Update app.py

app.py

@@ -72,8 +72,9 @@ class TemporalPredictionSystem:
         self.model_growth_rate = 0
         self.total_interactions = 0
 
-        
+        # Offset for continuous quote expansion
         self.quote_offset = 0
+
         # Response generators
         self.response_templates = [
             "Based on your current trajectory, I see {{future}}.",
@@ -86,7 +87,6 @@ class TemporalPredictionSystem:
             "From my vantage point, I'd suggest focusing more on {{suggestion}}.",
             "Your intuition about {{subject}} is {{accuracy}} - I know because I've {{evidence}}."
         ]
-
         self.insight_patterns = [
             "this challenge ultimately strengthens your {{attribute}}",
             "you develop a unique perspective on {{topic}} that others find valuable",
@@ -106,7 +106,7 @@ class TemporalPredictionSystem:
             self.topic_extractor[word] = 1
             self.topic_relations[word] = []
 
-        # Fetch quotes data and build markov chain
+        # Fetch quotes data and build Markov chain
         self.fetch_quotes_data()
 
     def fetch_quotes_data(self):
@@ -115,47 +115,41 @@ class TemporalPredictionSystem:
             url = f"https://datasets-server.huggingface.co/rows?dataset=Abirate%2Fenglish_quotes&config=default&split=train&offset={self.quote_offset}&length=100"
             response = requests.get(url)
             data = response.json()
-    
+
             if data and "rows" in data:
                 for row in data["rows"]:
                     if row and "row" in row and "quote" in row["row"]:
                         quote = row["row"]["quote"]
                         self.quotes_data.append(quote)
-    
                         # Process quote into words for corpus
                         words = [w for w in re.sub(r'[^\w\s]', '', quote.lower()).split() if len(w) > 3]
                         self.word_corpus = list(set(self.word_corpus + words))
-    
                         # Extract tags if available
                         if "tags" in row["row"]:
                             tags = row["row"]["tags"] if isinstance(row["row"]["tags"], list) else [row["row"]["tags"]]
                             self.tags_data.extend(tags)
-    
-                            # Add tags to corpus
                             tag_words = [tag.lower() for tag in tags if len(tag) > 3]
                             self.word_corpus = list(set(self.word_corpus + tag_words))
-    
+
                 print(f"Loaded {len(self.quotes_data)} quotes and {len(self.tags_data)} tags")
                 print(f"Word corpus expanded to {len(self.word_corpus)} words")
-    
+                
                 # Update topic extractor with any new words
                 for word in self.word_corpus:
                     if word not in self.topic_extractor:
                         self.topic_extractor[word] = 1
                         self.topic_relations[word] = []
-    
-                # Rebuild Markov chain to include new quotes and tags
+                
+                # Rebuild Markov chain with new data
                 self.build_initial_markov_chain()
-    
-                # Increment the offset for next iteration to load additional quotes
+                
+                # Increment offset for the next fetch
                 self.quote_offset += 100
-    
+
         except Exception as e:
             print(f"Error fetching quotes data: {e}")
-            # Rebuild the Markov chain with current corpus if fetch fails
             self.build_initial_markov_chain()
 
-
     def build_initial_markov_chain(self):
         """Build a Markov chain from quotes and corpus data"""
         chain = {}
@@ -470,7 +464,6 @@ class TemporalPredictionSystem:
         """Detect causal loops in the network using DFS"""
         loops = []
         visited = set()
-
         def dfs(node_id, path):
             if node_id in path:
                 loops.append(path[path.index(node_id):] + [node_id])
@@ -997,6 +990,7 @@ class TemporalPredictionSystem:
 # Global System Initialization
 # -------------------------------------------------------------------
 system = TemporalPredictionSystem()
+# Run some initial simulation steps to pre-train
 time.sleep(2)
 system.run_simulation_steps(20)
 print("Future Self Conversation System initialized and pre-trained.")
@@ -1023,22 +1017,56 @@ def update_network_visualization():
     plt.close(fig)
     return img
 
+def pre_train_fn(progress=gr.Progress()):
+    steps = 20
+    for i in range(steps):
+        system.run_simulation_steps(1)
+        progress((i+1)/steps)
+        yield f"<h3>Pre-training in progress: {(i+1)/steps*100:.0f}% complete</h3>", (i+1)/steps*100, gr.update(visible=False)
+        time.sleep(0.2)
+    yield "<h3>Pre-training complete!</h3>", 100, gr.update(visible=True)
+
 # -------------------------------------------------------------------
-# Gradio App Layout
+# Gradio App Layout (with custom CSS for mobile responsiveness)
 # -------------------------------------------------------------------
 with gr.Blocks(title="Future Self Conversation System") as demo:
-    gr.Markdown("# Future Self Conversation System")
-    gr.Markdown("Converse with a simulation of your future self, powered by a neural temporal prediction model.")
-    with gr.Tabs():
-        with gr.TabItem("Chat"):
-            chatbot = gr.Chatbot(label="Conversation")
-            txt = gr.Textbox(placeholder="Type your message here...", label="Your Message")
-            send_btn = gr.Button("Send")
-            send_btn.click(fn=chat_fn, inputs=[txt, chatbot], outputs=chatbot).then(lambda: "", None, txt)
-        with gr.TabItem("Network Visualization"):
-            viz_btn = gr.Button("Update Network Visualization")
-            image_out = gr.Image(label="Neural Network Visualization")
-            viz_btn.click(fn=update_network_visualization, inputs=[], outputs=image_out)
+    
+    # Custom CSS for chat height and mobile responsiveness
+    gr.HTML("""
+    <style>
+      /* Lower chat area height */
+      #chatbot { height: 300px !important; }
+      /* Mobile responsive adjustments */
+      @media (max-width: 600px) {
+        #chatbot { height: 200px !important; }
+      }
+      /* Ensure split screen columns stack on mobile */
+      .gradio-container .row { flex-wrap: wrap; }
+      .gradio-container .column { flex: 1 1 300px; }
+    </style>
+    """)
+    
+    # Pre-training progress area (shown on page load)
+    pretrain_status = gr.HTML("<h3>Initializing pre-training...</h3>")
+    progress_bar = gr.Slider(minimum=0, maximum=100, value=0, interactive=False, label="Pre-training Progress")
+    
+    # Main interface (hidden until pre-training completes)
+    main_interface = gr.Column(visible=False)
+    
+    with main_interface:
+        with gr.Row():
+            with gr.Column():
+                chatbot = gr.Chatbot(label="Conversation", elem_id="chatbot")
+                txt = gr.Textbox(placeholder="Type your message here...", label="Your Message")
+                send_btn = gr.Button("Send")
+            with gr.Column():
+                network_image = gr.Image(label="Neural Network Visualization")
+                # Auto-update network visualization every 5 seconds.
+                interval = gr.Interval(5, fn=update_network_visualization, outputs=network_image)
+        send_btn.click(fn=chat_fn, inputs=[txt, chatbot], outputs=chatbot).then(lambda: "", None, txt)
+    
+    # On page load, run pre-training and then reveal the main interface.
+    demo.load(fn=pre_train_fn, outputs=[pretrain_status, progress_bar, main_interface])
 
 # -------------------------------------------------------------------
 # Launch the App