Update app.py

app.py

@@ -1,295 +1,295 @@
-import speech_recognition as sr
-from sentiment_analysis import analyze_sentiment
-from product_recommender import ProductRecommender
-from objection_handler import ObjectionHandler
-from google_sheets import fetch_call_data, store_data_in_sheet
-from sentence_transformers import SentenceTransformer
-from env_setup import config
-import re
-import uuid
-from google.oauth2 import service_account
-from googleapiclient.discovery import build
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objs as go
-import streamlit as st 
-
-# Initialize components
-product_recommender = ProductRecommender(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet2.csv")
-objection_handler = ObjectionHandler(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet3.csv")
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-def generate_comprehensive_summary(chunks):
-    """
-    Generate a comprehensive summary from conversation chunks
-    """
-    # Extract full text from chunks
-    full_text = " ".join([chunk[0] for chunk in chunks])
-    
-    # Perform basic analysis
-    total_chunks = len(chunks)
-    sentiments = [chunk[1] for chunk in chunks]
-    
-    # Determine overall conversation context
-    context_keywords = {
-        'product_inquiry': ['dress', 'product', 'price', 'stock'],
-        'pricing': ['cost', 'price', 'budget'],
-        'negotiation': ['installment', 'payment', 'manage']
-    }
-    
-    # Detect conversation themes
-    themes = []
-    for keyword_type, keywords in context_keywords.items():
-        if any(keyword.lower() in full_text.lower() for keyword in keywords):
-            themes.append(keyword_type)
-    
-    # Basic sentiment analysis
-    positive_count = sentiments.count('POSITIVE')
-    negative_count = sentiments.count('NEGATIVE')
-    neutral_count = sentiments.count('NEUTRAL')
-    
-    # Key interaction highlights
-    key_interactions = []
-    for chunk in chunks:
-        if any(keyword.lower() in chunk[0].lower() for keyword in ['price', 'dress', 'stock', 'installment']):
-            key_interactions.append(chunk[0])
-    
-    # Construct summary
-    summary = f"Conversation Summary:\n"
-    
-    # Context and themes
-    if 'product_inquiry' in themes:
-        summary += "• Customer initiated a product inquiry about items.\n"
-    
-    if 'pricing' in themes:
-        summary += "• Price and budget considerations were discussed.\n"
-    
-    if 'negotiation' in themes:
-        summary += "• Customer and seller explored flexible payment options.\n"
-    
-    # Sentiment insights
-    summary += f"\nConversation Sentiment:\n"
-    summary += f"• Positive Interactions: {positive_count}\n"
-    summary += f"• Negative Interactions: {negative_count}\n"
-    summary += f"• Neutral Interactions: {neutral_count}\n"
-    
-    # Key highlights
-    summary += "\nKey Conversation Points:\n"
-    for interaction in key_interactions[:3]:  # Limit to top 3 key points
-        summary += f"• {interaction}\n"
-    
-    # Conversation outcome
-    if positive_count > negative_count:
-        summary += "\nOutcome: Constructive and potentially successful interaction."
-    elif negative_count > positive_count:
-        summary += "\nOutcome: Interaction may require further follow-up."
-    else:
-        summary += "\nOutcome: Neutral interaction with potential for future engagement."
-    
-    return summary
-
-def is_valid_input(text):
-    text = text.strip().lower()
-    if len(text) < 3 or re.match(r'^[a-zA-Z\s]*$', text) is None:
-        return False
-    return True
-
-def is_relevant_sentiment(sentiment_score):
-    return sentiment_score > 0.4
-
-def calculate_overall_sentiment(sentiment_scores):
-    if sentiment_scores:
-        average_sentiment = sum(sentiment_scores) / len(sentiment_scores)
-        overall_sentiment = (
-            "POSITIVE" if average_sentiment > 0 else
-            "NEGATIVE" if average_sentiment < 0 else
-            "NEUTRAL"
-        )
-    else:
-        overall_sentiment = "NEUTRAL"
-    return overall_sentiment
-
-def real_time_analysis():
-    recognizer = sr.Recognizer()
-    mic = sr.Microphone()
-
-    st.info("Say 'stop' to end the process.")
-
-    sentiment_scores = []
-    transcribed_chunks = []
-    total_text = ""
-
-    try:
-        while True:
-            with mic as source:
-                st.write("Listening...")
-                recognizer.adjust_for_ambient_noise(source)
-                audio = recognizer.listen(source)
-
-            try:
-                st.write("Recognizing...")
-                text = recognizer.recognize_google(audio)
-                st.write(f"*Recognized Text:* {text}")
-
-                if 'stop' in text.lower():
-                    st.write("Stopping real-time analysis...")
-                    break
-
-                # Append to the total conversation
-                total_text += text + " "
-                sentiment, score = analyze_sentiment(text)
-                sentiment_scores.append(score)
-                
-                # Handle objection
-                objection_response = handle_objection(text)
-
-                # Get product recommendation
-                recommendations = []
-                if is_valid_input(text) and is_relevant_sentiment(score):
-                    query_embedding = model.encode([text])
-                    distances, indices = product_recommender.index.search(query_embedding, 1)
-
-                    if distances[0][0] < 1.5:  # Similarity threshold
-                        recommendations = product_recommender.get_recommendations(text)
-
-                transcribed_chunks.append((text, sentiment, score))
-
-                st.write(f"*Sentiment:* {sentiment} (Score: {score})")
-                st.write(f"*Objection Response:* {objection_response}")
-                
-                if recommendations:
-                    st.write("*Product Recommendations:*")
-                    for rec in recommendations:
-                        st.write(rec)
-
-            except sr.UnknownValueError:
-                st.error("Speech Recognition could not understand the audio.")
-            except sr.RequestError as e:
-                st.error(f"Error with the Speech Recognition service: {e}")
-            except Exception as e:
-                st.error(f"Error during processing: {e}")
-
-        # After conversation ends, calculate and display overall sentiment and summary
-        overall_sentiment = calculate_overall_sentiment(sentiment_scores)
-        call_summary = generate_comprehensive_summary(transcribed_chunks)
-        
-        st.subheader("Conversation Summary:")
-        st.write(total_text.strip())
-        st.subheader("Overall Sentiment:")
-        st.write(overall_sentiment)
-
-        # Store data in Google Sheets
-        store_data_in_sheet(
-            config["google_sheet_id"], 
-            transcribed_chunks, 
-            call_summary, 
-            overall_sentiment
-        )
-        st.success("Conversation data stored successfully in Google Sheets!")
-
-    except Exception as e:
-        st.error(f"Error in real-time analysis: {e}")
-
-def handle_objection(text):
-    query_embedding = model.encode([text])
-    distances, indices = objection_handler.index.search(query_embedding, 1)
-    if distances[0][0] < 1.5:  # Adjust similarity threshold as needed
-        responses = objection_handler.handle_objection(text)
-        return "\n".join(responses) if responses else "No objection response found."
-    return "No objection response found."
-
-# (Previous imports remain the same)
-
-def run_app():
-    st.set_page_config(page_title="Sales Call Assistant", layout="wide")
-    st.title("AI Sales Call Assistant")
-
-    st.sidebar.title("Navigation")
-    app_mode = st.sidebar.radio("Choose a mode:", ["Real-Time Call Analysis", "Dashboard"])
-
-    if app_mode == "Real-Time Call Analysis":
-        st.header("Real-Time Sales Call Analysis")
-        if st.button("Start Listening"):
-            real_time_analysis()
-
-    elif app_mode == "Dashboard":
-        st.header("Call Summaries and Sentiment Analysis")
-        try:
-            data = fetch_call_data(config["google_sheet_id"])
-            if data.empty:
-                st.warning("No data available in the Google Sheet.")
-            else:
-                # Sentiment Visualizations
-                sentiment_counts = data['Sentiment'].value_counts()
-                
-                # Pie Chart
-                col1, col2 = st.columns(2)
-                with col1:
-                    st.subheader("Sentiment Distribution")
-                    fig_pie = px.pie(
-                        values=sentiment_counts.values, 
-                        names=sentiment_counts.index, 
-                        title='Call Sentiment Breakdown',
-                        color_discrete_map={
-                            'POSITIVE': 'green', 
-                            'NEGATIVE': 'red', 
-                            'NEUTRAL': 'blue'
-                        }
-                    )
-                    st.plotly_chart(fig_pie)
-
-                # Bar Chart
-                with col2:
-                    st.subheader("Sentiment Counts")
-                    fig_bar = px.bar(
-                        x=sentiment_counts.index, 
-                        y=sentiment_counts.values, 
-                        title='Number of Calls by Sentiment',
-                        labels={'x': 'Sentiment', 'y': 'Number of Calls'},
-                        color=sentiment_counts.index,
-                        color_discrete_map={
-                            'POSITIVE': 'green', 
-                            'NEGATIVE': 'red', 
-                            'NEUTRAL': 'blue'
-                        }
-                    )
-                    st.plotly_chart(fig_bar)
-
-                # Existing Call Details Section
-                st.subheader("All Calls")
-                display_data = data.copy()
-                display_data['Summary Preview'] = display_data['Summary'].str[:100] + '...'
-                st.dataframe(display_data[['Call ID', 'Chunk', 'Sentiment', 'Summary Preview', 'Overall Sentiment']])
-
-                # Dropdown to select Call ID
-                unique_call_ids = data[data['Call ID'] != '']['Call ID'].unique()
-                call_id = st.selectbox("Select a Call ID to view details:", unique_call_ids)
-
-                # Display selected Call ID details
-                call_details = data[data['Call ID'] == call_id]
-                if not call_details.empty:
-                    st.subheader("Detailed Call Information")
-                    st.write(f"**Call ID:** {call_id}")
-                    st.write(f"**Overall Sentiment:** {call_details.iloc[0]['Overall Sentiment']}")
-                    
-                    # Expand summary section
-                    st.subheader("Full Call Summary")
-                    st.text_area("Summary:", 
-                                 value=call_details.iloc[0]['Summary'], 
-                                 height=200, 
-                                 disabled=True)
-                    
-                    # Show all chunks for the selected call
-                    st.subheader("Conversation Chunks")
-                    for _, row in call_details.iterrows():
-                        if pd.notna(row['Chunk']):  
-                            st.write(f"**Chunk:** {row['Chunk']}")
-                            st.write(f"**Sentiment:** {row['Sentiment']}")
-                            st.write("---")  # Separator between chunks
-                else:
-                    st.error("No details available for the selected Call ID.")
-        except Exception as e:
-            st.error(f"Error loading dashboard: {e}")
-
-if __name__ == "__main__":
-    run_app()
+import speech_recognition as sr
+from sentiment_analysis import analyze_sentiment
+from product_recommender import ProductRecommender
+from objection_handler import ObjectionHandler
+from google_sheets import fetch_call_data, store_data_in_sheet
+from sentence_transformers import SentenceTransformer
+from env_setup import config
+import re
+import uuid
+from google.oauth2 import service_account
+from googleapiclient.discovery import build
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objs as go
+import streamlit as st 
+
+# Initialize components
+product_recommender = ProductRecommender(r"C:\Users\shaik\OneDrive\Desktop\Infosys\Project1 - Copy\modules\data\recommendations.csv")
+objection_handler = ObjectionHandler(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet3.csv")
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def generate_comprehensive_summary(chunks):
+    """
+    Generate a comprehensive summary from conversation chunks
+    """
+    # Extract full text from chunks
+    full_text = " ".join([chunk[0] for chunk in chunks])
+    
+    # Perform basic analysis
+    total_chunks = len(chunks)
+    sentiments = [chunk[1] for chunk in chunks]
+    
+    # Determine overall conversation context
+    context_keywords = {
+        'product_inquiry': ['dress', 'product', 'price', 'stock'],
+        'pricing': ['cost', 'price', 'budget'],
+        'negotiation': ['installment', 'payment', 'manage']
+    }
+    
+    # Detect conversation themes
+    themes = []
+    for keyword_type, keywords in context_keywords.items():
+        if any(keyword.lower() in full_text.lower() for keyword in keywords):
+            themes.append(keyword_type)
+    
+    # Basic sentiment analysis
+    positive_count = sentiments.count('POSITIVE')
+    negative_count = sentiments.count('NEGATIVE')
+    neutral_count = sentiments.count('NEUTRAL')
+    
+    # Key interaction highlights
+    key_interactions = []
+    for chunk in chunks:
+        if any(keyword.lower() in chunk[0].lower() for keyword in ['price', 'dress', 'stock', 'installment']):
+            key_interactions.append(chunk[0])
+    
+    # Construct summary
+    summary = f"Conversation Summary:\n"
+    
+    # Context and themes
+    if 'product_inquiry' in themes:
+        summary += "• Customer initiated a product inquiry about items.\n"
+    
+    if 'pricing' in themes:
+        summary += "• Price and budget considerations were discussed.\n"
+    
+    if 'negotiation' in themes:
+        summary += "• Customer and seller explored flexible payment options.\n"
+    
+    # Sentiment insights
+    summary += f"\nConversation Sentiment:\n"
+    summary += f"• Positive Interactions: {positive_count}\n"
+    summary += f"• Negative Interactions: {negative_count}\n"
+    summary += f"• Neutral Interactions: {neutral_count}\n"
+    
+    # Key highlights
+    summary += "\nKey Conversation Points:\n"
+    for interaction in key_interactions[:3]:  # Limit to top 3 key points
+        summary += f"• {interaction}\n"
+    
+    # Conversation outcome
+    if positive_count > negative_count:
+        summary += "\nOutcome: Constructive and potentially successful interaction."
+    elif negative_count > positive_count:
+        summary += "\nOutcome: Interaction may require further follow-up."
+    else:
+        summary += "\nOutcome: Neutral interaction with potential for future engagement."
+    
+    return summary
+
+def is_valid_input(text):
+    text = text.strip().lower()
+    if len(text) < 3 or re.match(r'^[a-zA-Z\s]*$', text) is None:
+        return False
+    return True
+
+def is_relevant_sentiment(sentiment_score):
+    return sentiment_score > 0.4
+
+def calculate_overall_sentiment(sentiment_scores):
+    if sentiment_scores:
+        average_sentiment = sum(sentiment_scores) / len(sentiment_scores)
+        overall_sentiment = (
+            "POSITIVE" if average_sentiment > 0 else
+            "NEGATIVE" if average_sentiment < 0 else
+            "NEUTRAL"
+        )
+    else:
+        overall_sentiment = "NEUTRAL"
+    return overall_sentiment
+
+def real_time_analysis():
+    recognizer = sr.Recognizer()
+    mic = sr.Microphone()
+
+    st.info("Say 'stop' to end the process.")
+
+    sentiment_scores = []
+    transcribed_chunks = []
+    total_text = ""
+
+    try:
+        while True:
+            with mic as source:
+                st.write("Listening...")
+                recognizer.adjust_for_ambient_noise(source)
+                audio = recognizer.listen(source)
+
+            try:
+                st.write("Recognizing...")
+                text = recognizer.recognize_google(audio)
+                st.write(f"*Recognized Text:* {text}")
+
+                if 'stop' in text.lower():
+                    st.write("Stopping real-time analysis...")
+                    break
+
+                # Append to the total conversation
+                total_text += text + " "
+                sentiment, score = analyze_sentiment(text)
+                sentiment_scores.append(score)
+                
+                # Handle objection
+                objection_response = handle_objection(text)
+
+                # Get product recommendation
+                recommendations = []
+                if is_valid_input(text) and is_relevant_sentiment(score):
+                    query_embedding = model.encode([text])
+                    distances, indices = product_recommender.index.search(query_embedding, 1)
+
+                    if distances[0][0] < 1.5:  # Similarity threshold
+                        recommendations = product_recommender.get_recommendations(text)
+
+                transcribed_chunks.append((text, sentiment, score))
+
+                st.write(f"*Sentiment:* {sentiment} (Score: {score})")
+                st.write(f"*Objection Response:* {objection_response}")
+                
+                if recommendations:
+                    st.write("*Product Recommendations:*")
+                    for rec in recommendations:
+                        st.write(rec)
+
+            except sr.UnknownValueError:
+                st.error("Speech Recognition could not understand the audio.")
+            except sr.RequestError as e:
+                st.error(f"Error with the Speech Recognition service: {e}")
+            except Exception as e:
+                st.error(f"Error during processing: {e}")
+
+        # After conversation ends, calculate and display overall sentiment and summary
+        overall_sentiment = calculate_overall_sentiment(sentiment_scores)
+        call_summary = generate_comprehensive_summary(transcribed_chunks)
+        
+        st.subheader("Conversation Summary:")
+        st.write(total_text.strip())
+        st.subheader("Overall Sentiment:")
+        st.write(overall_sentiment)
+
+        # Store data in Google Sheets
+        store_data_in_sheet(
+            config["google_sheet_id"], 
+            transcribed_chunks, 
+            call_summary, 
+            overall_sentiment
+        )
+        st.success("Conversation data stored successfully in Google Sheets!")
+
+    except Exception as e:
+        st.error(f"Error in real-time analysis: {e}")
+
+def handle_objection(text):
+    query_embedding = model.encode([text])
+    distances, indices = objection_handler.index.search(query_embedding, 1)
+    if distances[0][0] < 1.5:  # Adjust similarity threshold as needed
+        responses = objection_handler.handle_objection(text)
+        return "\n".join(responses) if responses else "No objection response found."
+    return "No objection response found."
+
+# (Previous imports remain the same)
+
+def run_app():
+    st.set_page_config(page_title="Sales Call Assistant", layout="wide")
+    st.title("AI Sales Call Assistant")
+
+    st.sidebar.title("Navigation")
+    app_mode = st.sidebar.radio("Choose a mode:", ["Real-Time Call Analysis", "Dashboard"])
+
+    if app_mode == "Real-Time Call Analysis":
+        st.header("Real-Time Sales Call Analysis")
+        if st.button("Start Listening"):
+            real_time_analysis()
+
+    elif app_mode == "Dashboard":
+        st.header("Call Summaries and Sentiment Analysis")
+        try:
+            data = fetch_call_data(config["google_sheet_id"])
+            if data.empty:
+                st.warning("No data available in the Google Sheet.")
+            else:
+                # Sentiment Visualizations
+                sentiment_counts = data['Sentiment'].value_counts()
+                
+                # Pie Chart
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.subheader("Sentiment Distribution")
+                    fig_pie = px.pie(
+                        values=sentiment_counts.values, 
+                        names=sentiment_counts.index, 
+                        title='Call Sentiment Breakdown',
+                        color_discrete_map={
+                            'POSITIVE': 'green', 
+                            'NEGATIVE': 'red', 
+                            'NEUTRAL': 'blue'
+                        }
+                    )
+                    st.plotly_chart(fig_pie)
+
+                # Bar Chart
+                with col2:
+                    st.subheader("Sentiment Counts")
+                    fig_bar = px.bar(
+                        x=sentiment_counts.index, 
+                        y=sentiment_counts.values, 
+                        title='Number of Calls by Sentiment',
+                        labels={'x': 'Sentiment', 'y': 'Number of Calls'},
+                        color=sentiment_counts.index,
+                        color_discrete_map={
+                            'POSITIVE': 'green', 
+                            'NEGATIVE': 'red', 
+                            'NEUTRAL': 'blue'
+                        }
+                    )
+                    st.plotly_chart(fig_bar)
+
+                # Existing Call Details Section
+                st.subheader("All Calls")
+                display_data = data.copy()
+                display_data['Summary Preview'] = display_data['Summary'].str[:100] + '...'
+                st.dataframe(display_data[['Call ID', 'Chunk', 'Sentiment', 'Summary Preview', 'Overall Sentiment']])
+
+                # Dropdown to select Call ID
+                unique_call_ids = data[data['Call ID'] != '']['Call ID'].unique()
+                call_id = st.selectbox("Select a Call ID to view details:", unique_call_ids)
+
+                # Display selected Call ID details
+                call_details = data[data['Call ID'] == call_id]
+                if not call_details.empty:
+                    st.subheader("Detailed Call Information")
+                    st.write(f"**Call ID:** {call_id}")
+                    st.write(f"**Overall Sentiment:** {call_details.iloc[0]['Overall Sentiment']}")
+                    
+                    # Expand summary section
+                    st.subheader("Full Call Summary")
+                    st.text_area("Summary:", 
+                                 value=call_details.iloc[0]['Summary'], 
+                                 height=200, 
+                                 disabled=True)
+                    
+                    # Show all chunks for the selected call
+                    st.subheader("Conversation Chunks")
+                    for _, row in call_details.iterrows():
+                        if pd.notna(row['Chunk']):  
+                            st.write(f"**Chunk:** {row['Chunk']}")
+                            st.write(f"**Sentiment:** {row['Sentiment']}")
+                            st.write("---")  # Separator between chunks
+                else:
+                    st.error("No details available for the selected Call ID.")
+        except Exception as e:
+            st.error(f"Error loading dashboard: {e}")
+
+if __name__ == "__main__":
+    run_app()