Update app.py

app.py

@@ -1,12 +1,13 @@
 import os
 from dotenv import load_dotenv
 import gradio as gr
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from sentence_transformers import SentenceTransformer
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
+
 load_dotenv()
+
 # Configure the Llama index settings
 Settings.llm = HuggingFaceInferenceAPI(
     model_name="google/gemma-1.1-7b-it",
@@ -41,7 +42,7 @@ def handle_query(query):
             "user",
             """
             You are a RedfernsTech chatbot whose aim is to provide better service to the user, utilizing provided context to deliver answers.
-            and collect the some basic inforation first also name ,email ,company name
+            and collect the some basic information first also name, email, company name
             {context_str}
             Question:
             {query_str}
@@ -58,26 +59,29 @@ def handle_query(query):
     answer = query_engine.query(query)
 
     if hasattr(answer, 'response'):
-        return answer.response
+        response = answer.response
     elif isinstance(answer, dict) and 'response' in answer:
-        return answer['response']
+        response = answer['response']
     else:
-        return "Sorry, I couldn't find an answer."
+        response = "Sorry, I couldn't find an answer."
+
+    # Append the query and response to chat history
+    chat_history.append((query, response))
 
-# Example usage
+    return response
 
-    # Process PDF ingestion from directory
+# Initialize chat history
+chat_history = []
+
+# Example usage: Process PDF ingestion from directory
 print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
 data_ingestion_from_directory()
 
-    # Example query
+# Example query
 query = "How do I use the RedfernsTech Q&A assistant?"
 print("Query:", query)
 response = handle_query(query)
 print("Answer:", response)
-# prompt: create a gradio chatbot for this
-
-
 
 # Define the input and output components for the Gradio interface
 input_component = gr.Textbox(
@@ -87,13 +91,19 @@ input_component = gr.Textbox(
 
 output_component = gr.Textbox()
 
+# Function to add chat history to output
+def chat_with_history(query):
+    response = handle_query(query)
+    history_str = "\n\n".join([f"Query:\n{q}\nAnswer:\n{a}" for q, a in chat_history])
+    return f"{response}\n\nChat History:\n\n{history_str}"
+
 # Create the Gradio interface
 interface = gr.Interface(
-    fn=handle_query,
+    fn=chat_with_history,
     inputs=input_component,
     outputs=output_component,
     title="RedfernsTech Q&A Chatbot",
-    description="Ask me anything about the uploaded document."
+    description="Ask me anything about the uploaded document. View chat history below."
 )
 
 # Launch the Gradio interface