Create app.py

app.py

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+from langchain_community.chat_message_histories import StreamlitChatMessageHistory
+import streamlit as st
+from langchain.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
+from more_itertools import chunked
+
+from langserve import RemoteRunnable
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import os
+from langchain import PromptTemplate
+from langchain import LLMChain
+from langchain_together import Together
+import re
+import pdfplumber
+# Set the API key with double quotes
+
+os.environ['TOGETHER_API_KEY'] = "5653bbfbaf1f7c1438206f18e5dfc2f5992b8f0b6aa9796b0131ea454648ccde"
+
+text = ""
+max_pages = 16
+with pdfplumber.open("/content/AI Engineer Test.pdf") as pdf:
+        for i, page in enumerate(pdf.pages):
+            if i >= max_pages:
+                break
+            text += page.extract_text() + "\n"
+
+def Bot(Questions):
+    chat_template = """
+    Based on the provided context: {text}
+    Please answer the following question: {Questions}
+    Only provide answers that are directly related to the context. If the question is unrelated, respond with "I don't know".
+    """
+    prompt = PromptTemplate(
+        input_variables=['text', 'Questions'],
+        template=chat_template
+    )
+    llama3 = Together(model="meta-llama/Llama-3-70b-chat-hf", max_tokens=250)
+    Generated_chat = LLMChain(llm=llama3, prompt=prompt)
+
+    try:
+        response = Generated_chat.invoke({
+            "text": text,
+            "Questions": Questions
+        })
+
+        response_text = response['text']
+
+        response_text = response_text.replace("assistant", "")
+
+        # Post-processing to handle repeated words and ensure completeness
+        words = response_text.split()
+        seen = set()
+        filtered_words = [word for word in words if word.lower() not in seen and not seen.add(word.lower())]
+        response_text = ' '.join(filtered_words)
+        response_text = response_text.strip()  # Ensuring no extra spaces at the ends
+        if not response_text.endswith('.'):
+            response_text += '.'
+
+        return response_text
+    except Exception as e:
+        return f"Error in generating response: {e}"
+
+def ChatBot(Questions):
+  greetings = ["hi", "hello", "hey", "greetings", "what's up", "howdy"]
+    # Check if the input question is a greeting
+  question_lower = Questions.lower().strip()
+  if question_lower in greetings or any(question_lower.startswith(greeting) for greeting in greetings):
+        return "Hello! How can I assist you with the document today?"
+  else:
+    response=Bot(Questions)
+    return response.translate(str.maketrans('', '', '\n'))
+
+    # --- Logo ---
+st.set_page_config(
+    page_title="AI Engineer Test Chatbot",
+    page_icon="/content/Insight Therapy Solutions.png",
+    layout="wide",
+)
+st.sidebar.image("/content/Insight Therapy Solutions.png", width=200)
+
+st.sidebar.title("Navigation")
+st.sidebar.write("Reclaim Your Mental Health")
+st.sidebar.markdown("[Visit us at](https://www.insighttherapysolutions.com/)")
+
+rag_chain = RemoteRunnable("http://69.61.24.171:8000/rag_chain/")
+
+
+
+msgs = StreamlitChatMessageHistory(key="langchain_messages")
+
+# --- Main Content ---
+st.markdown("## 🔍 Chatbot For AI Engineer test:")
+
+if len(msgs.messages) == 0:
+    msgs.add_ai_message("Hi! How can I assist you today?")
+
+
+for msg in msgs.messages:
+    st.chat_message(msg.type).write(msg.content)
+
+if prompt := st.chat_input():
+    st.chat_message("human").write(prompt)
+
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+
+        try:
+            _chat_history = st.session_state.langchain_messages[1:40]
+            _chat_history_tranform = list(
+                chunked([msg.content for msg in _chat_history], n=2)
+            )
+
+            response = rag_chain.stream(
+                {"question": prompt, "chat_history": _chat_history_tranform}
+            )
+
+            for res in response:
+                full_response += res or ""
+                message_placeholder.markdown(full_response + "|")
+                message_placeholder.markdown(full_response)
+
+            msgs.add_user_message(prompt)
+            msgs.add_ai_message(full_response)
+
+        except Exception as e:
+            st.error(f"An error occured. {e}")