Create app.py

app.py

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+import streamlit as st
+import os
+import time
+import PyPDF2
+from docx import Document
+import pandas as pd
+from dotenv import load_dotenv
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load environment variables
+load_dotenv()
+
+# Avatars and bios
+USER_AVATAR = "https://raw.githubusercontent.com/achilela/vila_fofoka_analysis/9904d9a0d445ab0488cf7395cb863cce7621d897/USER_AVATAR.png"
+BOT_AVATAR = "https://raw.githubusercontent.com/achilela/vila_fofoka_analysis/991f4c6e4e1dc7a8e24876ca5aae5228bcdb4dba/Ataliba_Avatar.jpg"
+
+ATALIBA_BIO = """
+**I am Ataliba Miguel's Digital Twin** 🤖
+
+**Background:**
+- 🎓 Mechanical Engineering (BSc)
+- ⛽ Oil & Gas Engineering (MSc Specialization)
+- 🔧 17+ years in Oil & Gas Industry
+- 🔍 Current: Topside Inspection Methods Engineer @ TotalEnergies
+- 🤖 AI Practitioner Specialist
+- 🚀 Founder of ValonyLabs (AI solutions for industrial corrosion, retail analytics, and KPI monitoring)
+
+**Capabilities:**
+- Technical document analysis
+- Engineering insights
+- AI-powered problem solving
+- Cross-domain knowledge integration
+
+Ask me about engineering challenges, AI applications, or industry best practices!
+"""
+
+# UI Setup
+st.markdown("""
+    <style>
+    @import url('https://fonts.cdnfonts.com/css/tw-cen-mt');
+    * { font-family: 'Tw Cen MT', sans-serif; }
+    .st-emotion-cache-1y4p8pa { padding: 2rem 1rem; }
+    </style>
+""", unsafe_allow_html=True)
+
+st.title("🚀 Ataliba o Agent Nerdx 🚀")
+
+# Sidebar
+with st.sidebar:
+    st.header("⚡️ Hugging Face Model Loaded")
+    st.markdown("Model: amiguel/unsloth_finetune_test")
+    uploaded_file = st.file_uploader("Upload technical documents", type=["pdf", "docx", "xlsx", "xlsm"])
+
+# Session state
+if "file_context" not in st.session_state:
+    st.session_state.file_context = None
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# File parser
+def parse_file(file):
+    try:
+        if file.type == "application/pdf":
+            reader = PyPDF2.PdfReader(file)
+            return "\n".join([page.extract_text() for page in reader.pages])
+        elif file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
+            doc = Document(file)
+            return "\n".join([para.text for para in doc.paragraphs])
+        elif file.type in ["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "application/vnd.ms-excel"]:
+            df = pd.read_excel(file)
+            return df.to_string()
+    except Exception as e:
+        st.error(f"Error processing file: {str(e)}")
+        return None
+
+# Process file
+if uploaded_file and not st.session_state.file_context:
+    st.session_state.file_context = parse_file(uploaded_file)
+    if st.session_state.file_context:
+        st.sidebar.success("✅ Document loaded successfully")
+
+# Load model
+@st.cache_resource
+def load_custom_model():
+    model_name = "amiguel/unsloth_finetune_test"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    return pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Generate response
+def generate_response(prompt):
+    bio_triggers = ['who are you', 'ataliba', 'yourself', 'skilled at', 
+                    'background', 'experience', 'valonylabs', 'totalenergies']
+    
+    if any(trigger in prompt.lower() for trigger in bio_triggers):
+        for line in ATALIBA_BIO.split('\n'):
+            yield line + '\n'
+            time.sleep(0.1)
+        return
+
+    try:
+        classifier = load_custom_model()
+        result = classifier(prompt)[0]
+        label = result['label']
+        score = result['score']
+        context = st.session_state.file_context or "No document loaded."
+        
+        response_text = f"\n📘 **Prediction**: `{label}`\n📊 **Confidence**: `{score:.2%}`\n🗂️ **Context**: `{context[:300]}...`"
+        for line in response_text.split('\n'):
+            yield line + '\n'
+            time.sleep(0.1)
+
+    except Exception as e:
+        yield f"⚠️ Model Error: {str(e)}"
+
+# Chat interface
+for msg in st.session_state.chat_history:
+    with st.chat_message(msg["role"], avatar=USER_AVATAR if msg["role"] == "user" else BOT_AVATAR):
+        st.markdown(msg["content"])
+
+if prompt := st.chat_input("Ask about documents or technical matters..."):
+    st.session_state.chat_history.append({"role": "user", "content": prompt})
+    with st.chat_message("user", avatar=USER_AVATAR):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant", avatar=BOT_AVATAR):
+        response_placeholder = st.empty()
+        full_response = ""
+
+        for chunk in generate_response(prompt):
+            full_response += chunk
+            response_placeholder.markdown(full_response + "▌")
+
+        response_placeholder.markdown(full_response)
+        st.session_state.chat_history.append({"role": "assistant", "content": full_response})