Update app.py

app.py

@@ -30,30 +30,22 @@ ADS.TOKEN = os.getenv('ADS_API_KEY') # Ensure your ADS API key is stored in env
 # Define system message with instructions
 system_message = """
 You are ExosAI, a helpful assistant specializing in Exoplanet and Astrophysics research.
-
 Generate a detailed structured response based on the following science context and user input, including the necessary observables, physical parameters, and technical requirements for observations. The response should include the following sections:
-
  Science Objectives: Describe key scientific study objectives related to the science context and user input.
-
  Physical Parameters: Outline the physical parameters related to the science context and user input.
-
  Observables: Specify the observables related to the science context and user input.
-
  Description of Desired Observations: Detail the types of observations related to the science context and user input.
-
  Technical Requirements Table: Generate a table with the following columns:
- - Requirements: The specific observational requirements (e.g., UV observations, Optical observations or Infrared observations. No radio or radar).
+ - Requirements: The specific observational requirements (e.g., UV observations, Optical observations or Infrared observations).
  - Necessary: The necessary values or parameters (e.g., wavelength ranges, spatial resolution).
  - Desired: The desired values or parameters.
  - Justification: A scientific explanation of why these requirements are important.
  - Comments: Additional notes or remarks regarding each requirement.
-
  Example:
  | Requirements | Necessary | Desired | Justification | Comments |
  |----------------------------------|------------------------------------------|------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|
  | UV Observations | Wavelength: 1200–2100 Å, 2500–3300 Å | Wavelength: 1200–3300 Å | Characterization of atomic and molecular emissions (H, C, O, S, etc.) from fluorescence and dissociative electron impact | Needed for detecting H2O, CO, CO2, and other volatile molecules relevant for volatile delivery studies. |
  | Infrared Observations | Wavelength: 2.5–4.8 μm | Wavelength: 1.5–4.8 μm | Tracks water emissions and CO2 lines in icy bodies and small planetesimals | Also allows detection of 3 μm absorption feature in icy bodies. |
-
  Ensure the response is structured clearly and the technical requirements table follows this format.
 """
 
@@ -62,13 +54,54 @@ def encode_text(text):
  outputs = bi_model(**inputs)
  return outputs.last_hidden_state.mean(dim=1).detach().numpy().flatten()
 
-def retrieve_relevant_context(user_input, context_texts):
+def get_chunks(text, chunk_size=300):
+ """
+ Split a long piece of text into smaller chunks of approximately 'chunk_size' characters.
+ """
+ if not text.strip():
+ raise ValueError("The provided context is empty or blank.")
+ 
+ # Split the text into chunks of approximately 'chunk_size' characters
+ chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+ return chunks
+
+def retrieve_relevant_context(user_input, context_texts, chunk_size=300, similarity_threshold=0.3):
+ """
+ Split the context text into smaller chunks, find the most relevant chunk
+ using cosine similarity, and return the most relevant chunk.
+ If no chunk meets the similarity threshold, return a fallback message.
+ """
+ # Check if the context is empty or just whitespace
+ if not context_texts.strip():
+ return "Error: Context is empty or improperly formatted.", None
+ 
+ # Split the long context text into chunks using the chunking function
+ context_chunks = get_chunks(context_texts, chunk_size)
+ 
+ # Handle single context case
+ if len(context_chunks) == 1:
+ return context_chunks[0], 1.0 # Return the single chunk with perfect similarity
+ 
+ # Encode the user input to create a query embedding
  user_embedding = encode_text(user_input).reshape(1, -1)
- context_embeddings = np.array([encode_text(text) for text in context_texts])
- context_embeddings = context_embeddings.reshape(len(context_embeddings), -1)
- similarities = cosine_similarity(user_embedding, context_embeddings).flatten()
+ 
+ # Encode all context chunks to create embeddings
+ chunk_embeddings = np.array([encode_text(chunk) for chunk in context_chunks])
+ 
+ # Compute cosine similarity between the user input and each chunk
+ similarities = cosine_similarity(user_embedding, chunk_embeddings).flatten()
+ 
+ # Check if any similarity scores are above the threshold
+ if max(similarities) < similarity_threshold:
+ return "No relevant context found for the user input.", None
+ 
+ # Identify the most relevant chunk based on the highest cosine similarity score
  most_relevant_idx = np.argmax(similarities)
- return context_texts[most_relevant_idx]
+ most_relevant_chunk = context_chunks[most_relevant_idx]
+ 
+ # Return the most relevant chunk and the similarity score
+ return most_relevant_chunk
+
 
 def extract_keywords_with_gpt(user_input, max_tokens=100, temperature=0.3):
  # Define a prompt to ask GPT-4 to extract keywords and important terms
@@ -307,7 +340,7 @@ def gpt_response_to_dataframe(gpt_response):
  
 def chatbot(user_input, context="", subdomain="", use_encoder=False, max_tokens=150, temperature=0.7, top_p=0.9, frequency_penalty=0.5, presence_penalty=0.0):
  if use_encoder and context:
- context_texts = context.split("\n")
+ context_texts = context
  relevant_context = retrieve_relevant_context(user_input, context_texts)
  else:
  relevant_context = ""
@@ -389,7 +422,7 @@ iface = gr.Interface(
  gr.HTML(label="Miro"), 
  gr.HTML(label="Generate Mind Map on Mapify") 
  ],
- title="ExosAI - NASA SMD SCDD AI Assistant [version-0.8a]",
+ title="ExosAI - NASA SMD SCDD AI Assistant [version-0.9a]",
  description="ExosAI is an AI-powered assistant for generating and visualising HWO Science Cases",
 )