Update app.py

app.py

@@ -1,15 +1,7 @@
 import gradio as gr
-import pandas as pd
-import torch
-from transformers import pipeline
-
-# Load the Zephyr-7B-Beta model pipeline
-pipe = pipeline(
-    "text-generation",
-    model="HuggingFaceH4/zephyr-7b-beta",
-    torch_dtype=torch.bfloat16,
-    device_map="auto"
-)
+
+# Load the Qwen2.5-72B-Instruct model
+model = gr.load("models/Qwen/Qwen2.5-72B-Instruct")
 
 # Define the initial system message
 system_message = {
@@ -40,11 +32,7 @@ def submit_questionnaire(name, age, location, gender, ethnicity, height, weight,
         "Everyday Style": everyday_style
     }
 
-    df = pd.DataFrame([questionnaire_data])  # Create DataFrame from dictionary
-
-    # Append to CSV file
-    df.to_csv("questionnaire_responses.csv", mode='a', header=not pd.io.common.file_exists("questionnaire_responses.csv"), index=False)
-
+    # Here you can add logic to save the data as required, e.g., in a CSV
     return "Thank you for completing the questionnaire!"
 
 # Function to handle chat
@@ -53,16 +41,15 @@ def chat(user_input, messages):
         # Append user message to the conversation history
         messages.append({"role": "user", "content": user_input})
 
-        # Prepare the input for the model
+        # Prepare input for the model
         input_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
 
-        # Generate a response using the pipeline
+        # Generate a response using the Qwen model
         try:
-            response = pipe(input_text, max_new_tokens=256, return_full_text=False)
-
-            # Extract the assistant's response
-            response_content = response[0]['generated_text'].strip()
-
+            response = model(input_text)  # Call the model directly
+            # Assuming the response is a list with one dictionary containing 'generated_text'
+            response_content = response[0]['generated_text'].strip()  # Access the generated text
+            
             # Store assistant response in the chat history
             messages.append({"role": "assistant", "content": response_content})
 
@@ -74,7 +61,7 @@ def chat(user_input, messages):
 
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("## FRIDAY")
+    gr.Markdown("## Fashion Assistant Chatbot")
 
     # Sidebar for user inputs
     with gr.Row():