Hopefully fix again!

app.py

@@ -22,7 +22,7 @@ model, tokenizer = FastLanguageModel.from_pretrained(
     dtype=None,
     load_in_4bit=True
 )
-adapter_path = "FosterSystemsDatabase/model"  # Path to LoRA adapter on Hugging Face
+adapter_path = "FosterSystemsDatabase/model"
 model = PeftModel.from_pretrained(model, adapter_path)
 
 # Load CSV data
@@ -30,17 +30,14 @@ file_path = 'Clean Missouri Data.csv'
 df = pd.read_csv(file_path, encoding='MacRoman')
 
 def search_relevant_policies(query, df, top_n=10, max_chars=40000):
-    # Convert policies into a TF-IDF matrix
     tfidf = TfidfVectorizer(stop_words='english')
     tfidf_matrix = tfidf.fit_transform(df['Content'])
     query_vector = tfidf.transform([query])
     cosine_sim = cosine_similarity(query_vector, tfidf_matrix).flatten()
-    
-    # Get the top N relevant policies
+
     top_indices = cosine_sim.argsort()[-top_n:][::-1]
     relevant_policies = df.iloc[top_indices].copy()
 
-    # Ensure total text is capped at max_chars
     char_count = 0
     valid_indices = []
     for idx, row in relevant_policies.iterrows():
@@ -49,7 +46,7 @@ def search_relevant_policies(query, df, top_n=10, max_chars=40000):
             break
         char_count += content_length
         valid_indices.append(idx)
-    
+
     truncated_policies = relevant_policies.loc[valid_indices]
     return truncated_policies
 
@@ -65,12 +62,12 @@ def process_query(query, tokenizer):
     relevant_policies = search_relevant_policies(query, df)
     formatted_policies = [row['Content'] for _, row in relevant_policies.iterrows()]
     relevant_policy_text = "\n\n".join(formatted_policies)
-    
+
     messages_with_relevant_policies = [
         {"role": "system", "content": relevant_policy_text},
         {"role": "user", "content": query},
     ]
-    
+
     tokenizer = get_chat_template(tokenizer, chat_template="llama-3.1")
     inputs = tokenizer.apply_chat_template(
         messages_with_relevant_policies,
@@ -78,20 +75,19 @@ def process_query(query, tokenizer):
         add_generation_prompt=True,
         return_tensors="pt"
     ).to("cuda")
-    
+
     FastLanguageModel.for_inference(model)
     outputs = model.generate(input_ids=inputs, max_new_tokens=512, use_cache=True, temperature=0.7, min_p=0.1)
     generated_response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
     response = get_content_after_query(generated_response, query)
-    
-    # Rank the top policy using SBERT
+
     model_sbert = SentenceTransformer('all-MiniLM-L6-v2')
     response_embedding = model_sbert.encode(generated_response, convert_to_tensor=True)
     policy_embeddings = model_sbert.encode(relevant_policies['Content'].tolist(), convert_to_tensor=True)
     cosine_similarities = util.cos_sim(response_embedding, policy_embeddings).flatten()
     most_relevant_index = cosine_similarities.argmax().item()
     most_relevant_link = relevant_policies.iloc[most_relevant_index]['Link to Content']
-    
+
     return {
         "response": response,
         "most_relevant_link": most_relevant_link
@@ -115,9 +111,9 @@ def greet(query):
 def choose_preference(name, output1, output2, preference, query, broken):
     if not name:
         return "Please enter your name before submitting."
-    
+
     broken_flag = "Yes" if broken else "No"
-    
+
     if preference == "Output 1":
         new_row = [query, output1, output2, name, broken_flag]
         spreadsheet.append_row(new_row)
@@ -133,18 +129,19 @@ def choose_preference(name, output1, output2, preference, query, broken):
 with gr.Blocks() as demo:
     name_input = gr.Textbox(label="Enter your name")
     query_input = gr.Textbox(label="Enter your query")
-    # "Generate Outputs" button is now placed right after the query input
     generate_button = gr.Button("Generate Outputs")
     output_1 = gr.Textbox(label="Output 1", interactive=False)
     output_2 = gr.Textbox(label="Output 2", interactive=False)
     preference = gr.Radio(["Output 1", "Output 2"], label="Choose your preferred output")
     broken_flag = gr.Checkbox(label="Mark as Broken Response")
+    preference_result = gr.Textbox(label="Preference Result", interactive=False)
     submit_button = gr.Button("Submit Preference")
-    
+
     generate_button.click(greet, inputs=query_input, outputs=[output_1, output_2])
     submit_button.click(
         choose_preference,
         inputs=[name_input, output_1, output_2, preference, query_input, broken_flag],
+        outputs=preference_result
     ).then(
         fn=lambda: ("", "", "", "", "", False, ""),
         inputs=[],