Update app.py

app.py

@@ -7,7 +7,7 @@ import PyPDF2
 
 # Load local models for inference
 stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-base")
-conversation_model = pipeline("conversational", model="facebook/blenderbot-400M-distill")
+conversation_model = pipeline("text-generation", model="facebook/blenderbot-400M-distill")
 
 # Load a pre-trained model for vector embeddings
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -28,6 +28,12 @@ def process_resume(pdf):
     }
     return resume_embeddings
 
+# Generate a conversation response
+def generate_conversation_response(user_input):
+    prompt = f"The user said: {user_input}. Respond appropriately as a recruiter."
+    response = conversation_model(prompt, max_length=100, num_return_sequences=1)
+    return response[0]["generated_text"]
+
 # Generate question from user response
 def generate_question(user_input, resume_embeddings):
     """Find the most relevant section in the resume and generate a question."""