" # Boost results that have exact matches\n",
" query_lower = query.lower()\n",
" for idx in results.index:\n",
" if 'combined_text' in results.columns:\n",
" if query_lower in str(results.at[idx, 'combined_text']).lower():\n",
" results.at[idx, 'search_score'] *= 1.5 # Boost exact matches\n",
" \n",
" return results.sort_values('search_score', ascending=False)\n",
" \n",
" return pd.DataFrame()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e8b88155-971f-4dd5-b26c-104a737bc426",
"metadata": {},
"outputs": [],
"source": [
"# ===== API CONFIGURATION =====\n",
"class AIModelManager:\n",
" \"\"\"Manages multiple AI model APIs and provides unified interface\"\"\"\n",
" \n",
" def __init__(self):\n",
" self.available_models = {}\n",
" self.clients = {}\n",
" self.current_model = None\n",
" self.initialize_apis()\n",
" \n",
" def initialize_apis(self):\n",
" \"\"\"Initialize all available AI APIs\"\"\"\n",
" \n",
" # Anthropic\n",
" ANTHROPIC_API_KEY = os.getenv(\"ANTHROPIC_API_KEY\")\n",
" if ANTHROPIC_API_KEY:\n",
" try:\n",
" self.clients['anthropic'] = Anthropic(api_key=ANTHROPIC_API_KEY)\n",
" self.available_models['Claude 3 Haiku'] = {\n",
" 'provider': 'anthropic',\n",
" 'model': 'claude-3-haiku-20240307'\n",
" }\n",
" print(f\"Anthropic API Key exists and begins {ANTHROPIC_API_KEY[:4]}\")\n",
" except Exception as e:\n",
" print(f\"Error initializing Anthropic: {e}\")\n",
" else:\n",
" print(\"Anthropic API Key not set\")\n",
" \n",
" # OpenAI\n",
" OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
" if OPENAI_API_KEY and OpenAI:\n",
" try:\n",
" self.clients['openai'] = OpenAI(api_key=OPENAI_API_KEY)\n",
" self.available_models['GPT-4o-mini'] = {\n",
" 'provider': 'openai',\n",
" 'model': 'gpt-4o-mini'\n",
" }\n",
" self.available_models['GPT-3.5 Turbo'] = {\n",
" 'provider': 'openai',\n",
" 'model': 'gpt-3.5-turbo'\n",
" }\n",
" print(f\"OpenAI API Key exists and begins {OPENAI_API_KEY[:7]}\")\n",
" except Exception as e:\n",
" print(f\"Error initializing OpenAI: {e}\")\n",
" else:\n",
" print(\"OpenAI API Key not set or library not installed\")\n",
" \n",
" # Deepseek (uses OpenAI-compatible API)\n",
" DEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\n",
" if DEEPSEEK_API_KEY and OpenAI:\n",
" try:\n",
" self.clients['deepseek'] = OpenAI(\n",
" api_key=DEEPSEEK_API_KEY,\n",
" base_url=\"https://api.deepseek.com\"\n",
" )\n",
" self.available_models['Deepseek Chat'] = {\n",
" 'provider': 'deepseek',\n",
" 'model': 'deepseek-chat'\n",
" }\n",
" print(f\"Deepseek API Key exists and begins {DEEPSEEK_API_KEY[:7]}\")\n",
" except Exception as e:\n",
" print(f\"Error initializing Deepseek: {e}\")\n",
" else:\n",
" print(\"Deepseek API Key not set or OpenAI library not installed\")\n",
" \n",
" # Groq\n",
" GROQ_API_KEY = os.getenv(\"GROQ_API_KEY\")\n",
" if GROQ_API_KEY and Groq:\n",
" try:\n",
" self.clients['groq'] = Groq(api_key=GROQ_API_KEY)\n",
" self.available_models['Llama 3.3 70B'] = {\n",
" 'provider': 'groq',\n",
" 'model': 'llama-3.3-70b-versatile'\n",
" }\n",
" self.available_models['Mixtral 8x7B'] = {\n",
" 'provider': 'groq',\n",
" 'model': 'mixtral-8x7b-32768'\n",
" }\n",
" print(f\"Groq API Key exists and begins {GROQ_API_KEY[:4]}\")\n",
" except Exception as e:\n",
" print(f\"Error initializing Groq: {e}\")\n",
" else:\n",
" print(\"Groq API Key not set or library not installed\")\n",
" \n",
" # Google Gemini\n",
" GOOGLE_API_KEY = os.getenv(\"GOOGLE_API_KEY\")\n",
" if GOOGLE_API_KEY and genai:\n",
" try:\n",
" genai.configure(api_key=GOOGLE_API_KEY)\n",
" self.clients['google'] = genai\n",
" self.available_models['Gemini 1.5 Flash'] = {\n",
" 'provider': 'google',\n",
" 'model': 'gemini-1.5-flash'\n",
" }\n",
" self.available_models['Gemini 1.5 Pro'] = {\n",
" 'provider': 'google',\n",
" 'model': 'gemini-1.5-pro'\n",
" }\n",
" print(f\"Google API Key exists and begins {GOOGLE_API_KEY[:2]}\")\n",
" except Exception as e:\n",
" print(f\"Error initializing Google Gemini: {e}\")\n",
" else:\n",
" print(\"Google API Key not set or library not installed\")\n",
" \n",
" # Set default model\n",
" if self.available_models:\n",
" self.current_model = list(self.available_models.keys())[0]\n",
" \n",
" def get_available_models(self):\n",
" \"\"\"Return list of available model names\"\"\"\n",
" return list(self.available_models.keys())\n",
" \n",
" def set_model(self, model_name):\n",
" \"\"\"Set the current model\"\"\"\n",
" if model_name in self.available_models:\n",
" self.current_model = model_name\n",
" return True\n",
" return False\n",
" \n",
" def generate_text(self, prompt, max_tokens=1000):\n",
" \"\"\"Generate text using the current model\"\"\"\n",
" if not self.current_model or self.current_model not in self.available_models:\n",
" return None\n",
" \n",
" model_info = self.available_models[self.current_model]\n",
" provider = model_info['provider']\n",
" model = model_info['model']\n",
" \n",
" try:\n",
" if provider == 'anthropic':\n",
" client = self.clients['anthropic']\n",
" response = client.messages.create(\n",
" model=model,\n",
" max_tokens=max_tokens,\n",
" messages=[{\"role\": \"user\", \"content\": prompt}]\n",
" )\n",
" return response.content[0].text\n",
" \n",
" elif provider in ['openai', 'deepseek']:\n",
" client = self.clients[provider]\n",
" response = client.chat.completions.create(\n",
" model=model,\n",
" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
" max_tokens=max_tokens\n",
" )\n",
" return response.choices[0].message.content\n",
" \n",
" elif provider == 'groq':\n",
" client = self.clients['groq']\n",
" response = client.chat.completions.create(\n",
" model=model,\n",
" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
" max_tokens=max_tokens\n",
" )\n",
" return response.choices[0].message.content\n",
" \n",
" elif provider == 'google':\n",
" model_obj = genai.GenerativeModel(model)\n",
" response = model_obj.generate_content(prompt)\n",
" return response.text\n",
" \n",
" except Exception as e:\n",
" print(f\"Error generating text with {self.current_model}: {e}\")\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "809f4c47-6ea8-4eaa-bac1-5ca83daac733",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Anthropic API Key exists and begins sk-a\n",
"OpenAI API Key exists and begins sk-proj\n",
"Deepseek API Key exists and begins sk-1099\n",
"Groq API Key exists and begins gsk_\n",
"Google API Key exists and begins AI\n"
]
}
],
"source": [
"# Initialize the model manager globally\n",
"model_manager = AIModelManager()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ad5f99f2-efd9-4759-88dc-df7f2f5359fb",
"metadata": {},
"outputs": [],
"source": [
"# ===== ENHANCED ANALYZER WITH MULTI-MODEL SUPPORT =====\n",
"\n",
"class EnhancedTextAnalyzer:\n",
" \"\"\"Main analysis engine with all enhanced features and multi-model support\"\"\"\n",
" \n",
" def __init__(self, model_manager=None):\n",
" self.model_manager = model_manager\n",
" self.column_detector = SmartColumnDetector()\n",
" self.text_processor = EnhancedTextProcessor()\n",
" self.search_engine = TextSearchEngine()\n",
" self.original_df = None\n",
" self.processed_df = None\n",
" self.results = {}\n",
" self.visualizations = {}\n",
" \n",
" def load_file(self, file):\n",
" \"\"\"Load data from various file formats\"\"\"\n",
" try:\n",
" if file.name.endswith('.csv'):\n",
" df = pd.read_csv(file.name)\n",
" elif file.name.endswith(('.xlsx', '.xls')):\n",
" df = pd.read_excel(file.name)\n",
" elif file.name.endswith('.json'):\n",
" df = pd.read_json(file.name)\n",
" else:\n",
" return None, \"Unsupported file format\"\n",
" \n",
" return df, f\"File loaded: {len(df)} records\"\n",
" except Exception as e:\n",
" return None, f\"Error loading file: {str(e)}\"\n",
" \n",
" def process_data(self, df):\n",
" \"\"\"Process data with smart extraction and analysis\"\"\"\n",
" # Extract relevant columns\n",
" extracted_df, detected_columns = self.column_detector.extract_relevant_data(df)\n",
" \n",
" # Store for reference\n",
" self.processed_df = extracted_df\n",
" \n",
" # Clear original from memory\n",
" del df\n",
" gc.collect()\n",
" \n",
" # Add analysis columns\n",
" if 'combined_text' in extracted_df.columns:\n",
" # Sentiment analysis\n",
" sentiments = []\n",
" polarities = []\n",
" topics_1 = []\n",
" topics_2 = []\n",
" topics_3 = []\n",
" insights = []\n",
" \n",
" for text in extracted_df['combined_text']:\n",
" # Sentiment\n",
" blob = TextBlob(text)\n",
" polarity = blob.sentiment.polarity\n",
" if polarity > 0.1:\n",
" sentiment = 'Positive'\n",
" elif polarity < -0.1:\n",
" sentiment = 'Negative'\n",
" else:\n",
" sentiment = 'Neutral'\n",
" \n",
" sentiments.append(sentiment)\n",
" polarities.append(polarity)\n",
" \n",
" # Extract specific topics (3 separate topics)\n",
" specific_topics = self.text_processor.extract_specific_topics(text)\n",
" topics_1.append(specific_topics[0])\n",
" topics_2.append(specific_topics[1])\n",
" topics_3.append(specific_topics[2])\n",
" \n",
" # Actionable insights using dictionary matching\n",
" insight = self.text_processor.extract_actionable_insights(text)\n",
" insights.append(insight)\n",
" \n",
" extracted_df['sentiment'] = sentiments\n",
" extracted_df['sentiment_score'] = polarities\n",
" extracted_df['topic_1'] = topics_1\n",
" extracted_df['topic_2'] = topics_2\n",
" extracted_df['topic_3'] = topics_3\n",
" extracted_df['actionable_insights'] = insights\n",
" \n",
" # Build search index with enhanced search capabilities\n",
" self.search_engine.build_index(extracted_df, 'combined_text')\n",
" \n",
" # Save processed data\n",
" output_file = 'processed_data.xlsx'\n",
" extracted_df.to_excel(output_file, index=False)\n",
" \n",
" return extracted_df, detected_columns, output_file\n",
" \n",
" def generate_ai_insights(self, df, num_samples=5):\n",
" \"\"\"Generate AI-powered insights using selected model\"\"\"\n",
" if not self.model_manager or not self.model_manager.current_model:\n",
" return \"No AI model available for generating insights\"\n",
" \n",
" if 'combined_text' not in df.columns or df.empty:\n",
" return \"No text data available for AI analysis\"\n",
" \n",
" # Sample some texts for analysis\n",
" sample_texts = df['combined_text'].dropna().head(num_samples).tolist()\n",
" if not sample_texts:\n",
" return \"No valid text samples found\"\n",
" \n",
" # Create prompt for AI analysis\n",
" prompt = f\"\"\"Analyze the following customer feedback samples and provide key insights:\n",
"\n",
"Samples:\n",
"{chr(10).join([f\"{i+1}. {text[:200]}...\" if len(text) > 200 else f\"{i+1}. {text}\" for i, text in enumerate(sample_texts)])}\n",
"\n",
"Please provide:\n",
"1. Main themes and patterns\n",
"2. Key sentiment indicators\n",
"3. Actionable recommendations\n",
"4. Areas of concern\n",
"\n",
"Keep the response concise and focused on actionable insights.\"\"\"\n",
"\n",
" # Generate insights using selected model\n",
" try:\n",
" response = self.model_manager.generate_text(prompt, max_tokens=500)\n",
" if response:\n",
" return f\"**AI Insights (using {self.model_manager.current_model}):**\\n\\n{response}\"\n",
" else:\n",
" return \"Failed to generate AI insights. Please check your API configuration.\"\n",
" except Exception as e:\n",
" return f\"Error generating AI insights: {str(e)}\"\n",
" \n",
" def generate_visualizations(self, df):\n",
" \"\"\"Generate various visualizations\"\"\"\n",
" visualizations = {}\n",
" \n",
" if 'sentiment' in df.columns:\n",
" # Sentiment distribution\n",
" sentiment_counts = df['sentiment'].value_counts()\n",
" fig_sentiment = px.pie(\n",
" values=sentiment_counts.values,\n",
" names=sentiment_counts.index,\n",
" title=\"Sentiment Distribution\",\n",
" color_discrete_map={\n",
" 'Positive': '#27AE60',\n",
" 'Negative': '#E74C3C',\n",
" 'Neutral': '#95A5A6'\n",
" }\n",
" )\n",
" visualizations['Sentiment Distribution'] = fig_sentiment\n",
" \n",
" if 'topic_1' in df.columns:\n",
" # Combine all topics for overall topic distribution\n",
" all_topics = []\n",
" for col in ['topic_1', 'topic_2', 'topic_3']:\n",
" if col in df.columns:\n",
" topics = df[col].dropna().tolist()\n",
" all_topics.extend([t for t in topics if t != ''])\n",
" \n",
" if all_topics:\n",
" topic_counts = Counter(all_topics)\n",
" top_topics = dict(topic_counts.most_common(15))\n",
" \n",
" fig_topics = px.bar(\n",
" x=list(top_topics.values()),\n",
" y=list(top_topics.keys()),\n",
" orientation='h',\n",
" title=\"Top 15 Specific Topics\",\n",
" labels={'x': 'Count', 'y': 'Topic'}\n",
" )\n",
" visualizations['Topic Distribution'] = fig_topics\n",
" \n",
" if 'sentiment' in df.columns and 'topic_1' in df.columns:\n",
" # Sentiment by primary topic (topic_1)\n",
" df_temp = df[df['topic_1'] != ''].copy()\n",
" if not df_temp.empty:\n",
" # Get top 10 topics for cleaner visualization\n",
" top_topics = df_temp['topic_1'].value_counts().head(10).index\n",
" df_filtered = df_temp[df_temp['topic_1'].isin(top_topics)]\n",
" \n",
" pivot_table = pd.crosstab(df_filtered['topic_1'], df_filtered['sentiment'])\n",
" fig_heatmap = px.imshow(\n",
" pivot_table,\n",
" labels=dict(x=\"Sentiment\", y=\"Primary Topic\", color=\"Count\"),\n",
" title=\"Sentiment by Primary Topic Heatmap\",\n",
" color_continuous_scale=\"RdYlGn\"\n",
" )\n",
" visualizations['Sentiment by Topic'] = fig_heatmap\n",
" \n",
" if 'date' in df.columns and 'sentiment' in df.columns:\n",
" # Sentiment over time\n",
" df_time = df.copy()\n",
" df_time['date'] = pd.to_datetime(df_time['date'])\n",
" time_data = df_time.groupby([pd.Grouper(key='date', freq='M'), 'sentiment']).size().reset_index(name='count')\n",
" \n",
" fig_timeline = px.line(\n",
" time_data,\n",
" x='date',\n",
" y='count',\n",
" color='sentiment',\n",
" title=\"Sentiment Trends Over Time\",\n",
" color_discrete_map={\n",
" 'Positive': '#27AE60',\n",
" 'Negative': '#E74C3C',\n",
" 'Neutral': '#95A5A6'\n",
" }\n",
" )\n",
" visualizations['Sentiment Timeline'] = fig_timeline\n",
" \n",
" if 'actionable_insights' in df.columns:\n",
" # Top actionable insights\n",
" all_insights = []\n",
" for insight in df['actionable_insights']:\n",
" if insight and insight != \"\":\n",
" # Split by comma as we're now using comma-separated insights\n",
" all_insights.extend([i.strip() for i in insight.split(',')])\n",
" \n",
" if all_insights:\n",
" insight_counts = Counter(all_insights)\n",
" top_insights = dict(insight_counts.most_common(10))\n",
" \n",
" fig_insights = px.bar(\n",
" x=list(top_insights.values()),\n",
" y=list(top_insights.keys()),\n",
" orientation='h',\n",
" title=\"Top 10 Actionable Insights\",\n",
" labels={'x': 'Frequency', 'y': 'Insight'}\n",
" )\n",
" visualizations['Top Insights'] = fig_insights\n",
" \n",
" return visualizations"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5ee86a52-b195-4010-a2b7-3abf57bf9949",
"metadata": {},
"outputs": [],
"source": [
"# ===== GRADIO INTERFACE =====\n",
"# Global variables\n",
"analyzer = None\n",
"current_data = None\n",
"current_visualizations = None\n",
"\n",
"def update_model(model_name):\n",
" \"\"\"Update the selected AI model\"\"\"\n",
" global model_manager\n",
" \n",
" if model_manager.set_model(model_name):\n",
" return f\"✅ Model switched to: {model_name}\"\n",
" else:\n",
" return f\"❌ Failed to switch to: {model_name}\"\n",
"\n",
"def process_file(file, model_name):\n",
" \"\"\"Process uploaded file with selected model\"\"\"\n",
" global analyzer, current_data, current_visualizations, model_manager\n",
" \n",
" if file is None:\n",
" return \"Please upload a file\", None, None, None, None, None, gr.update(choices=[])\n",
" \n",
" try:\n",
" # Update model if changed\n",
" if model_name and model_manager:\n",
" model_manager.set_model(model_name)\n",
" \n",
" analyzer = EnhancedTextAnalyzer(model_manager)\n",
" \n",
" # Load file\n",
" df, message = analyzer.load_file(file)\n",
" if df is None:\n",
" return message, None, None, None, None, None, gr.update(choices=[])\n",
" \n",
" # Process data\n",
" processed_df, detected_cols, output_file = analyzer.process_data(df)\n",
" current_data = processed_df\n",
" \n",
" # Generate visualizations\n",
" visualizations = analyzer.generate_visualizations(processed_df)\n",
" current_visualizations = visualizations\n",
" \n",
" # Generate AI insights\n",
" ai_insights = analyzer.generate_ai_insights(processed_df)\n",
" \n",
" # Create summary - safely handle detected columns\n",
" text_cols = list(detected_cols.get('text_columns', []))[:3] if detected_cols.get('text_columns') else []\n",
" id_cols = list(detected_cols.get('id_columns', []))[:3] if detected_cols.get('id_columns') else []\n",
" product_cols = list(detected_cols.get('product_columns', []))[:3] if detected_cols.get('product_columns') else []\n",
" \n",
" summary = f\"\"\"\n",
" ### ✅ File Processing Complete!\n",
" \n",
" **Detected Columns:**\n",
" - Text Columns: {', '.join(text_cols) if text_cols else 'None'}\n",
" - ID Columns: {', '.join(id_cols) if id_cols else 'Auto-generated'}\n",
" - Product Columns: {', '.join(product_cols) if product_cols else 'None'}\n",
" \n",
" **Analysis Results:**\n",
" - Total Records: {len(processed_df)}\n",
" - Processed File Saved: {output_file}\n",
" - AI Model Used: {model_manager.current_model if model_manager else 'None'}\n",
" \"\"\"\n",
" \n",
" # Data preview\n",
" preview = processed_df.head(10)\n",
" \n",
" # Get first visualization\n",
" first_viz = list(visualizations.values())[0] if visualizations else None\n",
" \n",
" return (\n",
" summary,\n",
" preview,\n",
" output_file,\n",
" ai_insights,\n",
" first_viz,\n",
" \"Ready for search\",\n",
" gr.update(choices=list(visualizations.keys()))\n",
" )\n",
" \n",
" except Exception as e:\n",
" return f\"Error: {str(e)}\", None, None, None, None, None, gr.update(choices=[])\n",
"\n",
"def search_data(query):\n",
" \"\"\"Search through the data with enhanced semantic search\"\"\"\n",
" global analyzer, current_data\n",
" \n",
" if analyzer is None or current_data is None:\n",
" return \"Please process a file first\", None, None\n",
" \n",
" if not query:\n",
" return \"Please enter a search query\", None, None\n",
" \n",
" try:\n",
" results = analyzer.search_engine.search(query, top_k=10)\n",
" \n",
" if results.empty:\n",
" return \"No results found\", None, None\n",
" \n",
" # Select relevant columns for display (updated to include new topic columns)\n",
" display_cols = ['unique_id', 'combined_text', 'sentiment', 'topic_1', 'topic_2', 'topic_3', 'actionable_insights', 'search_score']\n",
" display_cols = [col for col in display_cols if col in results.columns]\n",
" \n",
" results_display = results[display_cols]\n",
" \n",
" # Save search results\n",
" search_output = f\"search_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx\"\n",
" results_display.to_excel(search_output, index=False)\n",
" \n",
" return f\"Found {len(results)} results\", results_display.head(10), search_output\n",
" \n",
" except Exception as e:\n",
" return f\"Search error: {str(e)}\", None, None\n",
"\n",
"def update_visualization(viz_type):\n",
" \"\"\"Update displayed visualization\"\"\"\n",
" global current_visualizations\n",
" \n",
" if current_visualizations and viz_type in current_visualizations:\n",
" return current_visualizations[viz_type]\n",
" return None\n",
"\n",
"def export_results(format_type):\n",
" \"\"\"Export processed data in different formats\"\"\"\n",
" global current_data\n",
" \n",
" if current_data is None:\n",
" return \"No data to export\", None\n",
" \n",
" try:\n",
" timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\n",
" \n",
" if format_type == \"Excel\":\n",
" output_file = f\"analysis_results_{timestamp}.xlsx\"\n",
" current_data.to_excel(output_file, index=False)\n",
" else: # CSV\n",
" output_file = f\"analysis_results_{timestamp}.csv\"\n",
" current_data.to_csv(output_file, index=False)\n",
" \n",
" return f\"Data exported to {output_file}\", output_file\n",
" \n",
" except Exception as e:\n",
" return f\"Export error: {str(e)}\", None"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "38bf0375-9ef8-488c-821f-288c4f59ff5d",
"metadata": {},
"outputs": [],
"source": [
"# Create Gradio interface\n",
"def create_interface():\n",
" \"\"\"Create the Gradio interface with model selection\"\"\"\n",
" \n",
" with gr.Blocks(theme=gr.themes.Soft()) as app:\n",
" gr.Markdown(\n",
" \"\"\"\n",
" # 📊 Enhanced Text Analytics AI Agent\n",
" ### Smart Column Detection & Comprehensive Text Analysis with Multiple AI Models\n",
" \n",
" **Features:**\n",
" - 🤖 Multiple AI Model Support (OpenAI, Anthropic, Deepseek, Groq, Google)\n",
" - 🔍 Automatic detection of text, ID, and product columns\n",
" - 💾 Memory-efficient processing with automatic file cleanup\n",
" - 😊 Sentiment analysis with scoring\n",
" - 🎯 Topic/theme extraction\n",
" - 💡 Actionable insights generation\n",
" - 🔎 Advanced text search with similarity scoring\n",
" - 📈 Multiple visualization options\n",
" - 📥 Export results in Excel or CSV format\n",
" \"\"\"\n",
" )\n",
" \n",
" with gr.Tab(\"📤 Upload & Process\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" # Model selection dropdown\n",
" model_dropdown = gr.Dropdown(\n",
" label=\"🤖 Select AI Model\",\n",
" choices=model_manager.get_available_models(),\n",
" value=model_manager.current_model if model_manager.current_model else None,\n",
" interactive=True\n",
" )\n",
" \n",
" file_upload = gr.File(\n",
" label=\"Upload Data File\",\n",
" file_types=[\".csv\", \".xlsx\", \".xls\", \".json\"]\n",
" )\n",
" process_btn = gr.Button(\"🚀 Process File\", variant=\"primary\")\n",
" \n",
" with gr.Column(scale=2):\n",
" status_output = gr.Markdown(label=\"Processing Status\")\n",
" ai_insights = gr.Markdown(label=\"AI-Generated Insights\")\n",
" \n",
" with gr.Row():\n",
" data_preview = gr.Dataframe(\n",
" label=\"Data Preview (First 10 rows)\",\n",
" interactive=False\n",
" )\n",
" \n",
" processed_file = gr.File(\n",
" label=\"📁 Processed Data File\",\n",
" interactive=False\n",
" )\n",
" \n",
" with gr.Tab(\"🔍 Search\"):\n",
" gr.Markdown(\"### Search through your text data\")\n",
" \n",
" with gr.Row():\n",
" search_input = gr.Textbox(\n",
" label=\"Enter search query\",\n",
" placeholder=\"Type keywords to search...\"\n",
" )\n",
" search_btn = gr.Button(\"🔎 Search\", variant=\"primary\")\n",
" \n",
" search_status = gr.Markdown(label=\"Search Status\")\n",
" search_results = gr.Dataframe(\n",
" label=\"Search Results\",\n",
" interactive=False\n",
" )\n",
" search_file = gr.File(\n",
" label=\"📥 Download Search Results\",\n",
" interactive=False\n",
" )\n",
" \n",
" with gr.Tab(\"📈 Visualizations\"):\n",
" with gr.Row():\n",
" viz_selector = gr.Dropdown(\n",
" label=\"Select Visualization\",\n",
" choices=[],\n",
" interactive=True\n",
" )\n",
" \n",
" viz_plot = gr.Plot(label=\"Visualization\")\n",
" \n",
" with gr.Tab(\"📥 Export\"):\n",
" gr.Markdown(\"### Export your analyzed data\")\n",
" \n",
" with gr.Row():\n",
" export_format = gr.Radio(\n",
" choices=[\"Excel\", \"CSV\"],\n",
" value=\"Excel\",\n",
" label=\"Export Format\"\n",
" )\n",
" export_btn = gr.Button(\"📥 Export Data\", variant=\"primary\")\n",
" \n",
" export_status = gr.Markdown(label=\"Export Status\")\n",
" export_file = gr.File(\n",
" label=\"📁 Download Exported File\",\n",
" interactive=False\n",
" )\n",
" \n",
" # Event handlers\n",
" model_dropdown.change(\n",
" fn=update_model,\n",
" inputs=[model_dropdown],\n",
" outputs=[status_output]\n",
" )\n",
" \n",
" process_btn.click(\n",
" fn=process_file,\n",
" inputs=[file_upload, model_dropdown],\n",
" outputs=[\n",
" status_output,\n",
" data_preview,\n",
" processed_file,\n",
" ai_insights,\n",
" viz_plot,\n",
" search_status,\n",
" viz_selector\n",
" ]\n",
" )\n",
" \n",
" search_btn.click(\n",
" fn=search_data,\n",
" inputs=[search_input],\n",
" outputs=[search_status, search_results, search_file]\n",
" )\n",
" \n",
" viz_selector.change(\n",
" fn=update_visualization,\n",
" inputs=[viz_selector],\n",
" outputs=[viz_plot]\n",
" )\n",
" \n",
" export_btn.click(\n",
" fn=export_results,\n",
" inputs=[export_format],\n",
" outputs=[export_status, export_file]\n",
" )\n",
" \n",
" return app"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6c5a0767-a788-43a8-911c-04e81814f4c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7861\n",
"* Running on public URL: https://8190830de481785995.gradio.live\n",
"\n",
"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://8190830de481785995.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Keyboard interruption in main thread... closing server.\n",
"Killing tunnel 127.0.0.1:7861 <> https://8190830de481785995.gradio.live\n"
]
}
],
"source": [
"# Launch the application\n",
"if __name__ == \"__main__\":\n",
" app = create_interface()\n",
" app.launch(share=True, debug=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4f382d04-cee3-40ea-9687-5f2dff2282f7",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (2621292756.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn[12], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m python -m textblob.download_corpora\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"python -m textblob.download_corpora"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63afdaca-562b-4846-8fb2-c699f7ab6615",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d82bb0bb-053e-4c29-af8b-b732dfcb47ad",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "12da3957-a063-48f8-8916-e552cc317280",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|