chat history

app.py

@@ -63,6 +63,7 @@ with col2:
 # Clear chat history
 if clear_button:
     st.session_state.messages = []
+    chatbot.clear_conv_history()
     st.rerun()
 
 # Handle user input

query_chat.py

@@ -2,15 +2,18 @@ import google.generativeai as genai
 import os
 
 class GeminiQanA:
-    def __init__(self,text1:str='',text2:str=''):
-        """Initializes the Gemini question answerer by loading the model."""
+    def __init__(self, text1: str = '', text2: str = ''):
+        """Initializes the Gemini question-answering model with brochures and conversation history."""
         self.api_key = os.getenv("GOOGLE_API_KEY")
         genai.configure(api_key=self.api_key)
-        self.model = self._load_model(text1,text2)
+        self.text1 = text1
+        self.text2 = text2
+        self.conversation_history = []  # Store previous exchanges
+        self.model = self._load_model()
 
-    def _load_model(self,text1:str,text2:str):
-        """Loads the generative AI model with a system instruction."""
-        final_prompt = f'''Role:
+    def _load_model(self):
+        """Loads the generative AI model without the conversation history (history will be passed dynamically)."""
+        system_instruction = f'''Role:
 You are a sales agent responsible for assisting customers by answering questions about our team’s capabilities and the projects we offer. You have access to two brochures that detail the available projects and their features. Your goal is to provide accurate and honest responses based solely on the information within these brochures.
 
 Guidelines for Responses:
@@ -31,21 +34,6 @@ Guidelines for Responses:
 -Do not create new information or assume additional capabilities.
 -Do not make guarantees beyond what is stated in the brochures.
 -Do not offer speculative solutions that are not explicitly supported by the documents.
-Example Interactions:
-Scenario 1: Customer Asks About a Team Capability
-✅ Customer: "Does your team specialize in AI-powered automation?"
-✅ Agent: "According to our brochure, our team specializes in [list relevant capabilities]. While AI-powered automation is not specifically mentioned, we do offer [related project] which may align with your needs."
-
-Scenario 2: Customer Has a Specific Problem
-✅ Customer: "I need a system to manage logistics for my e-commerce business. Do you have a solution?"
-✅ Agent: "Yes, we offer [Project Name], which is designed for logistics management. It provides [brief relevant details from the brochure]. Would you like more information on its features?"
-
-Scenario 3: No Matching Solution Available
-✅ Customer: "Do you have a tool for automating customer sentiment analysis?"
-✅ Agent: "I'm sorry, but our current projects do not include a tool specifically for sentiment analysis. However, we do have [Project Name], which provides [related functionality]. Would that be of interest?"
-
-✅ Customer: "I need a blockchain-based security system. Can your team provide one?"
-✅ Agent: "Unfortunately, we do not have a blockchain-based security system in our current offerings. I'm happy to help with any other inquiries regarding our available solutions."
 
 Tone & Style:
 Maintain a professional, helpful, and customer-focused tone.
@@ -54,20 +42,38 @@ If a solution exists, explain how it meets the customer's needs without oversell
 If no solution exists, remain polite and transparent.
 
 First Brochure:
-{text1}
+{self.text1}
 
 Second Brochure:
-{text2}
-                    '''
+{self.text2}
+'''
+        return genai.GenerativeModel("gemini-1.5-pro", system_instruction=system_instruction)
+
+    def answer_question(self, question: str) -> str:
+        """Generates a response by including conversation history in the prompt."""
+        
+        # Format conversation history as a chat transcript
+        history_text = "\n".join([f"Customer: {q}\nAgent: {a}" for q, a in self.conversation_history])
         
-        return genai.GenerativeModel("gemini-1.5-pro", system_instruction=final_prompt)
+        # Create a dynamic prompt with history + current question
+        dynamic_prompt = f"""{history_text}
+                            Customer: {question}
+                            Agent:"""
+        
+        response = self.model.generate_content(dynamic_prompt)
+        answer = response.text.strip()
+
+        # Save this exchange in the history
+        self.conversation_history.append((question, answer))
+
+        return answer
     
-    def answer_question(self, question: str) -> str:
-        """Performs the API call to Gemini and returns the sentiment analysis response."""
-        response = self.model.generate_content(question)
-        return response.text
+    def clear_conv_history(self) -> None:
+        self.conversation_history.clear()
 
 if __name__ == "__main__":
-    analyzer = GeminiQanA()
-    response = analyzer.answer_question("Hello,how are you?")
-    print(response)
+    analyzer = GeminiQanA("Example text from first brochure", "Example text from second brochure")
+    
+    # Example conversation
+    print(analyzer.answer_question("What AI solutions do you offer?"))
+    print(analyzer.answer_question("Do you have a project for logistics automation?"))