Create app.py

app.py

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+import streamlit as st
+import openai
+import torch
+from diffusers import StableVideoDiffusionPipeline
+import tempfile
+import cv2
+import os
+from langchain.vectorstores import FAISS
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+
+# Configure OpenAI API (Replace 'your-api-key' with an actual key)
+openai.api_key = "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"
+
+# Load Stable Video Diffusion Model (Optimized for Performance)
+@st.cache_resource
+def load_video_model():
+    model_id = "stabilityai/stable-video-diffusion-img2vid"
+    pipe = StableVideoDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+    pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+    return pipe
+
+pipe = load_video_model()
+
+# Load RAG Components
+@st.cache_resource
+def load_rag():
+    loader = TextLoader("knowledge_base.txt")  # Ensure you have a knowledge base file
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    texts = text_splitter.split_documents(documents)
+    embeddings = OpenAIEmbeddings()
+    vectorstore = FAISS.from_documents(texts, embeddings)
+    retriever = vectorstore.as_retriever()
+    return RetrievalQA.from_chain_type(llm=openai.ChatCompletion, retriever=retriever)
+
+rag_chain = load_rag()
+
+# Streamlit UI Configuration
+st.set_page_config(page_title="LiteGPT - Chat & Video AI", layout="wide")
+st.title("💬 LiteGPT - Chat & Video AI")
+
+# Chatbot Function with RAG
+@st.cache_resource
+def chat_with_gpt(prompt):
+    response = rag_chain.run(prompt)
+    return response
+
+# Sidebar - Video Generation
+st.sidebar.header("🎥 AI Video Generator")
+video_prompt = st.sidebar.text_area("Enter a prompt for video generation")
+if st.sidebar.button("Generate Video"):
+    if video_prompt:
+        with st.spinner("Generating video..."):
+            video_frames = pipe(video_prompt, num_inference_steps=50).frames
+            video_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+            height, width, _ = video_frames[0].shape
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+            out = cv2.VideoWriter(video_path, fourcc, 8, (width, height))
+            for frame in video_frames:
+                out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+            out.release()
+            st.sidebar.video(video_path)
+    else:
+        st.sidebar.warning("Please enter a video prompt!")
+
+# Chat Interface
+st.subheader("💡 Chat with LiteGPT")
+user_input = st.text_input("Type your message:")
+if st.button("Send"):
+    if user_input:
+        response = chat_with_gpt(user_input)
+        st.write("🤖 LiteGPT:", response)
+    else:
+        st.warning("Please enter a message!")