Update app.py

app.py

@@ -1,40 +1,38 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModel, GPT2LMHeadModel, GPT2Tokenizer
-import torch
+from transformers import AutoTokenizer, AutoModel
+import openai
+import os
 
 # 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)
 
-# Load the GPT-2 model and tokenizer for response generation
-gpt2_model_name = "gpt2"
-gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
-gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model_name)
+# Set up OpenAI API key
+openai.api_key = os.getenv('OPENAI_API_KEY')
 
 def encode_text(text):
     inputs = bi_tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=128)
     outputs = bi_model(**inputs)
     # Ensure the output is 2D by averaging the last hidden state along the sequence dimension
-    return outputs.last_hidden_state.mean(dim=1).detach().numpy()
+    return outputs.last_hidden_state.mean(dim=1).detach().numpy().flatten()
 
 def generate_response(user_input, context_embedding):
-    # Create a structured prompt for GPT-2
-    combined_input = f"Question: {user_input}\nContext: {context_embedding}\nAnswer:"
+    # Create a structured prompt for GPT-4
+    context_str = ' '.join(map(str, context_embedding))  # Convert context embedding to a string
+    combined_input = f"Question: {user_input}\nContext: {context_str}\nAnswer:"
     
-    # Generate a response using GPT-2 with adjusted parameters
-    gpt2_inputs = gpt2_tokenizer.encode(combined_input, return_tensors='pt')
-    gpt2_outputs = gpt2_model.generate(
-        gpt2_inputs,
-        max_length=150,
-        num_return_sequences=1,
-        temperature=0.7,
+    # Generate a response using GPT-4
+    response = openai.Completion.create(
+        engine="gpt-4-turbo",  # Use GPT-4 engine if available, otherwise use text-davinci-003
+        prompt=combined_input,
+        max_tokens=150,
+        temperature=0.5,
         top_p=0.9,
-        repetition_penalty=1.2
+        frequency_penalty=0.5,
+        presence_penalty=0.0
     )
-    generated_text = gpt2_tokenizer.decode(gpt2_outputs[0], skip_special_tokens=True)
-    
-    return generated_text
+    return response.choices[0].text.strip()
 
 def chatbot(user_input, context=""):
     context_embedding = encode_text(context) if context else ""
@@ -47,7 +45,7 @@ iface = gr.Interface(
     inputs=[gr.Textbox(lines=2, placeholder="Enter your message here..."), gr.Textbox(lines=2, placeholder="Enter context here (optional)...")],
     outputs="text",
     title="Context-Aware Dynamic Response Chatbot",
-    description="A chatbot using a NASA-specific bi-encoder model to understand the input context and GPT-2 to generate dynamic responses."
+    description="A chatbot using a NASA-specific bi-encoder model to understand the input context and GPT-4 to generate dynamic responses."
 )
 
 # Launch the interface
@@ -57,3 +55,4 @@ iface.launch()
 
 
 
+