Delete app.py

app.py

@@ -1,272 +0,0 @@
-import os
-import streamlit as st
-from pydub import AudioSegment
-import numpy as np
-from transformers import pipeline
-from langchain_huggingface import HuggingFaceEndpoint
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import StrOutputParser
- 
-# Model IDs
-model_id = "meta-llama/Llama-3.2-1B-Instruct"
-model2_id = "meta-llama/Llama-3.2-1B-Instruct"
-whisper_model = "openai/whisper-small"  # Using Whisper model for audio transcription
- 
-def get_llm_hf_inference(model_id, max_new_tokens=128, temperature=0.1):
-    """Returns a language model for HuggingFace inference."""
-    try:
-        llm = HuggingFaceEndpoint(
-            repo_id=model_id,
-            max_new_tokens=max_new_tokens,
-            temperature=temperature,
-            token=os.getenv("HF_TOKEN")
-        )
-        return llm
-    except Exception as e:
-        st.error(f"Error initializing model: {e}")
-        return None
- 
-# Initialize Whisper transcription model
-def load_transcription_model():
-    try:
-        transcriber = pipeline("automatic-speech-recognition", model=whisper_model)
-        return transcriber
-    except Exception as e:
-        st.error(f"Error loading Whisper model: {e}")
-        return None
- 
-# Preprocess audio to 16kHz mono
-def preprocess_audio(file):
-    audio = AudioSegment.from_file(file).set_frame_rate(16000).set_channels(1)
-    audio_samples = np.array(audio.get_array_of_samples()).astype(np.float32) / (2**15)
-    return audio_samples
- 
-# Function to transcribe audio with preprocessing
-def transcribe_audio(file, transcriber):
-    audio = preprocess_audio(file)
-    transcription = transcriber(audio)["text"]
-    return transcription
- 
-# Configure the Streamlit app
-st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗")
- 
-# Initialize session state
-if "avatars" not in st.session_state:
-    st.session_state.avatars = {'user': None, 'assistant': None}
- 
-if 'user_text' not in st.session_state:
-    st.session_state.user_text = None
- 
-if "max_response_length" not in st.session_state:
-    st.session_state.max_response_length = 1000
- 
-if "system_message" not in st.session_state:
-    st.session_state.system_message = "friendly AI conversing with a human user"
- 
-if "starter_message" not in st.session_state:
-    st.session_state.starter_message = "Hello, there! How can I help you today?"
- 
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]
- 
-# Homepage content
-def display_homepage():
-    st.markdown(
-        """
-    <div style="text-align: center; margin-top: 50px; font-size: 60px; font-weight: bold; color: #2c3e50; margin-left: auto; margin-right: auto;">
-        Your personal AI Therapist
-    </div>
-        """, unsafe_allow_html=True
-    )
- 
-    st.markdown(
-        """
-        <div style="text-align: center; font-size: 20px; color: #000000; margin-top: 20px; max-width: 700px; margin-left: auto; margin-right: auto;">
-            This is your healthcare chatbot that streamlines outpatient care, solves routine queries 24/7, and effortlessly automates appointment bookings, prescriptions, and reports. Let AI help you with your mental health journey.
-        </div>
-        """, unsafe_allow_html=True
-    )
- 
-    st.markdown(
-        """
-        <style>
-        .stApp {
-            background-image: url('https://images.pexels.com/photos/2680270/pexels-photo-2680270.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1');
-            background-size: cover;
-            background-position: center;
-            background-repeat: no-repeat;
-            height: 100vh;
-            color: white;
-        }
- 
-        .stButton>button {
-            background-color: #2980b9;
-            color: white;
-            font-size: 18px;
-            font-weight: bold;
-            padding: 15px 30px;
-            border-radius: 8px;
-            border: none;
-            box-shadow: 0px 5px 15px rgba(0, 0, 0, 0.1);
-            cursor: pointer;
-            transition: background-color 0.3s ease;
-        }
- 
-        .stButton>button:hover {
-            background-color: #3498db;
-        }
-        </style>
-        """, unsafe_allow_html=True
-    )
- 
-    if st.button("Start Chat", key="start_chat_button"):
-        st.session_state.chat_started = True
-        # No need for rerun; session state change will trigger re-rendering
- 
-# Chatbot page content
-def display_chatbot():
-    st.title("Personal Psychologist Chatbot")
-    st.markdown(f"*This is a simple chatbot that acts as a psychologist and gives solutions to your psychological problems. It uses the {model_id}.*")
- 
-    # Sidebar for settings
-    with st.sidebar:
-        # Reset Chat History
-        reset_history = st.button("Reset Chat History")
-        go_home = st.button("Back to Home")
-        if go_home:
-            st.session_state.chat_started = False
-            st.experimental_rerun()  # This will reload the app to show the homepage
- 
-    # Initialize or reset chat history
-    if reset_history:
-        st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]
- 
-    def get_response(system_message, chat_history, user_text, model_id, max_new_tokens=256):
-        """Generates a response from the chatbot model."""
-        hf = get_llm_hf_inference(model_id=model_id, max_new_tokens=max_new_tokens)
-        if hf is None:
-            return "Error: Model not initialized.", chat_history
- 
-        # Create the prompt template
-        prompt = PromptTemplate.from_template(
-            (
-                "[INST] {system_message}"
-                "\nCurrent Conversation:\n{chat_history}\n\n"
-                "\nUser: {user_text}.\n [/INST]"
-                "\nAI:"
-            )
-        )
-        chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
- 
-        # Generate the response
-        response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
-        response = response.split("AI:")[-1].strip()
- 
-        # Enhanced end-of-conversation detection:
-        # Check if response contains low engagement or end-of-conversation patterns
-        low_engagement_threshold = 3  # Threshold for short responses
-        end_keywords = ["thank you", "thanks", "goodbye", "bye", "that's all", "done"]
-        
-        # Check for short responses over multiple turns
-        short_responses = len(user_text.split()) <= low_engagement_threshold
-        end_pattern_match = any(keyword in user_text.lower() for keyword in end_keywords)
-        
-        # Recent short responses pattern
-        recent_short_responses = all(len(msg["content"].split()) <= low_engagement_threshold for msg in chat_history[-2:])
-        response_is_acknowledgment = user_text.lower() in ["yes", "okay", "alright"]
-
-        # Trigger health report prompt based on combination of patterns
-        if (end_pattern_match or (short_responses and recent_short_responses)) and not response_is_acknowledgment:
-            follow_up_question = "Would you like to have a report of your current health? Yes/No"
-            response += f"\n\n{follow_up_question}"
-
-        # Update the chat history
-        chat_history.append({'role': 'user', 'content': user_text})
-        chat_history.append({'role': 'assistant', 'content': response})
-        return response, chat_history
- 
-    def get_summary_of_chat_history(chat_history, model2_id):
-        """Generates a comprehensive summary of the chat history and a health report."""
-        hf = get_llm_hf_inference(model_id=model2_id, max_new_tokens=256)
-        if hf is None:
-            return "Error: Model not initialized."
-
-        # Format the chat content
-        chat_content = "\n".join([f"{message['role']}: {message['content']}" for message in chat_history])
-
-        # Improved summary prompt
-        prompt = PromptTemplate.from_template(
-            (
-                "Please analyze the following conversation to determine the user's emotional state and well-being."
-                "\nConsider the user's tone, engagement, and specific phrases they used. Based on the overall mood, "
-                "patterns of responses, and any indicators of stress, anxiety, positivity, or concern, generate a detailed "
-                "health summary that reflects the user's mental and emotional condition."
-                "\nAdditionally, provide recommendations or reassurance if needed.\n\nConversation:\n{chat_content}"
-            )
-        )
-
-        summary = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
-        summary_response = summary.invoke(input={"chat_content": chat_content})
-
-        return summary_response
-
-
- 
-    # Load Whisper model for transcription
-    transcriber = load_transcription_model()
- 
-    # User input for audio and text
-    st.markdown("### Choose your input:")
-    audio_file = st.file_uploader("Upload an audio file for transcription", type=["mp3", "wav", "m4a"])
-    st.session_state.user_text = st.chat_input(placeholder="Or enter your text here.")
- 
-    # Check if audio file is uploaded and transcribe if available
-    if audio_file is not None and transcriber:
-        with st.spinner("Transcribing audio..."):
-            try:
-                st.session_state.user_text = transcribe_audio(audio_file, transcriber)
-                st.success("Audio transcribed successfully!")
-            except Exception as e:
-                st.error(f"Error transcribing audio: {e}")
- 
-    # Chat interface
-    output_container = st.container()
- 
-    # Display chat messages
-    with output_container:
-        for message in st.session_state.chat_history:
-            if message['role'] == 'system':
-                continue
-            with st.chat_message(message['role'], avatar=st.session_state['avatars'][message['role']]):
-                st.markdown(message['content'])
- 
-    # Process text input for chatbot response
-    if st.session_state.user_text:
-        with st.chat_message("user", avatar=st.session_state.avatars['user']):
-            st.markdown(st.session_state.user_text)
- 
-        with st.chat_message("assistant", avatar=st.session_state.avatars['assistant']):
-            with st.spinner("Addressing your concerns..."):
-                response, st.session_state.chat_history = get_response(
-                    system_message=st.session_state.system_message,
-                    user_text=st.session_state.user_text,
-                    chat_history=st.session_state.chat_history,
-                    model_id=model_id,
-                    max_new_tokens=st.session_state.max_response_length,
-                )
-                st.markdown(response)
- 
-                # Check if the user has agreed to the report
-                if "yes" in st.session_state.user_text.lower() and "Would you like to have a report of your current health?" in response:
-                    with st.spinner("Generating your health report..."):
-                        report = get_summary_of_chat_history(st.session_state.chat_history, model2_id)
-                        st.markdown(report)
- 
-# Main logic to switch between homepage and chatbot
-if 'chat_started' not in st.session_state or not st.session_state.chat_started:
-    display_homepage()
-else:
-    display_chatbot()
- 
-# Adjust the layout to reduce white space
-st.markdown("<style>div.stContainer {padding-top: 0;}</style>", unsafe_allow_html=True)