Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModel
+from openai import OpenAI
+import os
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load the NASA-specific bi-encoder model and tokenizer
+bi_encoder_model_name = "nasa-impact/nasa-smd-ibm-st-v2"
+bi_tokenizer = AutoTokenizer.from_pretrained(bi_encoder_model_name)
+bi_model = AutoModel.from_pretrained(bi_encoder_model_name)
+
+# Set up OpenAI client
+api_key = os.getenv('OPENAI_API_KEY')
+client = OpenAI(api_key=api_key)
+
+# Define a system message to introduce Exos
+system_message = "You are Exos, a helpful assistant specializing in Exoplanet research. Provide detailed and accurate responses related to Exoplanet research."
+
+def encode_text(text):
+    inputs = bi_tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=128)
+    outputs = bi_model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).detach().numpy().flatten()  # Ensure the output is 2D
+
+def retrieve_relevant_context(user_input, context_texts):
+    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)  # Flatten each embedding
+    similarities = cosine_similarity(user_embedding, context_embeddings).flatten()
+    most_relevant_idx = np.argmax(similarities)
+    return context_texts[most_relevant_idx]
+
+def generate_response(user_input, relevant_context="", max_tokens=150, temperature=0.7, top_p=0.9, frequency_penalty=0.5, presence_penalty=0.0):
+    if relevant_context:
+        combined_input = f"Context: {relevant_context}\nQuestion: {user_input}\nAnswer:"
+    else:
+        combined_input = f"Question: {user_input}\nAnswer:"
+    
+    response = client.chat.completions.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": combined_input}
+        ],
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        frequency_penalty=frequency_penalty,
+        presence_penalty=presence_penalty
+    )
+    return response.choices[0].message.content.strip()
+
+def chatbot(user_input, context="", 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")
+        relevant_context = retrieve_relevant_context(user_input, context_texts)
+    else:
+        relevant_context = ""
+    response = generate_response(user_input, relevant_context, max_tokens, temperature, top_p, frequency_penalty, presence_penalty)
+    return response
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter your message here...", label="Your Question"),
+        gr.Textbox(lines=5, placeholder="Enter context here, separated by new lines...", label="Context"),
+        gr.Checkbox(label="Use NASA SMD Bi-Encoder for Context"),
+        gr.Slider(50, 500, value=150, step=10, label="Max Tokens"),
+        gr.Slider(0.0, 1.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(0.0, 1.0, value=0.9, step=0.1, label="Top-p"),
+        gr.Slider(0.0, 1.0, value=0.5, step=0.1, label="Frequency Penalty"),
+        gr.Slider(0.0, 1.0, value=0.0, step=0.1, label="Presence Penalty")
+    ],
+    outputs=gr.Textbox(label="Exos says..."),
+    title="Exos - Your Exoplanet Research Assistant",
+    description="Exos is a helpful assistant specializing in Exoplanet research. Provide context to get more refined and relevant responses.",
+)
+
+# Launch the interface
+iface.launch(share=True)