app.py

import gradio as gr
import pandas as pd
import re
from huggingface_hub import InferenceClient
import plotly.express as px
from collections import Counter

# Initialize Hugging Face client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def parse_message(message):
    """Extract information from a chat message using regex."""
    info = {}
    
    # Extract timestamp and phone number
    timestamp_match = re.search(r'\[(.*?)\]', message)
    phone_match = re.search(r'\] (.*?):', message)
    
    if timestamp_match and phone_match:
        info['timestamp'] = timestamp_match.group(1)
        info['phone'] = phone_match.group(1)
        
        # Extract rest of the message
        content = message.split(':', 1)[1].strip()
        
        # Extract name
        name_match = re.match(r'^([^•\n-]+)', content)
        if name_match:
            info['name'] = name_match.group(1).strip()
            
        # Extract affiliation
        affiliation_match = re.search(r'[Aa]ffiliation:?\s*([^•\n]+)', content)
        if affiliation_match:
            info['affiliation'] = affiliation_match.group(1).strip()
            
        # Extract research field/interests
        field_match = re.search(r'([Ff]ield of [Ii]nterest|[Dd]omaine de recherche|[Rr]esearch area|[Aa]reas of interest):?\s*([^•\n]+)', content)
        if field_match:
            info['research_field'] = field_match.group(2).strip()
            
        # Extract thesis topic
        thesis_match = re.search(r'[Tt]hesis:?\s*([^•\n]+)', content)
        if thesis_match:
            info['thesis_topic'] = thesis_match.group(1).strip()
    
    return info

def create_researcher_df(chat_history):
    """Convert chat messages to structured DataFrame."""
    researchers = []
    messages = chat_history.split('\n')
    
    for message in messages:
        if message.strip():
            info = parse_message(message)
            if info:
                researchers.append(info)
    
    df = pd.DataFrame(researchers)
    return df

def analyze_research_fields(df):
    """Analyze and categorize research fields."""
    if 'research_field' not in df.columns:
        return pd.Series()
        
    fields = df['research_field'].dropna()
    # Split fields and flatten
    all_fields = [field.strip().lower() for fields_list in fields for field in fields_list.split(',')]
    return pd.Series(Counter(all_fields))

def create_visualizations(df):
    """Create visualizations from the researcher data."""
    figures = []
    
    # 1. Affiliation Distribution
    if 'affiliation' in df.columns and not df['affiliation'].empty:
        affiliation_counts = df['affiliation'].value_counts()
        fig_affiliation = px.pie(
            values=affiliation_counts.values,
            names=affiliation_counts.index,
            title='Distribution of Researchers by Affiliation'
        )
        figures.append(fig_affiliation)
    
    # 2. Research Fields Analysis
    field_counts = analyze_research_fields(df)
    if not field_counts.empty:
        fig_fields = px.bar(
            x=field_counts.index,
            y=field_counts.values,
            title='Popular Research Fields',
            labels={'x': 'Field', 'y': 'Count'}
        )
        figures.append(fig_fields)
        
    return figures[0] if figures else None

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    chat_history_text=""
):
    """Enhanced response function with data analysis capabilities."""
    try:
        # Process chat history if provided
        if chat_history_text:
            df = create_researcher_df(chat_history_text)
            
            # Generate analysis summary
            summary = f"Analysis of {len(df)} researchers:\n"
            if 'affiliation' in df.columns:
                summary += f"- Institutions represented: {df['affiliation'].nunique()}\n"
            
            field_counts = analyze_research_fields(df)
            if not field_counts.empty:
                top_fields = field_counts.nlargest(3)
                summary += "- Top research fields:\n"
                for field, count in top_fields.items():
                    summary += f"  • {field}: {count} researchers\n"
            
            # Add analysis to message
            message += f"\n\nCommunity Analysis:\n{summary}"
        
        # Generate response using the LLM
        messages = [{"role": "system", "content": system_message}]
        for val in history:
            if val[0]:
                messages.append({"role": "user", "content": val[0]})
            if val[1]:
                messages.append({"role": "assistant", "content": val[1]})
        
        messages.append({"role": "user", "content": message})
        
        response = ""
        for token in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token_content = token.choices[0].delta.content
            response += token_content
            yield response
            
    except Exception as e:
        yield f"Error: {str(e)}"

# Create Gradio interface
demo = gr.Interface(
    fn=respond,
    inputs=[
        gr.Textbox(label="Message"),
        gr.State([]),  # history
        gr.Textbox(value="You are a friendly Research Community Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
        gr.Textbox(label="Chat History", lines=10)
    ],
    outputs=[
        gr.Textbox(label="Response"),
        gr.Plot(label="Community Analysis")
    ],
    title="CohortBot",
    description="A chatbot that analyzes research community data and provides visualizations."
)

if __name__ == "__main__":
    demo.launch()