Update app.py

app.py

@@ -1,76 +1,111 @@
-import os
-from typing import List, Optional
-from web_ui import WebUI
-from qwen_agent.agents import Assistant
-import spaces
+import gradio as gr
+import logging
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import spaces  # Import spaces module
 
-@spaces.GPU
-def create_ev_prompt(electricity_consumption, car_type, vehicle_choice, range_km, daily_mileage, travel_mileage):
-    """
-    Create the prompt for the EV Advisor based on user inputs.
-    """
-    return (
-        f"Analyze the advantages of a {car_type} with the following parameters:\n"
-        f"- Electricity consumption: {electricity_consumption} kWh/100km\n"
-        f"- Range: {range_km} km\n"
-        f"- Daily mileage: {daily_mileage} km\n"
-        f"- Annual travel mileage: {travel_mileage} km\n"
-        f"- Vehicle type: {vehicle_choice}\n\n"
-        "Please calculate potential fuel cost savings, CO2 emissions reductions, "
-        "and highlight other advantages like maintenance costs and driving experience. "
-        "Use these assumptions:\n"
-        "- Electricity cost: 0.13 EUR/kWh\n"
-        "- Gasoline cost: 1.6 EUR/liter\n"
-        "- Average fuel economy for conventional vehicles: 25 mpg\n"
-        "- Average fuel economy for hybrids: 50 mpg\n\n"
-        "Provide a detailed report with calculations and actionable insights."
-    )
+# Configure logging
+logging.basicConfig(level=logging.INFO)
 
-@spaces.GPU
-def app_gui():
-    """
-    Define and run the EV Advisor application GUI.
-    """
+# Model configuration
+model_name_or_path = "Qwen/QwQ-32B-Preview"
 
-    # Define the assistant
-    bot = Assistant(
-        llm={
-            'model': os.environ.get("MODELNAME", "DevQuasar/Qwen.QwQ-32B-Preview-GGUF"),
-            'model_type': 'qwen_dashscope',
-            'generate_cfg': {
-                'max_input_tokens': 32768,
-                'max_retries': 10,
-                'temperature': 0.7,
-                'repetition_penalty': 1.0,
-                'top_k': 20,
-                'top_p': 0.9,
-            },
-        },
-        name="EV Advisor",
-        description="The assistant explains the advantages of electric or hybrid vehicles, provides cost and CO2 savings, and other benefits.",
-        system_message=(
-            "You are an assistant specializing in electric and hybrid vehicles. You will ask the user "
-            "for necessary details like electricity consumption, car type, and travel mileage, then calculate "
-            "and explain the benefits of EVs with detailed insights and actionable advice."
-        ),
-        rag_cfg={'max_ref_token': 32768, 'rag_searchers': []},
-    )
+# Initialize tokenizer and model variables
+tokenizer = None
+model = None
+
+# Define the function to load the model with the @spaces.GPU decorator
+@spaces.GPU()
+def load_model():
+    global tokenizer, model
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_name_or_path, use_fast=False, trust_remote_code=True
+        )
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name_or_path,
+            torch_dtype=torch.float16,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        logging.info("Model loaded successfully.")
+    except Exception as e:
+        logging.error(f"Failed to load model: {e}")
+        raise e
+
+# Call the load_model function to load the model
+load_model()
 
-    chatbot_config = {
-        'input.placeholder': "Ask about electric vehicles, or type '/start' to begin the questionnaire.",
-        'verbose': True,
-        'prompt.suggestions': [
-            {'text': "What are the benefits of switching to an electric car?"},
-            {'text': "How do hybrid vehicles compare to conventional ones in terms of emissions?"},
-            {'text': "/start"},
-        ],
-    }
+def explain_advantages(electricity_consumption, car_type, vehicle_choice, range_km, daily_mileage, travel_mileage):
+    logging.info("Function 'explain_advantages' called with inputs:")
+    logging.info(f"Electricity Consumption: {electricity_consumption}")
+    logging.info(f"Car Type: {car_type}")
+    logging.info(f"Vehicle Choice: {vehicle_choice}")
+    logging.info(f"Range (km): {range_km}")
+    logging.info(f"Daily Mileage (km): {daily_mileage}")
+    logging.info(f"Travel Mileage (km/year): {travel_mileage}")
 
-    # WebUI Integration
-    WebUI(bot, chatbot_config=chatbot_config).run(
-        concurrency_limit=80,
-        enable_mention=False
+    # Input validation
+    try:
+        electricity_consumption = float(electricity_consumption)
+        range_km = float(range_km)
+        daily_mileage = float(daily_mileage)
+        travel_mileage = float(travel_mileage)
+        logging.info("Input conversion successful.")
+    except ValueError as ve:
+        logging.error(f"Input conversion error: {ve}")
+        return "Invalid input: Please enter numeric values for consumption, range, and mileage."
+
+    # Construct the prompt
+    prompt = (
+        f"Given a {car_type} with electricity consumption of {electricity_consumption} kWh/100km, "
+        f"a range of {range_km} km, daily mileage of {daily_mileage} km, and annual mileage of {travel_mileage} km, "
+        f"compare the benefits of choosing a {vehicle_choice} vehicle over a conventional vehicle. "
+        f"Calculate potential fuel cost savings, CO2 emissions savings, and highlight additional benefits such as maintenance costs and driving experience. "
+        f"Assume the average electricity cost is 0.13 EUR per kWh and gasoline cost is 1.6 EUR per liter. "
+        f"Consider the average fuel economy for conventional vehicles to be 25 mpg and for hybrids to be 50 mpg. "
+        f"Provide a convincing argument with derived figures to illustrate the advantages. "
+        f"Provide advice to optimize the benefits of the chosen vehicle based on the input figures."
     )
+    logging.info("Prompt constructed successfully.")
+
+    # Prepare the input for the model
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    # Generate the response
+    try:
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=500,
+            temperature=0.7,
+            top_p=0.95,
+            repetition_penalty=1.0,
+            do_sample=True,
+        )
+        logging.info("Model generation successful.")
+        # Decode and post-process the generated text
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        response = generated_text[len(prompt):].strip()
+        return response
+    except Exception as e:
+        logging.error(f"Error during model generation: {e}")
+        return f"An error occurred while generating the response: {e}"
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=explain_advantages,
+    inputs=[
+        gr.Number(label="Electricity Consumption (kWh/100km)", value=15.0),
+        gr.Textbox(label="Type of Car", value="Electric"),
+        gr.Radio(choices=["Hybrid", "Electric"], label="Hybrid/Electric"),
+        gr.Number(label="Range (km)", value=300.0),
+        gr.Number(label="Estimated Daily Mileage (km)", value=50.0),
+        gr.Number(label="Mileage from Travels (km/year)", value=1000.0),
+    ],
+    outputs=gr.Textbox(label="Advantages of Going Fully Electric"),
+    title="Explaining the Advantages of Going Fully Electric",
+    description="Enter your vehicle's parameters to understand the benefits of switching to a fully electric vehicle.",
+)
 
-if __name__ == "__main__":
-    app_gui()
+# Launch the app
+iface.launch()