remove app simple. update readme

README-HF.md

@@ -32,10 +32,10 @@ The current social graph represents a British person with MND who:
 
 1. Select a person from the dropdown menu
 2. View their context information
-3. Optionally enter current conversation context
-4. Choose a suggestion type
-5. Click "Generate Suggestions" to get contextually relevant phrases
-6. Click "Speak Selected Text" to simulate speaking the selected phrase
+3. Optionally enter current conversation context or record audio
+4. If you record audio, click "Transcribe" to convert it to text
+5. Choose a suggestion type
+6. Click "Generate Suggestions" to get contextually relevant phrases
 
 ## Customizing the Social Graph
 

README.md

@@ -97,11 +97,11 @@ To deploy this application to Hugging Face Spaces:
 
 ## Future Improvements
 
-- Add speech recognition for input
-- Implement text-to-speech for output
 - Add a visual representation of the social graph
 - Support for multiple users with different social graphs
 - Add emotion/sentiment detection for more contextually appropriate suggestions
+- Implement text-to-speech for output
+- Improve speech recognition with a larger Whisper model
 
 ## License
 

app.py

@@ -169,7 +169,12 @@ with gr.Blocks(title="AAC Social Graph Assistant") as demo:
         outputs=[suggestions_output],
     )
 
-    speak_btn.click(speak_text, inputs=[suggestions_output], outputs=[speech_output])
+    # Transcribe audio to text
+    transcribe_btn.click(
+        transcribe_audio,
+        inputs=[audio_input],
+        outputs=[user_input],
+    )
 
 # Launch the app
 if __name__ == "__main__":

app_simple.py

@@ -1,148 +0,0 @@
-import gradio as gr
-from utils import SocialGraphManager, SuggestionGenerator
-
-# Initialize the social graph manager and suggestion generator
-social_graph = SocialGraphManager("social_graph.json")
-suggestion_generator = SuggestionGenerator()
-
-def format_person_display(person):
-    """Format person information for display in the dropdown."""
-    return f"{person['name']} ({person['role']})"
-
-def get_people_choices():
-    """Get formatted choices for the people dropdown."""
-    people = social_graph.get_people_list()
-    return {format_person_display(person): person["id"] for person in people}
-
-def get_suggestion_categories():
-    """Get suggestion categories from the social graph."""
-    if "common_utterances" in social_graph.graph:
-        return list(social_graph.graph["common_utterances"].keys())
-    return []
-
-def on_person_change(person_id):
-    """Handle person selection change."""
-    if not person_id:
-        return "", []
-    
-    person_context = social_graph.get_person_context(person_id)
-    context_info = f"**{person_context.get('name', '')}** - {person_context.get('role', '')}\n\n"
-    context_info += f"**Topics:** {', '.join(person_context.get('topics', []))}\n\n"
-    context_info += f"**Frequency:** {person_context.get('frequency', '')}\n\n"
-    context_info += f"**Context:** {person_context.get('context', '')}"
-    
-    # Get common phrases for this person
-    phrases = person_context.get("common_phrases", [])
-    phrases_text = "\n\n".join(phrases)
-    
-    return context_info, phrases_text
-
-def generate_suggestions(person_id, user_input, suggestion_type):
-    """Generate suggestions based on the selected person and user input."""
-    if not person_id:
-        return "Please select a person first."
-    
-    person_context = social_graph.get_person_context(person_id)
-    
-    # If suggestion type is "model", use the language model
-    if suggestion_type == "model":
-        suggestion = suggestion_generator.generate_suggestion(person_context, user_input)
-        return suggestion
-    
-    # If suggestion type is "common_phrases", use the person's common phrases
-    elif suggestion_type == "common_phrases":
-        phrases = social_graph.get_relevant_phrases(person_id, user_input)
-        return "\n\n".join(phrases)
-    
-    # If suggestion type is a category from common_utterances
-    elif suggestion_type in get_suggestion_categories():
-        utterances = social_graph.get_common_utterances(suggestion_type)
-        return "\n\n".join(utterances)
-    
-    # Default fallback
-    return "No suggestions available."
-
-def speak_text(text):
-    """Function to 'speak' the selected text (placeholder for TTS integration)."""
-    return f"Speaking: {text}"
-
-# Create the Gradio interface
-with gr.Blocks(title="AAC Social Graph Assistant") as demo:
-    gr.Markdown("# AAC Social Graph Assistant")
-    gr.Markdown("Select who you're talking to, and get contextually relevant suggestions.")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            # Person selection
-            person_dropdown = gr.Dropdown(
-                choices=get_people_choices(),
-                label="Who are you talking to?"
-            )
-            
-            # Context display
-            context_display = gr.Markdown(label="Context Information")
-            
-            # User input
-            user_input = gr.Textbox(
-                label="Your current conversation (optional)",
-                placeholder="Type or paste current conversation context here...",
-                lines=3
-            )
-            
-            # Suggestion type selection
-            suggestion_type = gr.Radio(
-                choices=["model", "common_phrases"] + get_suggestion_categories(),
-                value="model",
-                label="Suggestion Type"
-            )
-            
-            # Generate button
-            generate_btn = gr.Button("Generate Suggestions", variant="primary")
-        
-        with gr.Column(scale=1):
-            # Common phrases
-            common_phrases = gr.Textbox(
-                label="Common Phrases",
-                placeholder="Common phrases will appear here...",
-                lines=5
-            )
-            
-            # Suggestions output
-            suggestions_output = gr.Textbox(
-                label="Suggested Phrases",
-                placeholder="Suggestions will appear here...",
-                lines=8
-            )
-            
-            # Speak button
-            speak_btn = gr.Button("Speak Selected Text", variant="secondary")
-            
-            # Speech output
-            speech_output = gr.Textbox(
-                label="Speech Output",
-                placeholder="Speech output will appear here...",
-                lines=2
-            )
-    
-    # Set up event handlers
-    person_dropdown.change(
-        on_person_change,
-        inputs=[person_dropdown],
-        outputs=[context_display, common_phrases]
-    )
-    
-    generate_btn.click(
-        generate_suggestions,
-        inputs=[person_dropdown, user_input, suggestion_type],
-        outputs=[suggestions_output]
-    )
-    
-    speak_btn.click(
-        speak_text,
-        inputs=[suggestions_output],
-        outputs=[speech_output]
-    )
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch()