Update chatbot.py

chatbot.py

@@ -4,22 +4,16 @@ from transformers import T5Tokenizer, T5ForConditionalGeneration
 from sentence_transformers import SentenceTransformer
 from pinecone import Pinecone
 
-device = 'cpu' 
+device = 'cpu'
 
 # Initialize Pinecone instance
-pc = Pinecone(api_key='89eeb534-da10-4068-92f7-12eddeabe1e5')
-
-# Check if the index exists; if not, create it
-index_name = 'abstractive-question-answering'
-index = pc.Index(index_name)
+pc = Pinecone(api_key='your-pinecone-api-key')
 
 # Initialize FastAPI app
 app = FastAPI()
 
 # Initialize the models
 def load_models():
-    print("Loading models...")
-    
     retriever = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_mpnet-base")
     tokenizer = T5Tokenizer.from_pretrained('t5-small')
     generator = T5ForConditionalGeneration.from_pretrained('t5-base').to(device)
@@ -38,29 +32,17 @@ def predict(query: QueryInput):
     xq = retriever.encode([query_text]).tolist()
     xc = index.query(vector=xq, top_k=1, include_metadata=True)
 
-    # Check if 'matches' exists and is a list
     if 'matches' in xc and isinstance(xc['matches'], list):
         context = [m['metadata']['Output'] for m in xc['matches']]
         context_str = " ".join(context)
         formatted_query = f"answer the question: {query_text} context: {context_str}"
     else:
-        # Handle the case where 'matches' isn't found or isn't in the expected format
         context_str = ""
         formatted_query = f"answer the question: {query_text} context: {context_str}"
 
     # Generate answer using T5 model
-    output_text = context_str
-    if len(output_text.splitlines()) > 5:
-        return {"response": output_text}
-
-    if output_text.lower() == "none":
-        return {"response": "The topic is not covered in the student manual."}
-
     inputs = tokenizer.encode(formatted_query, return_tensors="pt", max_length=512, truncation=True).to(device)
     ids = generator.generate(inputs, num_beams=2, min_length=10, max_length=60, repetition_penalty=1.2)
     answer = tokenizer.decode(ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
 
     return {"response": answer}
-
-# To run the server (use uvicorn when deploying):
-# uvicorn chatbot:app --reload