chat history added

app.py

@@ -7,7 +7,6 @@ import soundfile as sf
 from pdfminer.high_level import extract_text
 from llama_cpp import Llama
 
-
 # Check if MPS is available and set the device
 if torch.backends.mps.is_available():
     device = torch.device("mps")
@@ -26,7 +25,12 @@ def toText(audio):
     return question
 
 
+# Global variable to store chat history
+chat_history = []
+
 def extract_answer(question, text):
+    global chat_history
+    
     # Load the LLaMA model
     model_path="/Users/chandima/.cache/lm-studio/models/lmstudio-community/Llama-3.2-3B-Instruct-GGUF/Llama-3.2-3B-Instruct-Q3_K_L.gguf"
     # Load the LLaMA model with MPS acceleration
@@ -40,21 +44,28 @@ def extract_answer(question, text):
         use_mmap=True,  # Optional: for faster loading
     )
 
+    # Construct the conversation history
+    conversation = "\n".join([f"Human: {q}\nAI: {a}" for q, a in chat_history])
+
     # Use LLaMA to extract skills
     prompt = f"""
-    Answer the question based on the Resume.
+    You are an AI assistant answering questions based on a resume. Here's the conversation so far:
+
+    {conversation}
 
-    Question:
-    {question}:
+    Human: {question}
 
     Resume:
     {text}
 
-    Answer:
-    """
+    AI: """
     
     response = llm(prompt, max_tokens=800, stop=["Human:", "\n\n"])
     answer = response['choices'][0]['text'].strip()
+    
+    # Append the new question and answer to the chat history
+    chat_history.append((question, answer))
+    
     print(answer)
     return answer
 
@@ -79,6 +90,11 @@ def clone(audio, file):
 def start_recording():
     return None
 
+def reset_conversation():
+    global chat_history
+    chat_history = []
+    return None
+
 with gr.Blocks() as iface:
     with gr.Row():
         audio_input = gr.Audio(sources="microphone", type="filepath", label='Question from Resume')
@@ -96,4 +112,8 @@ with gr.Blocks() as iface:
     # Add event to start recording after output audio finishes
     output.play(fn=start_recording, outputs=audio_input)
 
+    # Add a button to reset the conversation
+    reset_btn = gr.Button("Reset Conversation")
+    reset_btn.click(fn=reset_conversation, inputs=None, outputs=None)
+
 iface.launch()