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import streamlit as st
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import pyaudio
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import json
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from vosk import Model, KaldiRecognizer
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from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
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from sentence_transformers import SentenceTransformer
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import time
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import pandas as pd
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from dotenv import load_dotenv
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import os
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import numpy as np
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def cosine_similarity(a, b):
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return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
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class SalesAnalysisApp:
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def __init__(self):
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model_name = "tabularisai/multilingual-sentiment-analysis"
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.sentiment_analyzer = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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vosk_model_path = os.getenv("VOSK_MODEL_PATH")
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self.vosk_model = Model(vosk_model_path)
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self.recognizer = KaldiRecognizer(self.vosk_model, 16000)
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self.audio = pyaudio.PyAudio()
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self.stream = None
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self.product_data = pd.read_csv(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet2.csv")
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self.objection_data = pd.read_csv(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet3.csv")
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self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
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def get_recommendations(self, text):
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text_embedding = self.sentence_model.encode([text])
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product_embeddings = self.sentence_model.encode(self.product_data['Description'].tolist())
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similarities = [cosine_similarity(text_embedding[0], prod_emb) for prod_emb in product_embeddings]
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top_indices = np.argsort(similarities)[-5:][::-1]
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return self.product_data.iloc[top_indices]['Product'].tolist()
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def get_objection_response(self, text):
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text_embedding = self.sentence_model.encode([text])
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objection_embeddings = self.sentence_model.encode(self.objection_data['Objection'].tolist())
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similarities = [cosine_similarity(text_embedding[0], obj_emb) for obj_emb in objection_embeddings]
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max_similarity = max(similarities)
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if max_similarity > 0.5:
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top_idx = np.argmax(similarities)
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return self.objection_data.iloc[top_idx]['Response']
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return None
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def analyze_sentiment(self, text):
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if not text.strip():
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return "NEUTRAL", 0.0
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result = self.sentiment_analyzer(text.strip().lower())[0]
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sentiment_map = {
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'Very Negative': "NEGATIVE",
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'Negative': "NEGATIVE",
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'Neutral': "NEUTRAL",
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'Positive': "POSITIVE",
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'Very Positive': "POSITIVE"
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}
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return sentiment_map.get(result['label'], "NEUTRAL"), result['score']
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def run_app(self):
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st.title("Real-time Sales Call Analysis")
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if st.button("Start Recording"):
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self.stream = self.audio.open(format=pyaudio.paInt16,
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channels=1,
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rate=16000,
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input=True,
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frames_per_buffer=4000)
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transcript_placeholder = st.empty()
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sentiment_placeholder = st.empty()
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recommendations_placeholder = st.empty()
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objections_placeholder = st.empty()
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try:
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while True:
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data = self.stream.read(4000, exception_on_overflow=False)
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if self.recognizer.AcceptWaveform(data):
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result = json.loads(self.recognizer.Result())
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text = result["text"]
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if text:
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transcript_placeholder.write(f"Transcription: {text}")
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sentiment, score = self.analyze_sentiment(text)
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sentiment_placeholder.write(f"Sentiment: {sentiment} (Score: {score:.2f})")
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recommendations = self.get_recommendations(text)
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if recommendations:
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recommendations_placeholder.write("Product Recommendations:")
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for rec in recommendations:
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recommendations_placeholder.write(f"- {rec}")
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objection_response = self.get_objection_response(text)
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if objection_response:
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objections_placeholder.write(f"Suggested Response: {objection_response}")
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time.sleep(0.1)
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except Exception as e:
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st.error(f"Error: {str(e)}")
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if self.stream:
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self.stream.stop_stream()
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self.stream.close()
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if __name__ == "__main__":
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app = SalesAnalysisApp()
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app.run_app() |
