Update app.py

app.py

@@ -1,67 +1,88 @@
-import llama_cpp
-import llama_cpp.llama_tokenizer
+import yaml
+import re
+from smolagents import CodeAgent, HfApiModel
+from tools.final_answer import FinalAnswerTool
+from Gradio_UI import GradioUI
 
-import gradio as gr
-
-llama = llama_cpp.Llama.from_pretrained(
-    repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF",
-    filename="*q8_0.gguf",
-    tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(
-        "Qwen/Qwen1.5-0.5B"
-    ),
-    verbose=False,
+# Updated system prompt: Only output the final, direct advice in plain text.
+system_prompt = (
+    "You are a health and lifestyle advisor specializing in the early detection and prevention of hypertension. "
+    "Provide only the final, direct, and concise lifestyle advice based solely on the user's details. "
+    "Do NOT include any internal reasoning, chain-of-thought, intermediate steps, or code snippets. "
+    "Output exactly one final answer as plain text with no extra commentary."
 )
 
-model = "gpt-3.5-turbo"
-
-
-def predict(message, history):
-    messages = []
-
-    for user_message, assistant_message in history:
-        messages.append({"role": "user", "content": user_message})
-        messages.append({"role": "assistant", "content": assistant_message})
+def remove_code_snippets(text):
+    """
+    Removes code blocks, inline code, chain-of-thought, and debugging/step logs from the output.
+    """
+    # Remove triple-backtick code blocks.
+    text = re.sub(r"```[\s\S]+?```", "", text, flags=re.DOTALL)
+    # Remove inline code enclosed in single backticks.
+    text = re.sub(r"`[^`]+`", "", text)
+    # Remove any text between <think> and </think> tags.
+    text = re.sub(r"<think>[\s\S]*?</think>", "", text, flags=re.DOTALL)
+    # Remove debug/step log banners (e.g., "━━━━━ Step X ━━━━━")
+    text = re.sub(r"━+.*Step \d+.*━+", "", text)
+    # Remove any lines that start with "[Step" (which include duration and token info).
+    text = re.sub(r"\[Step \d+: Duration .*", "", text)
+    # Remove lines that mention code snippet instructions.
+    text = re.sub(r"Make sure to include code with the correct pattern.*", "", text)
+    # Finally, remove any remaining lines that seem to be debug logs.
+    lines = text.splitlines()
+    cleaned_lines = [line for line in lines if not re.search(r"Step \d+|Duration", line)]
+    return "\n".join(cleaned_lines).strip()
 
-    messages.append({"role": "user", "content": message})
-
-    response = llama.create_chat_completion_openai_v1(
-        model=model, messages=messages, stream=True
-    )
-
-    text = ""
-    for chunk in response:
-        content = chunk.choices[0].delta.content
-        if content:
-            text += content
-            yield text
+# Use only the final_answer tool.
+final_answer = FinalAnswerTool()
 
+# Set up the model with a reduced token limit.
+model = HfApiModel(
+    max_tokens=1024,
+    temperature=0.5,
+    model_id='Qwen/Qwen1.5-0.5B-Chat-GGUF',
+    custom_role_conversions=None,
+)
 
-js = """function () {
-  gradioURL = window.location.href
-  if (!gradioURL.endsWith('?__theme=dark')) {
-    window.location.replace(gradioURL + '?__theme=dark');
-  }
-}"""
+# Load prompt templates from YAML.
+with open("prompts.yaml", 'r') as stream:
+    prompt_templates = yaml.safe_load(stream)
 
-css = """
-footer {
-    visibility: hidden;
-}
-full-height {
-    height: 100%;
-}
-"""
+# Ensure the final_answer key exists in prompt_templates to prevent KeyError.
+if "final_answer" not in prompt_templates:
+    prompt_templates["final_answer"] = {"pre_messages": "", "post_messages": ""}
 
-with gr.Blocks(theme=gr.themes.Soft(), js=js, css=css, fill_height=True) as demo:
-    gr.ChatInterface(
-        predict,
-        fill_height=True,
-        examples=[
-            "What is the capital of France?",
-            "Who was the first person on the moon?",
-        ],
-    )
+# Initialize CodeAgent with a low verbosity level to reduce extra debug output.
+agent = CodeAgent(
+    model=model,
+    tools=[final_answer],
+    max_steps=4,
+    verbosity_level=0,
+    grammar=None,
+    planning_interval=None,
+    name="Hypertension Prevention Advisor",
+    description=system_prompt,
+    prompt_templates=prompt_templates
+)
 
+def run_agent(user_input):
+    """
+    Runs the agent, then removes any internal chain-of-thought, step logs, and code snippets
+    before returning the final plain-text answer.
+    """
+    raw_response = agent.run(user_input)
+    print("Raw Agent Response:", raw_response)  # Debugging output (optional)
+    
+    if not raw_response.strip():
+        return "I'm sorry, but I couldn't generate a response. Please try again."
+    if "final_answer" not in raw_response.lower():
+        return "Error: The response did not use the `final_answer` tool. Please try again."
+    
+    clean_response = remove_code_snippets(raw_response)
+    words = clean_response.split()
+    if len(set(words)) < 5:
+        return "I'm unable to generate a meaningful response. Please refine your query."
+    return clean_response
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio UI.
+GradioUI(agent).launch()