Create app.py

app.py

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+import streamlit as st
+import time
+import os
+
+# Set page title and description
+st.set_page_config(
+    page_title="IPFS-LLaMA Demo",
+    page_icon="🦙",
+    layout="wide"
+)
+
+st.title("LLaMA-IPFS Demo")
+st.markdown("This app demonstrates using llama-cpp-python with IPFS-hosted models.")
+
+# Install required packages if not already installed
+@st.cache_resource
+def install_dependencies():
+    import subprocess
+    packages = ["llama-cpp-python", "huggingface-hub", "llama-ipfs"]
+    for package in packages:
+        try:
+            __import__(package)
+        except ImportError:
+            st.write(f"Installing {package}...")
+            subprocess.check_call(["pip", "install", package, "--quiet"])
+    
+    # Activate IPFS integration
+    import llama_ipfs
+    llama_ipfs.activate()
+    st.success("✅ Dependencies installed and IPFS integration activated!")
+
+# Display a loading spinner while installing dependencies
+with st.spinner("Setting up environment..."):
+    install_dependencies()
+
+# Load the LLaMA model
+@st.cache_resource
+def load_model(repo_id, filename):
+    from llama_cpp import Llama
+    with st.spinner(f"Loading model from {repo_id}/{filename}..."):
+        st.info("This may take a few minutes for the first run as the model is downloaded from IPFS.")
+        try:
+            model = Llama.from_pretrained(
+                repo_id=repo_id, 
+                filename=filename, 
+                verbose=False
+            )
+            return model
+        except Exception as e:
+            st.error(f"Error loading model: {str(e)}")
+            return None
+
+# Model selection
+model_options = {
+    "GPT-2 (117M)": {
+        "repo_id": "ipfs://bafybeie7quk74kmqg34nl2ewdwmsrlvvt6heayien364gtu2x6g2qpznhq",
+        "filename": "ggml-model-Q4_K_M.gguf"
+    }
+}
+
+# Sidebar for model selection and parameters
+st.sidebar.title("Model Settings")
+
+selected_model = st.sidebar.selectbox(
+    "Select Model",
+    list(model_options.keys()),
+    index=0
+)
+
+# Load the selected model
+model_info = model_options[selected_model]
+llm = load_model(model_info["repo_id"], model_info["filename"])
+
+# Generation parameters
+st.sidebar.title("Generation Settings")
+temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.0, 0.1)
+max_tokens = st.sidebar.slider("Max Output Tokens", 10, 100, 30)
+top_p = st.sidebar.slider("Top P", 0.1, 1.0, 0.95, 0.05)
+repeat_penalty = st.sidebar.slider("Repeat Penalty", 1.0, 2.0, 1.0, 0.1)
+
+# Main content area
+st.header("Question & Answer")
+
+# Input fields
+context = st.text_area(
+    "Context",
+    value="France is a country in Western Europe known for its rich history and culture. "
+    "It is home to many famous landmarks including the Eiffel Tower. Its capital is Paris.",
+    height=150
+)
+
+question = st.text_input("Question", value="What is the capital of France?")
+
+# Generate button
+if st.button("Generate Answer"):
+    if llm is not None:
+        prompt = f"Context: {context}\nQuestion: {question}\nBased solely on the above context, answer the question in one word:"
+        
+        # Show the prompt
+        with st.expander("Prompt"):
+            st.text(prompt)
+        
+        # Generate the answer with progress
+        with st.spinner("Generating answer..."):
+            start_time = time.time()
+            output = llm(
+                prompt,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                repeat_penalty=repeat_penalty,
+                stop=["\n"]
+            )
+            end_time = time.time()
+            
+            # Extract and display the answer
+            answer = output['choices'][0]['text'].strip()
+            
+            st.success(f"Generated in {end_time - start_time:.2f} seconds")
+            
+            # Display the answer
+            st.header("Answer")
+            st.write(answer)
+    else:
+        st.error("Model not loaded. Please check the error message above.")
+
+# Information about the app
+st.markdown("---")
+st.markdown("""
+### How It Works
+
+The `llama-ipfs` package patches the llama-cpp-python library to:
+
+1. Recognize `ipfs://` URIs as valid model identifiers
+2. Download model files from IPFS nodes or gateways
+3. Cache models locally for faster loading in subsequent runs
+
+This allows you to load models from a decentralized network without changing any of your existing code!
+""")