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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| import json | |
| # Define your API key and endpoint | |
| api_key = 'AIzaSyAQ4dXlOkF8rPC21f6omTS4p6v-uJ2vVIg' | |
| url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-exp:generateContent" | |
| headers = {'Content-Type': 'application/json'} | |
| # Cache the sentiment analysis function to improve performance | |
| def analyze_sentiment(text): | |
| """ | |
| Analyze the sentiment of the given text using the Gemini API. | |
| """ | |
| system_prompt = """ | |
| You are a Sentiment Analysis Tool (SEA). Analyze the following comments and classify the sentiment of each as positive, neutral, or negative. | |
| Return the results in the following format: | |
| Comment: <comment> | |
| Sentiment: <sentiment> | |
| --- | |
| Additionally, provide actionable insights into customer satisfaction trends in the following format: | |
| ### Suggestions for Improvement: | |
| - <suggestion 1> | |
| - <suggestion 2> | |
| """ | |
| data = { | |
| "contents": [{ | |
| "parts": [{"text": f"{system_prompt}\n\n{text}"}] | |
| }] | |
| } | |
| response = requests.post(url, headers=headers, json=data, params={'key': api_key}) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| st.error(f"Request failed with status code {response.status_code}: {response.text}") | |
| return None | |
| def read_file_content(file, file_type): | |
| """ | |
| Read the entire content of the file based on its type. | |
| """ | |
| if file_type == 'csv': | |
| df = pd.read_csv(file) | |
| text = ' '.join(df.apply(lambda x: ' '.join(x.dropna().astype(str)), axis=1)) | |
| elif file_type == 'xlsx': | |
| df = pd.read_excel(file) | |
| text = ' '.join(df.apply(lambda x: ' '.join(x.dropna().astype(str)), axis=1)) | |
| elif file_type == 'json': | |
| df = pd.read_json(file) | |
| text = ' '.join(df.apply(lambda x: ' '.join(x.dropna().astype(str)), axis=1)) | |
| elif file_type == 'txt' or file_type == 'md': | |
| text = file.read().decode('utf-8') | |
| else: | |
| st.error("Unsupported file type.") | |
| return None | |
| return text | |
| def process_large_text(text, chunk_size=5000): | |
| """ | |
| Split large text into smaller chunks for processing. | |
| """ | |
| chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)] | |
| return chunks | |
| def display_sentiment_results(response_text, file_name): | |
| """ | |
| Display sentiment analysis results for a single file. | |
| """ | |
| # Parse comments and sentiments | |
| results = [] | |
| lines = response_text.split('\n') | |
| comment = None | |
| sentiment = None | |
| for line in lines: | |
| if line.startswith("Comment:"): | |
| comment = line.replace("Comment:", "").strip() | |
| elif line.startswith("Sentiment:"): | |
| sentiment = line.replace("Sentiment:", "").strip() | |
| if comment and sentiment: | |
| results.append((comment, sentiment)) | |
| comment = None | |
| sentiment = None | |
| # Display results | |
| st.write(f"### Sentiment Analysis Results for **{file_name}**") | |
| df_results = pd.DataFrame(results, columns=['Comment/Prompt', 'Sentiment']) | |
| st.dataframe(df_results) | |
| # Sentiment distribution | |
| sentiment_counts = df_results['Sentiment'].value_counts().reset_index() | |
| sentiment_counts.columns = ['Sentiment', 'Count'] | |
| with st.expander(f"View Sentiment Distribution for {file_name}"): | |
| st.bar_chart(sentiment_counts.set_index('Sentiment')) | |
| # Suggestions | |
| suggestions = [] | |
| current_section = None | |
| for line in lines: | |
| if line.startswith("### Suggestions for Improvement:"): | |
| current_section = "Suggestions for Improvement" | |
| elif current_section and line.startswith("- "): | |
| suggestions.append(line.replace("- ", "").strip()) | |
| if suggestions: | |
| st.write("### Suggestions for Improvement") | |
| for suggestion in suggestions: | |
| st.write(f"- {suggestion}") | |
| else: | |
| st.warning("No suggestions available.") | |
| # CSV download | |
| output_file = f"sentiment_analysis_results_{file_name}.csv" | |
| df_results.to_csv(output_file, index=False) | |
| st.download_button( | |
| label=f"Download Results for {file_name} as CSV", | |
| data=open(output_file, 'rb').read(), | |
| file_name=output_file, | |
| mime='text/csv', | |
| ) | |
| # Streamlit layout | |
| st.set_page_config(page_title="Sentiment Analysis Tool", layout="wide") | |
| st.title("Sentiment Analysis Tool (SEA) 💬") | |
| st.write("Analyze customer feedback with sentiment classification and actionable insights.") | |
| # Sidebar for instructions | |
| with st.sidebar: | |
| st.header("Instructions 📄") | |
| st.write(""" | |
| 1. Upload one or more files containing customer feedback in the main area. | |
| 2. Analyze real-time feedback using the text input box. | |
| 3. Download sentiment analysis results as CSV files. | |
| """) | |
| st.write("---") | |
| st.header("About") | |
| st.write("This app uses the Gemini API for sentiment analysis and provides actionable insights.") | |
| # Main layout with tabs | |
| tab1, tab2 = st.tabs(["📁 File Analysis", "✍️ Real-Time Feedback"]) | |
| with tab1: | |
| st.write("### Upload one or more files for batch sentiment analysis:") | |
| uploaded_files = st.file_uploader("Choose files", type=["csv", "xlsx", "json", "txt", "md"], accept_multiple_files=True) | |
| if uploaded_files: | |
| for uploaded_file in uploaded_files: | |
| file_type = uploaded_file.name.split('.')[-1] | |
| with st.spinner(f"Processing {uploaded_file.name}..."): | |
| # Read the entire file content | |
| text = read_file_content(uploaded_file, file_type) | |
| if text: | |
| # Process large text in chunks if necessary | |
| chunks = process_large_text(text) | |
| combined_results = "" | |
| for chunk in chunks: | |
| sentiment_result = analyze_sentiment(chunk) | |
| if sentiment_result: | |
| response_text = sentiment_result.get('candidates', [{}])[0].get('content', {}).get('parts', [{}])[0].get('text', '').strip() | |
| combined_results += response_text + "\n" | |
| if combined_results: | |
| display_sentiment_results(combined_results, uploaded_file.name) | |
| else: | |
| st.error(f"Sentiment analysis failed for {uploaded_file.name}.") | |
| with tab2: | |
| st.write("### Enter your feedback for real-time analysis:") | |
| feedback_input = st.text_area("Enter your feedback:", placeholder="Type your feedback here...") | |
| if st.button("Analyze Sentiment"): | |
| if feedback_input.strip() == "": | |
| st.warning("Please enter some feedback to analyze.") | |
| else: | |
| with st.spinner("Analyzing sentiment..."): | |
| sentiment_result = analyze_sentiment(feedback_input) | |
| if sentiment_result: | |
| sentiment = sentiment_result.get('candidates', [{}])[0].get('content', {}).get('parts', [{}])[0].get('text', '').strip().lower() | |
| if "positive" in sentiment: | |
| st.success(f"Sentiment: **Positive** 😊") | |
| elif "neutral" in sentiment: | |
| st.info(f"Sentiment: **Neutral** 😐") | |
| elif "negative" in sentiment: | |
| st.error(f"Sentiment: **Negative** 😠") | |
| else: | |
| st.warning(f"Sentiment: **Unknown** 🤔") | |
| else: | |
| st.error("Sentiment analysis failed.") |