| import streamlit as st |
| import chromadb |
| import requests |
| import os |
|
|
| |
| MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.1" |
| API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}" |
|
|
| API_TOKEN = st.secrets["HUGGINGFACEHUB_API_TOKEN"] |
|
|
| |
| headers = { |
| "Authorization": f"Bearer {API_TOKEN}" |
| } |
|
|
| |
| chroma_client = chromadb.PersistentClient(path="chroma_store") |
| collection = chroma_client.get_or_create_collection(name="tech_docs") |
|
|
| |
| def query_huggingface(prompt): |
| payload = { |
| "inputs": prompt, |
| "options": {"wait_for_model": True} |
| } |
| response = requests.post(API_URL, headers=headers, json=payload) |
|
|
| |
| if response.status_code != 200: |
| return f"❌ HF API Error: {response.status_code} - {response.text}" |
|
|
| try: |
| result = response.json() |
| if isinstance(result, list) and "generated_text" in result[0]: |
| return result[0]["generated_text"] |
| elif isinstance(result, dict) and "error" in result: |
| return f"❌ Model Error: {result['error']}" |
| else: |
| return "⚠️ Unexpected model response format." |
| except Exception as e: |
| return f"⚠️ Failed to parse response: {e}" |
|
|
| |
| st.title("💬 Ask Me Anything - Tech RAG Chatbot") |
|
|
| user_query = st.text_input("🔎 Ask your question:") |
|
|
| if user_query: |
| |
| results = collection.query(query_texts=[user_query], n_results=3) |
| context = "\n".join(results["documents"][0]) if results["documents"] else "" |
|
|
| |
| prompt = f"""Answer the question using the context below: |
| |
| Context: |
| {context} |
| |
| Question: |
| {user_query} |
| |
| Answer:""" |
|
|
| |
| with st.spinner("Thinking..."): |
| answer = query_huggingface(prompt) |
|
|
| st.markdown("### 📢 Answer:") |
| st.write(answer) |
|
|
