Update app.py

app.py

@@ -78,6 +78,9 @@ model_name = "tiiuae/falcon-7b-instruct"
 model = AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+# Load the dataset
+dataset = load_dataset("nisaar/Lawyer_GPT_India")  
+
 def write_top_bar():
     col1, col2, col3 = st.columns([1,10,2])
     with col1:
@@ -109,8 +112,11 @@ def handle_input():
     if len(chat_history) == MAX_HISTORY_LENGTH:
         chat_history = chat_history[:-1]
 
+    # Find the most similar example in the dataset
+    closest_example = find_closest_example(input, dataset)  # Implement your own logic to find the closest example
+
     # Generate response using the model
-    inputs = tokenizer.encode(input, return_tensors="pt")
+    inputs = tokenizer.encode(closest_example, return_tensors="pt")
     outputs = model.generate(inputs)
     answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
@@ -122,6 +128,12 @@ def handle_input():
     })
     st.session_state.input = ""
 
+def find_closest_example(input, dataset):
+    # Implement your own logic to find the closest example in the dataset based on the user input
+    # You can use techniques like cosine similarity, semantic similarity, or any other approach that fits your dataset and requirements
+    # Return the closest example as a string
+    pass
+
 def write_user_message(md):
     col1, col2 = st.columns([1,12])