Create app.py

app.py

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+import os
+import requests
+import gradio as gr
+from cerebras.cloud.sdk import Cerebras
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.schema import Document
+import numpy as np
+from langchain_community.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from sentence_transformers import SentenceTransformer
+
+# Initialize Cerebras API client
+
+Facts = os.getenv("Facto")
+client = Cerebras(api_key= Facts)
+
+Newskey = os.getenv("News")
+# Function to fetch latest news articles from NewsAPI
+def get_latest_news(query):
+    api_key = Newskey
+    url = f"https://newsapi.org/v2/everything?q={query}&apiKey={api_key}"
+    response = requests.get(url)
+    data = response.json()
+    return [(article["title"], article["url"], article["source"]["name"]) for article in data.get("articles", [])[:5]]
+
+# Function to update fact_checks.txt with new user input (overwrites previous content)
+def update_fact_checks_file(query):
+    with open("fact_checks.txt", "w", encoding="utf-8") as file:
+        file.write(f"{query}\n")
+
+# Function to create a FAISS retriever dynamically
+def create_faiss_retriever():
+    if not os.path.exists("fact_checks.txt"):
+        open("fact_checks.txt", "w").close()  # Create file if it doesn't exist
+    
+    loader = TextLoader("fact_checks.txt")
+    documents = loader.load()
+    
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=50)
+    docs = text_splitter.split_documents(documents)
+    
+    embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = FAISS.from_documents(docs, embedding_model)
+    
+    return vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 8})
+
+# Function to clear the fact_checks.txt file after execution
+def clear_fact_checks_file():
+    open("fact_checks.txt", "w").close()
+
+# Function to perform fact-checking with Llama 3.3
+def fact_check_with_llama3(query):
+    # Save query to fact_checks.txt
+    update_fact_checks_file(query)
+
+    # Reload FAISS index with new data
+    retriever = create_faiss_retriever()
+
+    # Retrieve relevant facts from FAISS
+    retrieved_docs = retriever.invoke(query)
+    retrieved_texts = [doc.page_content for doc in retrieved_docs]
+
+    # Fetch real-time news
+    news = get_latest_news(query)
+
+    # Combine all retrieved context
+    context_text = "\n".join(retrieved_texts)
+    
+    # Construct prompt for Llama 3.3
+    prompt = f"""
+    Claim: {query}
+    Context: {context_text}
+    Based on the provided context, determine whether the claim is True, False, or Misleading. Provide a concise explanation and cite relevant sources. Don't mention any instance of your knowledge cut-off.
+    """
+    
+    # Call Llama 3.3 API
+    stream = client.chat.completions.create(
+        messages=[{"role": "system", "content": prompt}],
+        model="llama-3.3-70b",
+        stream=True,
+        max_completion_tokens=512,
+        temperature=0.2,
+        top_p=1
+    )
+    
+    # Generate AI response
+    result = "".join(chunk.choices[0].delta.content or "" for chunk in stream)
+    
+    # Format results with sources
+    sources = "\n".join([f"{title} ({source}): {url}" for title, url, source in news])
+
+    # Clear the file after execution
+    clear_fact_checks_file()
+
+    return result, sources if sources else "No relevant sources found."
+
+# Gradio Interface
+def fact_check_interface(query):
+    response, sources = fact_check_with_llama3(query)
+    return response, sources
+
+gui = gr.Interface(
+    fn=fact_check_interface,
+    inputs=gr.Textbox(placeholder="Enter a claim to fact-check"),
+    outputs=[gr.Textbox(label="Fact-Check Result"), gr.Textbox(label="Sources")],
+    title="Facto - AI Fact-Checking System",
+    description="Enter a claim, and the system will verify it using Llama 3.3 and external knowledge sources, citing relevant sources."
+)
+
+gui.launch(debug=True)