Update app.py

app.py

@@ -1,10 +1,8 @@
 import gradio as gr
 import os
 import tempfile
-import json
 import requests
 from pathlib import Path
-from transformers import pipeline
 
 # Create necessary directories
 DOCUMENTS_DIR = Path("documents")
@@ -35,32 +33,25 @@ DEFAULT_RESPONSES = {
 # Simple document storage (in-memory for this example)
 knowledge_base = []
 
-# Create a classifier
-try:
-    classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
-except Exception as e:
-    print(f"Error loading classifier: {e}")
-    classifier = None
-
 def classify_intent(text):
-    """Classify the intent of the user's message"""
-    if not text or not classifier:
-        return "other", 0.0
-    
-    try:
-        results = classifier(
-            text, 
-            candidate_labels=POSSIBLE_INTENTS,
-            hypothesis_template="This is a {} request."
-        )
+    """Simple keyword-based intent classification"""
+    if not text:
+        return "other", 0.6
         
-        top_intent = results["labels"][0]
-        confidence = results["scores"][0]
-        
-        return top_intent, confidence
-    except Exception as e:
-        print(f"Error classifying intent: {e}")
-        return "other", 0.0
+    text = text.lower()
+    
+    if any(word in text for word in ["buy", "purchase", "product", "price", "cost"]):
+        return "product_inquiry", 0.8
+    elif any(word in text for word in ["help", "issue", "problem", "fix", "broken"]):
+        return "technical_support", 0.8
+    elif any(word in text for word in ["bill", "payment", "charge", "invoice"]):
+        return "billing_question", 0.8
+    elif any(word in text for word in ["appointment", "schedule", "book", "meeting"]):
+        return "appointment_scheduling", 0.8
+    elif any(word in text for word in ["unhappy", "disappointed", "complaint", "bad"]):
+        return "complaint", 0.8
+    else:
+        return "general_information", 0.6
 
 def load_pdf(file):
     """Load a PDF document into the knowledge base"""
@@ -130,19 +121,23 @@ def list_documents():
     }
 
 # Twilio voice handlers
-def twilio_call_handler():
+def twilio_call_handler(data=None):
     """Handle incoming Twilio calls"""
     twiml = """<?xml version="1.0" encoding="UTF-8"?>
     <Response>
         <Say>Hello! Thank you for calling. How can I help you today?</Say>
-        <Gather input="speech" action="https://huggingface.co/spaces/iajitpanday/vBot-1.5/api/twilio/speech" method="POST" speechTimeout="auto" speechModel="phone_call"/>
+        <Gather input="speech" action="https://huggingface.co/spaces/iajitpanday/vBot-1.5/api/predict" method="POST" speechTimeout="auto" speechModel="phone_call"/>
         <Say>I didn't hear anything. Please call back when you're ready.</Say>
     </Response>
     """
     return twiml
 
-def twilio_speech_handler(speech_result=""):
+def twilio_speech_handler(data):
     """Process speech from Twilio"""
+    # Extract speech from Twilio request
+    speech_result = data.get("SpeechResult", "")
+    
+    # Process the speech
     intent, _ = classify_intent(speech_result)
     response = generate_response(speech_result, intent)
     
@@ -151,30 +146,12 @@ def twilio_speech_handler(speech_result=""):
         <Say>{response}</Say>
         <Pause length="1"/>
         <Say>Is there anything else I can help you with today?</Say>
-        <Gather input="speech" action="https://huggingface.co/spaces/iajitpanday/vBot-1.5/api/twilio/followup" method="POST" speechTimeout="auto" speechModel="phone_call"/>
+        <Gather input="speech" action="https://huggingface.co/spaces/iajitpanday/vBot-1.5/api/predict" method="POST" speechTimeout="auto" speechModel="phone_call"/>
         <Say>Thank you for calling. Have a great day!</Say>
     </Response>
     """
     return twiml
 
-def twilio_followup_handler(speech_result=""):
-    """Handle follow-up responses"""
-    if any(word in speech_result.lower() for word in ["yes", "yeah", "sure", "please", "correct"]):
-        twiml = """<?xml version="1.0" encoding="UTF-8"?>
-        <Response>
-            <Say>Great! How else can I help you today?</Say>
-            <Gather input="speech" action="https://huggingface.co/spaces/iajitpanday/vBot-1.5/api/twilio/speech" method="POST" speechTimeout="auto" speechModel="phone_call"/>
-            <Say>I didn't hear anything. Thank you for calling. Goodbye!</Say>
-        </Response>
-        """
-    else:
-        twiml = """<?xml version="1.0" encoding="UTF-8"?>
-        <Response>
-            <Say>Thank you for calling. Have a great day!</Say>
-        </Response>
-        """
-    return twiml
-
 # Create Gradio interface
 with gr.Blocks(title="Call Assistant System") as demo:
     gr.Markdown("# Call Assistant System")
@@ -236,35 +213,38 @@ with gr.Blocks(title="Call Assistant System") as demo:
         gr.Markdown("""
         ## Twilio Integration Instructions
         
-        This app provides API endpoints for Twilio voice integration. Follow these steps to set up:
+        This app provides an API endpoint for Twilio voice integration. Follow these steps to set up:
         
         1. Log into your Twilio account
         2. Go to Phone Numbers → Manage → Active numbers
         3. Select your number (+19704064410)
-        4. For "A Call Comes In", set "Webhook" to:
-           - URL: `https://huggingface.co/spaces/iajitpanday/vBot-1.5/api/twilio/call`
-           - Method: HTTP POST
+        4. For "A Call Comes In", create a TwiML bin with:
+           ```xml
+           <?xml version="1.0" encoding="UTF-8"?>
+           <Response>
+               <Say>Hello! Thank you for calling. How can I help you today?</Say>
+               <Gather input="speech" action="https://huggingface.co/spaces/iajitpanday/vBot-1.5/api/predict" method="POST" speechTimeout="auto" speechModel="phone_call"/>
+               <Say>I didn't hear anything. Please call back when you're ready.</Say>
+           </Response>
+           ```
         
         The system will:
-        - Answer incoming calls
-        - Process speech input
+        - Process speech input through the /api/predict endpoint
         - Generate responses using your knowledge base
         - Handle follow-up questions
         """)
 
-# API endpoints for Twilio
-def api_twilio_call():
-    """API endpoint for call handling"""
-    return twilio_call_handler()
-
-def api_twilio_speech(SpeechResult=""):
-    """API endpoint for speech processing"""
-    return twilio_speech_handler(SpeechResult)
-
-def api_twilio_followup(SpeechResult=""):
-    """API endpoint for follow-up handling"""
-    return twilio_followup_handler(SpeechResult)
+# Define API prediction function for Twilio
+def predict_api(data):
+    """API endpoint for Twilio integration"""
+    # Check if this is a speech result from Twilio
+    if isinstance(data, dict) and "SpeechResult" in data:
+        return twilio_speech_handler(data)
+    
+    # If it's a call initial request or other Twilio request
+    # Just return the call handler response
+    return twilio_call_handler(data)
 
-# Launch the interface
+# Launch the interface with API
 demo.queue()
 demo.launch()