Create app_2.py

app_2.py

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+import os
+import streamlit as st
+from dotenv import load_dotenv
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+import fitz  # PyMuPDF
+
+load_dotenv()
+
+api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+# Define the repository ID and task
+repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+task = "text-generation"
+
+# App config
+st.set_page_config(page_title="Yatra Sevak.AI", page_icon="🌍")
+st.title("Yatra Sevak.AI ✈️")
+
+# Define the template outside the function
+template = """
+You are a travel assistant chatbot your name is Yatra Sevak.AI designed to help users plan their trips and provide travel-related information. Here are some scenarios you should be able to handle:
+1. Destination Information: Provide information about popular travel destinations. Offer insights on attractions, local cuisine, cultural highlights, weather conditions, and best times to visit.
+2. Travel Tips: Offer practical travel tips and advice. Topics may include packing essentials, visa requirements, currency exchange, local customs, and safety tips.
+3. Local Attractions: Suggest local attractions and points of interest based on the user's destination. Highlight must-see landmarks, museums, parks, and recreational activities.
+4. Customer Service: Address customer service inquiries and provide assistance with travel-related issues.
+5. Online Marketing & Sales Associate: Act as an Online Marketing & Sales Associate and try to suggest our services to the user. If you think that the user can be converted to a client, then ask for their contact information and also give them our contact information.
+Please ensure responses are informative, accurate, and tailored to the user's queries and preferences. Use natural language to engage users and provide a seamless experience throughout their travel planning journey.
+Chat history:
+{chat_history}
+User question:
+{user_question}
+"""
+
+pdf_path = "YatraAI_Travel_Plans.pdf"
+
+def extract_text_from_pdf(pdf_path):
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+pdf_text = extract_text_from_pdf(pdf_path)
+
+# Function to get a response from the model
+def get_response(user_query, chat_history):
+    # Initialize the Hugging Face Endpoint
+    llm = HuggingFaceEndpoint(
+        huggingfacehub_api_token=api_token,
+        repo_id=repo_id,
+        task=task
+    )
+
+    # Include the PDF content in the prompt
+    prompt_with_pdf = f"{template}\n\nCompany Info and Travel Plans:\n{pdf_text}"
+
+    chain = ChatPromptTemplate.from_template(prompt_with_pdf) | llm | StrOutputParser()
+
+    response = chain.invoke({
+        "chat_history": chat_history,
+        "user_question": user_query,
+    })
+
+    return response
+
+# Initialize session state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = [
+        AIMessage(content="Hello, I am Yatra Sevak.AI. How can I help you?"),
+    ]
+
+# Display chat history
+for message in st.session_state.chat_history:
+    if isinstance(message, AIMessage):
+        with st.chat_message("AI"):
+            st.write(message.content)
+    elif isinstance(message, HumanMessage):
+        with st.chat_message("Human"):
+            st.write(message.content)
+
+# User input
+user_query = st.chat_input("Type your message here...")
+if user_query is not None and user_query != "":
+    st.session_state.chat_history.append(HumanMessage(content=user_query))
+
+    with st.chat_message("Human"):
+        st.markdown(user_query)
+
+    response = get_response(user_query, st.session_state.chat_history)
+
+    # Remove any unwanted prefixes from the response
+    response = response.replace("AI response:", "").replace("chat response:", "").replace("bot response:", "").strip()
+
+    with st.chat_message("AI"):
+        st.write(response)
+
+    st.session_state.chat_history.append(AIMessage(content=response))