Delete main.py

main.py

@@ -1,117 +0,0 @@
-import streamlit as st
-import pyaudio
-import json
-from vosk import Model, KaldiRecognizer
-from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
-from sentence_transformers import SentenceTransformer
-import time
-import pandas as pd
-from dotenv import load_dotenv
-import os
-import numpy as np
-
-def cosine_similarity(a, b):
-    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
-
-class SalesAnalysisApp:
-    def __init__(self):
-        model_name = "tabularisai/multilingual-sentiment-analysis"
-        model = AutoModelForSequenceClassification.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.sentiment_analyzer = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
-        
-        vosk_model_path = os.getenv("VOSK_MODEL_PATH")
-        self.vosk_model = Model(vosk_model_path)
-        self.recognizer = KaldiRecognizer(self.vosk_model, 16000)
-        
-        self.audio = pyaudio.PyAudio()
-        self.stream = None
-        
-        self.product_data = pd.read_csv(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet2.csv")
-        self.objection_data = pd.read_csv(r"C:\Users\shaik\Downloads\Sales Calls Transcriptions - Sheet3.csv")
-        
-        self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
-
-    def get_recommendations(self, text):
-        text_embedding = self.sentence_model.encode([text])
-        product_embeddings = self.sentence_model.encode(self.product_data['Description'].tolist())
-        
-        similarities = [cosine_similarity(text_embedding[0], prod_emb) for prod_emb in product_embeddings]
-        top_indices = np.argsort(similarities)[-5:][::-1]
-        return self.product_data.iloc[top_indices]['Product'].tolist()
-
-    def get_objection_response(self, text):
-        text_embedding = self.sentence_model.encode([text])
-        objection_embeddings = self.sentence_model.encode(self.objection_data['Objection'].tolist())
-        
-        similarities = [cosine_similarity(text_embedding[0], obj_emb) for obj_emb in objection_embeddings]
-        max_similarity = max(similarities)
-        if max_similarity > 0.5:
-            top_idx = np.argmax(similarities)
-            return self.objection_data.iloc[top_idx]['Response']
-        return None
-
-    # Rest of the code remains the same...
-    def analyze_sentiment(self, text):
-        if not text.strip():
-            return "NEUTRAL", 0.0
-        result = self.sentiment_analyzer(text.strip().lower())[0]
-        sentiment_map = {
-            'Very Negative': "NEGATIVE",
-            'Negative': "NEGATIVE",
-            'Neutral': "NEUTRAL",
-            'Positive': "POSITIVE",
-            'Very Positive': "POSITIVE"
-        }
-        return sentiment_map.get(result['label'], "NEUTRAL"), result['score']
-
-    def run_app(self):
-        st.title("Real-time Sales Call Analysis")
-        
-        if st.button("Start Recording"):
-            self.stream = self.audio.open(format=pyaudio.paInt16,
-                                        channels=1,
-                                        rate=16000,
-                                        input=True,
-                                        frames_per_buffer=4000)
-            
-            transcript_placeholder = st.empty()
-            sentiment_placeholder = st.empty()
-            recommendations_placeholder = st.empty()
-            objections_placeholder = st.empty()
-            
-            try:
-                while True:
-                    data = self.stream.read(4000, exception_on_overflow=False)
-                    
-                    if self.recognizer.AcceptWaveform(data):
-                        result = json.loads(self.recognizer.Result())
-                        text = result["text"]
-                        
-                        if text:
-                            transcript_placeholder.write(f"Transcription: {text}")
-                            
-                            sentiment, score = self.analyze_sentiment(text)
-                            sentiment_placeholder.write(f"Sentiment: {sentiment} (Score: {score:.2f})")
-                            
-                            recommendations = self.get_recommendations(text)
-                            if recommendations:
-                                recommendations_placeholder.write("Product Recommendations:")
-                                for rec in recommendations:
-                                    recommendations_placeholder.write(f"- {rec}")
-                            
-                            objection_response = self.get_objection_response(text)
-                            if objection_response:
-                                objections_placeholder.write(f"Suggested Response: {objection_response}")
-                            
-                            time.sleep(0.1)
-                            
-            except Exception as e:
-                st.error(f"Error: {str(e)}")
-                if self.stream:
-                    self.stream.stop_stream()
-                    self.stream.close()
-
-if __name__ == "__main__":
-    app = SalesAnalysisApp()
-    app.run_app()