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app.py

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+# -*- coding: utf-8 -*-
+"""chatbot.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1zgihAeNpcDd0opNPsbnmS2UNgz0rsfOB
+"""
+
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+
+import logging
+import sys
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
+from llama_index.llms import HuggingFaceLLM
+from langchain.document_loaders import PyPDFLoader
+
+documents = SimpleDirectoryReader("Dat").load_data()
+
+from llama_index.prompts.prompts import SimpleInputPrompt
+
+
+system_prompt =  """Emphasize empathy and active listening: Create a safe space for users to share their thoughts and feelings without judgment.
+Focus on understanding and validation: Reflect back user emotions and experiences to demonstrate understanding and build trust.
+Offer evidence-based support: Provide grounding techniques, coping strategies, and psychoeducation based on sound mental health principles.
+Personalize responses: Tailor interactions to individual needs, preferences, and goals.
+Maintain ethical boundaries: Respect user privacy, confidentiality, and autonomy.
+Recognize limitations: Acknowledge that the chatbot is not a replacement for professional therapy and encourage seeking licensed help when needed.
+Key goals:
+
+Reduce symptoms of anxiety, depression, and stress.
+Improve emotional regulation and coping skills.
+Enhance self-awareness and self-compassion.
+Promote healthy relationships and communication.
+Build resilience and problem-solving skills.
+Encourage positive self-care and lifestyle choices.
+Specific prompts:
+
+"Greet the user warmly and introduce yourself as their AI therapist."
+"Ask open-ended questions to elicit user thoughts, feelings, and concerns."
+"Respond empathetically to user disclosures, validating their experiences."
+"Offer appropriate mental health resources, exercises, or techniques based on user needs."
+"Guide users through mindfulness exercises or relaxation techniques."
+"Challenge negative thinking patterns and encourage cognitive reframing."
+"Help users set realistic goals and track progress towards mental wellness."
+"Provide psychoeducation on various mental health topics and treatment options."
+"Conclude sessions with positive affirmations and encouragement."
+"Remind users of the chatbot's limitations and the importance of seeking professional help."
+"Always prioritize user safety and offer crisis resources in case of urgent needs."
+Additional considerations:
+
+Tailor prompts to specific mental health conditions or challenges.
+Incorporate humor or lightheartedness when appropriate to build rapport.
+Provide options for different communication styles (e.g., text, voice, interactive activities).
+Continuously monitor and refine prompts based on user feedback and clinical expertise."""
+
+
+
+# This will wrap the default prompts that are internal to llama-index
+query_wrapper_prompt = SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
+
+import torch
+
+llm = HuggingFaceLLM(
+    context_window=4096,
+    max_new_tokens=256,
+    generate_kwargs={"temperature": 0.5, "do_sample": False},
+    system_prompt=system_prompt,
+    query_wrapper_prompt=query_wrapper_prompt,
+    tokenizer_name="NousResearch/Llama-2-7b-chat-hf",
+    model_name="NousResearch/Llama-2-7b-chat-hf",
+    device_map="auto",
+    # uncomment this if using CUDA to reduce memory usage
+    model_kwargs={"torch_dtype": torch.float16 })
+
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from llama_index.embeddings.langchain import LangchainEmbedding
+from llama_index import ServiceContext
+
+embed_model = LangchainEmbedding(
+  HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+)
+
+service_context = ServiceContext.from_defaults(
+    chunk_size=1024,
+    llm=llm,
+    embed_model=embed_model
+)
+
+index = VectorStoreIndex.from_documents(documents, service_context=service_context)
+
+query_engine = index.as_query_engine()
+response = query_engine.query("What is cyclothymic disorder?")
+
+print(response)
+
+import gradio as gr
+
+def respond_to_user(input_text):
+    response = query_engine.query(input_text)
+    return response
+
+
+import gradio as gr
+
+def render_messages():
+    messages = []
+    if hasattr(iface, "_message_history"):
+        for msg in iface._message_history[-3::-1]:
+            messages.append({"role": msg["role"], "content": msg["content"]})
+        
+    template = r"""
+    <div class='card'>
+        %for message in messages:
+            {%if message['role'] == 'assistant':%}
+                <div class='card bg-light mb-3' style='max-width: 50rem;'>
+                    <div class='card-header'>Assistant</div>
+                    <div class='card-body'>{{message['content']}}</div>
+                </div>
+            {%else:%}
+                <div class='card' style='max-width: 50rem;'>
+                    <div class='card-header'>User</div>
+                    <div class='card-body'>{{message['content']}}</div>
+                </div>
+            {%endif%}
+        %endfor%}
+    </div>
+    """
+    return gr.outputs.HTML(template)
+
+def process_inputs(input_text):
+    result = respond_to_user(input_text)
+    iface._message_history.append({"role": "assistant", "content": result})
+    return "", render_messages()
+
+iface = gr.Interface(fn=process_inputs,
+                     inputs=gr.components.Textbox(lines=3, placeholder="Type something..."),
+                     outputs=[],
+                     title="AI Therapist Chatbot",
+                     allow_flagging="never",
+                     theme="monochrome",
+                     css="""
+                       .gradio-container > div:first-child{
+                           margin-top: 0 !important;
+                       }
+                       .gradio-label-container label{
+                           font-weight: bold;
+                           color: black;
+                       }
+                   """,
+                     article="""
+                       Welcome to our AI Therapist chatbot! Feel free to ask anything related to mental health and receive guidance. Confidentiality and privacy notice: Your conversation remains private; we do not store any data.
+                     """)
+
+iface.render()

requirements.txt

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+pypdf
+python-dotenv
+transformers 
+einops 
+accelerate 
+langchain 
+bitsandbytes
+sentence_transformers
+llama-index
+langchain-community