Update app.py

app.py

@@ -1,137 +1,42 @@
-from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
-import datetime
-import requests
-import pytz
-import yaml
-from tools.final_answer import FinalAnswerTool
-from playwright.sync_api import sync_playwright
-from bs4 import BeautifulSoup
-import pandas as pd
-import time
-
-from Gradio_UI import GradioUI
-
-# Below is an example of a tool that does nothing. Amaze us with your creativity !
-@tool
-def scrape_drug_reviews(drug:str)-> str: #it's import to specify the return type
-    #Keep this format for the description / args / args description but feel free to modify the tool
-    """A tool that calls a scraping function on the drugs.com website to scrape for reviews on target dtug specified in input
-    Args:
-        drug: the name of the target drug we want to retrieve reviews for, in lower case (e.g. 'flecainide')
-        
-    """
-    try:
-        data = scrape_drugs_com_reviews(drug)
-        # Get current time in that timezone
-        return data.to_string() 
-    except Exception as e:
-        return f"Error fetching reviews for the target drug you provided: '{drug}'"
-
-
-async def scrape_drugs_com_reviews(drug_name, max_pages=3, delay=2):
-    """
-    Scrapes user reviews from Drugs.com for a given drug.
-    """
-    base_url = f"https://www.drugs.com/comments/{drug_name}/"
-    all_reviews = []
-
-    async with async_playwright() as p:
-        browser = await p.chromium.launch(headless=False)
-        context = await browser.new_context(
-            user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121 Safari/537.36",
-            locale="en-US",
-            viewport={'width': 1280, 'height': 800},
-            device_scale_factor=1,
-            is_mobile=False,
-            has_touch=False
-        )
-        page = await context.new_page()
-
-        for page_num in range(1, max_pages + 1):
-            url = base_url if page_num == 1 else f"{base_url}?page={page_num}"
-            print(f"Scraping: {url}")
-            await page.goto(url, timeout=60000)
-            await asyncio.sleep(delay)  # Give page some time to load
-
-            html = await page.content()
-            await asyncio.sleep(delay)  # Give page some time to load
-            soup = BeautifulSoup(html, 'html.parser')
-            print(soup)
-            review_blocks = soup.find_all('div', class_='ddc-comment ddc-box ddc-mgb-2')
-
-            if not review_blocks:
-                print("No reviews found on this page.")
-                break
-
-            for block in review_blocks:
-                review_paragraph = block.find('p')
-                if review_paragraph:
-                    # Remove the <b> tag from the paragraph to isolate the review text
-                    if review_paragraph.b:
-                        review_paragraph.b.extract()  # Removes <b> so it doesn't show up in the text
-                        # Get the cleaned text
-                        review_text = review_paragraph.get_text(strip=True)
-
-
-                all_reviews.append({
-
-                    "review": review_text if review_text else None,
-                    "source": url
-                })
-
-            await asyncio.sleep(delay)
-
-        await browser.close()
-    return pd.DataFrame(all_reviews)
+import gradio as gr
+from transformers import pipeline
 
 
+from Gradio_UI import GradioUI
 
-@tool
-def get_current_time_in_timezone(timezone: str) -> str:
-    """A tool that fetches the current local time in a specified timezone.
-    Args:
-        timezone: A string representing a valid timezone (e.g., 'America/New_York').
-    """
-    try:
-        # Create timezone object
-        tz = pytz.timezone(timezone)
-        # Get current time in that timezone
-        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
-        return f"The current local time in {timezone} is: {local_time}"
-    except Exception as e:
-        return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
 
 final_answer = FinalAnswerTool()
 
-# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
+# Load your fine-tuned model from Hugging Face Hub
+model = pipeline("text2text-generation", model='unica/CLiMA')  # Replace with your actual model repo name
 
-model = HfApiModel(
-max_tokens=2096,
-temperature=0.5,
-model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-custom_role_conversions=None,
-)
 
+# Define your prompt template (customize as needed)
+def format_prompt(user_input):
+    return f"Identify causal relations in the following clinical narrative:\n\n{user_input}\n\nCausal relations:"  # Modify if your model uses a different template
 
-# Import tool from Hub
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
+# Define prediction function
+def generate_relations(text):
+    prompt = format_prompt(text)
+    result = model(prompt, max_length=512, do_sample=False)
+    return result[0]['generated_text']
 
-with open("prompts.yaml", 'r') as stream:
-    prompt_templates = yaml.safe_load(stream)
     
-agent = CodeAgent(
-    model=model,
-    tools=[scrape_drug_reviews,final_answer], ## add your tools here (don't remove final answer)
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
-    name=None,
-    description=None,
-    prompt_templates=prompt_templates
-)
 
+# Gradio interface
+demo = gr.Interface(
+    fn=generate_relations,
+    inputs=gr.Textbox(lines=10, label="Clinical Note or Drug Review Text"),
+    outputs=gr.Textbox(label="Extracted Causal Relations"),
+    title="Causal Relation Extractor with MedLlama",
+    description="Paste your clinical note or drug review. This AI agent extracts drug-condition or symptom causal relations using a fine-tuned LLM.",
+    examples=[
+        ["Patient reported severe headaches after starting amitriptyline."],
+        ["Lisinopril helped reduce the patient's blood pressure but caused persistent cough."],
+        ["After using Metformin, the patient experienced gastrointestinal discomfort."]
+    ]
+)
 
-GradioUI(agent).launch()
+# Launch the app
+demo.launch()