Saves Chat, Added "Read More" and "Delete This Response" options and more

Saves Chat, Added "Read More" and "Delete This Response" options. Last response deletes whole conversation. Adjusted buttons size. Removed thinking block <think> </think> from output.

Author: ChayScripts

Horizontal View - app.py

@@ -2,6 +2,9 @@
 import requests
 import time
 import concurrent.futures
+import json
+import os
+import re
 
 st.set_page_config(page_title="LLM Comparison", layout="wide")
 
@@ -15,6 +18,12 @@
     line-height: 1 !important;
     margin-top: 28px !important;
 }
+
+div.stButton button[data-testid*="stButton-primary"] {
+    font-size: 14px !important;
+    height: 35px !important;
+}
+
 div[data-testid="stSelectbox"] > div {
     margin-right: 0px !important;
 }
@@ -38,6 +47,44 @@ def get_models():
     st.warning("No models found. Ensure Ollama is running and has models pulled.")
     st.stop()
 
+HISTORY_FILE = "chat_history.json"
+
+def load_chat_history():
+    if os.path.exists(HISTORY_FILE):
+        try:
+            with open(HISTORY_FILE, "r", encoding="utf-8") as f:
+                return json.load(f)
+        except json.JSONDecodeError:
+            st.warning("Error decoding chat history file. Starting with empty history.")
+            return []
+        except Exception as e:
+            st.error(f"Could not load chat history: {e}")
+            return []
+    return []
+
+def save_chat_history(history):
+    try:
+        with open(HISTORY_FILE, "w", encoding="utf-8") as f:
+            json.dump(history, f, indent=4)
+    except Exception as e:
+        st.error(f"Could not save chat history: {e}")
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = load_chat_history()
+
+def delete_model_response(conversation_index, model_response_idx_in_entry):
+    actual_conversation_index = len(st.session_state.chat_history) - 1 - conversation_index
+    
+    if (0 <= actual_conversation_index < len(st.session_state.chat_history) and
+        0 <= model_response_idx_in_entry < len(st.session_state.chat_history[actual_conversation_index]["responses"])):
+        
+        st.session_state.chat_history[actual_conversation_index]["responses"].pop(model_response_idx_in_entry)
+        
+        if not st.session_state.chat_history[actual_conversation_index]["responses"]:
+            st.session_state.chat_history.pop(actual_conversation_index)
+            
+        save_chat_history(st.session_state.chat_history)
+
 prompt = st.text_area("Prompt", "")
 
 if "model_count" not in st.session_state:
@@ -53,9 +100,8 @@ def remove_model(index):
 for i in range(st.session_state.model_count):
     col1, col2 = st.columns([0.97, 0.02])
     with col1:
-        # Ensure the list is long enough
         if i >= len(st.session_state.selected_models):
-            st.session_state.selected_models.append("") 
+            st.session_state.selected_models.append("")
 
         st.session_state.selected_models[i] = st.selectbox(
             f"Model {i+1}",
@@ -68,17 +114,16 @@ def remove_model(index):
 
 selected_models_filtered = [model for model in st.session_state.selected_models if model]
 
-_, _, spacer, col_add, col_run = st.columns([0.5, 0.2, 0.1, 0.1, 0.1])
+_, col_add, col_run = st.columns([0.7, 0.15, 0.15])
 with col_add:
-    if st.button("Add new model"):
+    if st.button("Add New Model"):
         st.session_state.model_count += 1
         st.session_state.selected_models.append("")
         st.rerun()
 with col_run:
     run_clicked = st.button("Run Models", type="primary")
 
 def query_ollama_model(model_name, prompt_text):
-    """Function to query a single Ollama model."""
     try:
         start_time = time.time()
         res = requests.post(
@@ -92,15 +137,18 @@ def query_ollama_model(model_name, prompt_text):
 
         duration = round(end_time - start_time, 2)
         content = response_data.get("response", "")
-        eval_count = response_data.get("eval_count", len(content.split()))
+        
+        cleaned_content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL)
+        
+        eval_count = response_data.get("eval_count", len(cleaned_content.split()))
         eval_rate = response_data.get("eval_rate", round(eval_count / duration, 2) if duration > 0 else 0)
 
         return {
             "model": model_name,
             "duration": duration,
             "eval_count": eval_count,
             "eval_rate": eval_rate,
-            "response": content
+            "response": cleaned_content
         }
     except Exception as e:
         return {
@@ -114,14 +162,9 @@ def query_ollama_model(model_name, prompt_text):
 if run_clicked and prompt and selected_models_filtered:
     responses = []
 
-    # Create placeholders for immediate feedback
-    response_placeholders = {model: st.empty() for model in selected_models_filtered}
-
     with concurrent.futures.ThreadPoolExecutor(max_workers=len(selected_models_filtered)) as executor:
-        # Submit tasks to the thread pool
         future_to_model = {executor.submit(query_ollama_model, model, prompt): model for model in selected_models_filtered}
 
-        # Iterate as futures complete
         for future in concurrent.futures.as_completed(future_to_model):
             model_name = future_to_model[future]
             try:
@@ -136,28 +179,81 @@ def query_ollama_model(model_name, prompt_text):
                     "response": f"Error: {exc}"
                 })
 
-    # Sort responses by the order of selected models for consistent display
     ordered_responses = []
     for model in selected_models_filtered:
         for res in responses:
             if res["model"] == model:
                 ordered_responses.append(res)
                 break
+    
+    st.session_state.chat_history.append({"prompt": prompt, "responses": ordered_responses})
+    save_chat_history(st.session_state.chat_history)
+
+st.markdown("---")
+st.subheader("Previous Interactions")
+
+def get_truncated_text(text, word_limit=50):
+    words = text.split()
+    if len(words) > word_limit:
+        return ' '.join(words[:word_limit]) + "..."
+    return text
 
-    cols = st.columns(len(ordered_responses))
-    for i, res in enumerate(ordered_responses):
-        with cols[i]:
-            st.markdown(
-                f"### <span style='color:#3366cc'>{res['model']}</span>" if i % 2 == 0 else f"### <span style='color:#cc0000'>{res['model']}</span>",
-                unsafe_allow_html=True
-            )
-            st.markdown(
-                f"""
-                <div style="background-color:#e6f0ff; padding:10px; border-radius:8px; margin-bottom:10px;">
-                    <b>Duration</b>: <span style="color:#3366cc;">{res['duration']} secs</span> &nbsp;
-                    <b>Eval count</b>: <span style="color:green;">{res['eval_count']} tokens</span> &nbsp;
-                    <b>Eval rate</b>: <span style="color:green;">{res['eval_rate']} tokens/s</span>
-                </div>
-                """, unsafe_allow_html=True
-            )
-            st.write(res["response"])
+if st.session_state.chat_history:
+    for entry_idx, entry in enumerate(reversed(st.session_state.chat_history)):
+        st.markdown(f"**Prompt:** {entry['prompt']}")
+        
+        cols = st.columns(len(entry['responses']))
+        
+        for i, res in enumerate(entry['responses']):
+            with cols[i]:
+                st.markdown(
+                    f"### <span style='color:#3366cc'>{res['model']}</span>" if i % 2 == 0 else f"### <span style='color:#cc0000'>{res['model']}</span>",
+                    unsafe_allow_html=True
+                )
+                st.markdown(
+                    f"""
+                    <div style="background-color:#e6f0ff; padding:10px; border-radius:8px; margin-bottom:10px;">
+                        <b>Duration</b>: <span style="color:#3366cc;">{res['duration']} secs</span> &nbsp;
+                        <b>Eval count</b>: <span style="color:green;">{res['eval_count']} tokens</span> &nbsp;
+                        <b>Eval rate</b>: <span style="color:green;">{res['eval_rate']} tokens/s</span>
+                    </div>
+                    """, unsafe_allow_html=True
+                )
+                
+                full_response_text = res["response"]
+                words = full_response_text.split()
+                content_is_longer_than_50_words = len(words) > 50
+                
+                read_more_toggle_key = f"read_more_entry_{entry_idx}_model_{i}"
+                
+                if read_more_toggle_key not in st.session_state:
+                    st.session_state[read_more_toggle_key] = False
+                
+                if content_is_longer_than_50_words and not st.session_state[read_more_toggle_key]:
+                    st.write(get_truncated_text(full_response_text, word_limit=50))
+                else:
+                    st.write(full_response_text)
+                
+                button_cols = st.columns([0.5, 0.5])
+                
+                with button_cols[0]:
+                    if content_is_longer_than_50_words:
+                        if not st.session_state[read_more_toggle_key]:
+                            if st.button("Read More", key=f"btn_read_{read_more_toggle_key}"):
+                                st.session_state[read_more_toggle_key] = True
+                                st.rerun()
+                        else:
+                            if st.button("Show Less", key=f"btn_less_{read_more_toggle_key}"):
+                                st.session_state[read_more_toggle_key] = False
+                                st.rerun()
+                
+                with button_cols[1]:
+                    st.button(
+                        "Delete This Response",
+                        key=f"delete_response_{entry_idx}_{i}",
+                        on_click=delete_model_response,
+                        args=(entry_idx, i)
+                    )
+        st.markdown("---")
+else:
+    st.info("No previous interactions found. Run models to start saving history!")