updated using langchain and openai

app.py

@@ -5,6 +5,13 @@ from bs4 import BeautifulSoup
 import PyPDF2
 import docx
 import time
+from langchain import OpenAI, ConversationChain, PromptTemplate
+from dotenv import load_dotenv
+import os
+
+load_dotenv()  # Load environment variables from .env file
+openai_api_key = os.getenv("openai_api_key")
+llm = OpenAI(openai_api_key=openai_api_key)
 
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 sentiment_analyzer = pipeline("sentiment-analysis")
@@ -66,6 +73,24 @@ def analyze_text(input_text, input_type, tasks, progress=gr.Progress()):
     
     return original_text, summary, sentiment, ", ".join(topics)
 
+def chat(input_text, chat_history):
+    prompt_template = """
+    Assistant is an AI language model that helps with text analysis tasks.
+
+    {chat_history}
+    Human: {input_text}
+    Assistant:"""
+
+    prompt = PromptTemplate(
+        input_variables=["chat_history", "input_text"], 
+        template=prompt_template
+    )
+    
+    chain = ConversationChain(llm=llm, prompt=prompt)
+    response = chain.predict(input_text=input_text)
+    
+    return response
+
 def create_interface():
     with gr.Blocks(title="Text Analysis App") as interface:
         input_type = gr.Dropdown(["Text", "URL", "File"], label="Input Type")
@@ -86,6 +111,11 @@ def create_interface():
             sentiment_output = gr.Textbox(label="Sentiment")
         with gr.Tab("Topics"):
             topics_output = gr.Textbox(label="Topics")
+        with gr.Tab("Conversation"):
+            conversation_history = gr.State([])
+            conversation_input = gr.Textbox(label="Human")
+            conversation_output = gr.Textbox(label="Assistant")
+            conversation_button = gr.Button("Send")
 
         def update_input_visibility(input_type):
             text_input.visible = input_type == "Text"
@@ -116,6 +146,18 @@ def create_interface():
             outputs=[original_text_output, summary_output, sentiment_output, topics_output]
         )
 
+        def process_conversation(conversation_history, conversation_input):
+            conversation_history.append(f"Human: {conversation_input}")
+            response = chat(conversation_input, "\n".join(conversation_history))
+            conversation_history.append(f"Assistant: {response}")
+            return conversation_history, "", response
+
+        conversation_button.click(
+            fn=process_conversation,
+            inputs=[conversation_history, conversation_input],
+            outputs=[conversation_history, conversation_input, conversation_output]
+        )
+
     return interface
 
 if __name__ == "__main__":

requirements.txt

@@ -4,4 +4,7 @@ torch
 requests
 beautifulsoup4
 PyPDF2
-python-docx
+python-docx
+langchain
+openai
+python-dotenv