app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util
import openai
import os
#trying so when user puts la or los angles specific pic comes:

# URL or path to your image file
#PICTURE_URL = "Stars/sf.png"

#def respond(user_input):
   # if "los angeles" in user_input.lower() or "la" in user_input.lower():
        #return f"Here's a picture of Los Angeles!", PICTURE_URL
  #  else:
       # return "How can I help you with astronomy?", None

# Define the Gradio interface
#iface = gr.Interface(
  #  fn=respond, 
   # inputs="text", 
   # outputs=["text", "image"]
#)

# Launch the interface
#iface.launch()

os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Initialize paths and model identifiers for easy configuration and maintenance
filename = "output_topic_details.txt"  # Path to the file storing chess-specific details
retrieval_model_name = 'output/sentence-transformer-finetuned/'

# Define paths to images
path_to_sf_image = "Stars/sf.png"
path_to_sacramento_image = "Stars/sacramento.png"
path_to_la_image = "Stars/la.png"

openai.api_key = os.environ["OPENAI_API_KEY"]



system_message = "You are an astronomy chatbot named Starfinder specialized in providing information on stargazing, astronomical events, and outer space."
# Initial system message to set the behavior of the assistant
messages = [{"role": "system", "content": system_message}]

# Attempt to load the necessary models and provide feedback on success or failure
try:
    retrieval_model = SentenceTransformer(retrieval_model_name)
    print("Models loaded successfully.")
except Exception as e:
    print(f"Failed to load models: {e}")

def load_and_preprocess_text(filename):
    """
    Load and preprocess text from a file, removing empty lines and stripping whitespace.
    """
    try:
        with open(filename, 'r', encoding='utf-8') as file:
            segments = [line.strip() for line in file if line.strip()]
        print("Text loaded and preprocessed successfully.")
        return segments
    except Exception as e:
        print(f"Failed to load or preprocess text: {e}")
        return []

segments = load_and_preprocess_text(filename)

def find_relevant_segment(user_query, segments):
    """
    Find the most relevant text segment for a user's query using cosine similarity among sentence embeddings.
    This version finds the best match based on the content of the query.
    """
    try:
        # Lowercase the query for better matching
        lower_query = user_query.lower()
        
        # Encode the query and the segments
        query_embedding = retrieval_model.encode(lower_query)
        segment_embeddings = retrieval_model.encode(segments)
        
        # Compute cosine similarities between the query and the segments
        similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]
        
        # Find the index of the most similar segment
        best_idx = similarities.argmax()
        
        # Return the most relevant segment
        return segments[best_idx]
    except Exception as e:
        print(f"Error in finding relevant segment: {e}")
        return ""

def generate_response(user_query, relevant_segment):
    """
    Generate a response emphasizing the bot's capability in providing astronomical information.
    """
    try:
        user_message = f"Here's the information on outer space: {relevant_segment}"

        # Append user's message to messages list
        messages.append({"role": "user", "content": user_message})
        
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=messages,
            max_tokens=150,
            temperature=0.2,
            top_p=1,
            frequency_penalty=0,
            presence_penalty=0
        )
        
        # Extract the response text
        output_text = response['choices'][0]['message']['content'].strip()
        
        # Append assistant's message to messages list for context
        messages.append({"role": "assistant", "content": output_text})
        
        return output_text
        
    except Exception as e:
        print(f"Error in generating response: {e}")
        return f"Error in generating response: {e}"

def query_model(question):
    """
    Process a question, find relevant information, and generate a response.
    """
    if question == "":
        return "Welcome to Starfinder! Ask me anything about outer space, stargazing, and upcoming astronomical events.", None
    
    if "san francisco" in question.lower():
        return "There are many locations near San Francisco where you can stargaze: Lick Observatory (Mount Hamilton), Chabot Space & Science Center (Oakland) , Twin Peaks (SF), Sibley Volcanic National Reserve (Oakland), Mount Tamalpais (Marin), San Francisco State University Observatory (SF), Mount Diablo (East Bay)!", "https://huggingface.co/spaces/Starfinders/Stars/resolve/main/sf.png"
    if "sacramento" in question.lower():
        return "There are many locations near Sacramento where you can stargaze: Kalithea Park, Northstar Park, Curtis Park, Lake Theodore, Casa Bella Verde, McKinley Park, Tiscornia Park, Old Sacramento Waterfront.", "https://huggingface.co/spaces/Starfinders/Stars/resolve/main/sacramento.png"
    if "los angeles" in question.lower() or "la" in question.lower():
        return "There are many locations near Los Angeles where you can stargaze: Leo Carrillo State Beach (Malibu), Malibu Creek State Park (Malibu), Griffith Observatory (Griffith Park), Mount Wilson Observatory (Angeles Crest)", "https://huggingface.co/spaces/Starfinders/Stars/resolve/main/la.png"
    
    relevant_segment = find_relevant_segment(question, segments)
    if not relevant_segment:
        return "Could not find specific information. Please refine your question.", None
    response = generate_response(question, relevant_segment)
    return response, None


# Define the welcome message and specific topics the chatbot can provide information about
welcome_message = """
# ✧ Welcome to Starfinder!
## Your AI-driven assistant for all astronomy-related queries. Created by Aarna, Aditi, and Anastasia of the 2024 Kode With Klossy SF Camp. 
"""

topics = """
### Feel Free to ask me anything from the topics below!
- The Night sky
- Outer space insights
- Light pollution
- Stargazing spots
- Celestial events
- Astronomy tips
"""

# Setup the Gradio Blocks interface with custom layout components
with gr.Blocks(theme='earneleh/paris') as demo:
    gr.Markdown(welcome_message)  # Display the formatted welcome message
    with gr.Row():
        with gr.Column():
                gr.Markdown(topics)  # Show the topics on the left side
        with gr.Row():
            with gr.Column():
                question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
                answer = gr.Textbox(label="StarFinder Response", placeholder="StarFinder will respond here...", interactive=False, lines=10)
                image_output = gr.Image(label="Image Output")  # Add an Image component
                submit_button = gr.Button("Submit")
                submit_button.click(fn=query_model, inputs=question, outputs=[answer, image_output])  # Update outputs to include the image component

# Launch the Gradio app to allow user interaction
demo.launch(share=True)