Update app.py

app.py

@@ -8,61 +8,15 @@ import os
 # Initialize the language model
 llm = HuggingFaceHub(repo_id="tiiuae/falcon-7b-instruct", model_kwargs={"temperature":0.6})
 
-def generate_emotional_report(user_emotion_goals, user_emotional_spaces=None, user_communication_method=None, user_listening_practices=None, user_reflection_methods=None, user_design_principles=None, user_understanding_of_principles=None, user_application_of_principles=None, user_review_and_adjustment=None, user_finalizing_emotional_report=None):
- # Check if user_emotion_goals is not None
- if user_emotion_goals is not None:
- # Generate the other variables based on user_emotion_goals
- user_emotional_spaces = llm(f"Generate emotional spaces based on {user_emotion_goals}")
- user_communication_method = llm(f"Generate communication method based on {user_emotion_goals}")
- user_listening_practices = llm(f"Generate listening practices based on {user_emotion_goals}")
- user_reflection_methods = llm(f"Generate reflection methods based on {user_emotion_goals}")
- user_design_principles = llm(f"Generate design principles based on {user_emotion_goals}")
- user_understanding_of_principles = llm(f"Generate understanding of principles based on {user_emotion_goals}")
- user_application_of_principles = llm(f"Generate application of principles based on {user_emotion_goals}")
- user_review_and_adjustment = llm(f"Generate review and adjustment based on {user_emotion_goals}")
- user_finalizing_emotional_report = llm(f"Generate finalizing emotional report based on {user_emotion_goals}")
+def chat(user_message):
+   # Generate a response from the language model
+   response = llm(user_message)
+   
+   # Return the response
+   return response
 
- # Define your chat template here
- chat_template = f"""
- # Step 1: Identify the Emotional Goals
- [ASSISTANT] What kind of emotions would you like to evoke in your partner? The user's response is: {user_emotion_goals}
-
- # Step 2: Create Emotional Spaces
- [ASSISTANT] What environments or activities can we design to elicit these emotions? The user's response is: {user_emotional_spaces}
-
- # Step 3: Communicate Openly
- [ASSISTANT] How can we express feelings and desires openly? The user's response is: {user_communication_method}
-
- # Step 4: Listen Actively
- [ASSISTANT] How can we pay attention to our partner's feelings and needs? The user's response is: {user_listening_practices}
-
- # Step 5: Reflect on Emotions
- [ASSISTANT] How can we encourage reflection on the emotions experienced during shared activities? The user's response is: {user_reflection_methods}
-
- # Step 6: Implement Emotional Design Principles
- [ASSISTANT] How can we integrate aesthetics, usability, and storytelling in our relationship? The user's response is: {user_design_principles}
-
- # Step 7: Understand Emotional Design Principles
- [ASSISTANT] What principles of emotional design are applicable to our relationship? The user's response is: {user_understanding_of_principles}
-
- # Step 8: Apply Emotional Design Principles
- [ASSISTANT] How can we apply these principles to our relationship? The user's response is: {user_application_of_principles}
-
- # Step 9: Review Emotional Design Principles
- [ASSISTANT] How can we review and adjust our application of these principles? The user's response is: {user_review_and_adjustment}
-
- # Step 10: Finalize Emotional Report
- [ASSISTANT] Let's finalize our emotional report. The user's response is: {user_finalizing_emotional_report}
- """
- # Pass the chat template to the language model
- response = llm(chat_template)
- 
- # Return the response
- return response
-
-# Create a Gradio ChatInterface
-iface = gr.ChatInterface(fn=generate_emotional_report, inputs="text", outputs="text", title="Emotional Design Report Generator", description="Enter your emotional goals and let the assistant generate an emotional design report.")
+# Create a Gradio interface
+iface = gr.Interface(fn=chat, inputs="text", outputs="text")
 
 # Launch the interface
-iface.launch()
-    
+iface.launch()