Update app.py

app.py

@@ -6,6 +6,7 @@ from gtts import gTTS
 from pydub import AudioSegment
 from pydub.silence import detect_nonsilent
 from waitress import serve
+from simple_salesforce import Salesforce
 
 app = Flask(__name__)
 
@@ -57,6 +58,21 @@ def is_silent_audio(audio_path):
     nonsilent_parts = detect_nonsilent(audio, min_silence_len=500, silence_thresh=audio.dBFS-16)  # Reduced silence duration
     return len(nonsilent_parts) == 0  # If no speech detected
 
+# Salesforce connection details
+sf = Salesforce(username='diggavalli98@gmail.com', password='Sati@1020', security_token='sSSjyhInIsUohKpG8sHzty2q')
+
+# Function to create Salesforce record
+def create_salesforce_record(name, email, phone_number):
+    try:
+        customer_login = sf.Customer_Login__c.create({
+            'Name': name,
+            'Email__c': email,
+            'Phone_Number__c': phone_number
+        })
+        return customer_login
+    except Exception as e:
+        return {"error": f"Failed to create record in Salesforce: {str(e)}"}
+
 @app.route("/")
 def index():
     return render_template("index.html")
@@ -83,7 +99,20 @@ def transcribe():
         result = asr_model(output_audio_path, generate_kwargs={"language": "en"})
         transcribed_text = result["text"].strip().capitalize()
 
-        return jsonify({"text": transcribed_text})
+        # Now, let's split the transcribed text into name, email, and phone number (basic example)
+        parts = transcribed_text.split()  # This is a simplistic approach; you may need a better parsing mechanism
+        name = parts[0]  # Assuming first word is the name
+        email = parts[1] if '@' in parts[1] else "unknown@domain.com"  # Very basic email extraction
+        phone_number = parts[2] if len(parts) > 2 else "0000000000"  # Basic phone number assumption
+
+        # Create record in Salesforce
+        salesforce_response = create_salesforce_record(name, email, phone_number)
+        
+        if "error" in salesforce_response:
+            return jsonify(salesforce_response), 500
+        
+        return jsonify({"text": transcribed_text, "salesforce_record": salesforce_response})
+
     except Exception as e:
         return jsonify({"error": f"Speech recognition error: {str(e)}"}), 500