Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+import csv
+
+# 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
+@st.cache_data
+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 process_file(file):
+    """
+    Process the uploaded file, analyze sentiment for all comments, and display results.
+    """
+    file_text = file.read().decode('utf-8')
+    sentiment_result = analyze_sentiment(file_text)
+    if sentiment_result:
+        response_text = sentiment_result.get('candidates', [{}])[0].get('content', {}).get('parts', [{}])[0].get('text', '').strip()
+
+        # 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
+        df = pd.DataFrame(results, columns=['Comment/Prompt', 'Sentiment'])
+        st.write("### Predicted Sentiments")
+        st.dataframe(df)
+
+        # Sentiment distribution
+        sentiment_counts = df['Sentiment'].value_counts().reset_index()
+        sentiment_counts.columns = ['Sentiment', 'Count']
+        with st.expander("View Sentiment Distribution"):
+            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 = 'sentiment_analysis_results.csv'
+        df.to_csv(output_file, index=False)
+        st.download_button(
+            label="Download Results as CSV",
+            data=open(output_file, 'rb').read(),
+            file_name=output_file,
+            mime='text/csv',
+        )
+    else:
+        st.error("Sentiment analysis failed.")
+
+# 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 a CSV file containing customer feedback in the main area.
+    2. Analyze real-time feedback using the text input box.
+    3. Download sentiment analysis results as a CSV file.
+    """)
+    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 a CSV file for batch sentiment analysis:")
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    if uploaded_file is not None:
+        with st.spinner("Processing file..."):
+            process_file(uploaded_file)
+
+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.")