add missing part of app.py

app.py

@@ -3,6 +3,7 @@ from llama_cpp import Llama
 from huggingface_hub import hf_hub_download
 import os
 import gc
+import shutil
 
 # Available models
 MODELS = {
@@ -24,22 +25,22 @@ MODELS = {
     "Meta-Llama-3.1-8B-Instruct (Q2_K)": {
         "repo_id": "MaziyarPanahi/Meta-Llama-3.1-8B-Instruct-GGUF",
         "filename": "Meta-Llama-3.1-8B-Instruct.Q2_K.gguf",
-        "description": "Meta Llama 3.1 8B Instruct (Q2_K)"
+        "description": "Meta-Llama-3.1-8B-Instruct (Q2_K)"
     },
     "DeepSeek-R1-Distill-Llama-8B (Q2_K)": {
         "repo_id": "unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF",
         "filename": "DeepSeek-R1-Distill-Llama-8B-Q2_K.gguf",
-        "description": "DeepSeek R1 Distill Llama 8B (Q2_K)"
+        "description": "DeepSeek-R1-Distill-Llama-8B (Q2_K)"
     },
     "Mistral-7B-Instruct-v0.3 (IQ3_XS)": {
         "repo_id": "MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF",
         "filename": "Mistral-7B-Instruct-v0.3.IQ3_XS.gguf",
-        "description": "Mistral 7B Instruct v0.3 (IQ3_XS)"
+        "description": "Mistral-7B-Instruct-v0.3 (IQ3_XS)"
     },
     "Qwen2.5-Coder-7B-Instruct (Q2_K)": {
         "repo_id": "Qwen/Qwen2.5-Coder-7B-Instruct-GGUF",
         "filename": "qwen2.5-coder-7b-instruct-q2_k.gguf",
-        "description": "Qwen2.5 Coder 7B Instruct (Q2_K)"
+        "description": "Qwen2.5-Coder-7B-Instruct (Q2_K)"
     },
 }
 
@@ -54,10 +55,34 @@ with st.sidebar:
     top_p = st.slider("Top-P", 0.1, 1.0, 0.95)
     repeat_penalty = st.slider("Repetition Penalty", 1.0, 2.0, 1.1)
 
+    if st.button("🧹 Clear All Cached Models"):
+        try:
+            for f in os.listdir("models"):
+                if f.endswith(".gguf"):
+                    os.remove(os.path.join("models", f))
+            st.success("Model cache cleared.")
+        except Exception as e:
+            st.error(f"Failed to clear models: {e}")
+
+    if st.button("📦 Show Disk Usage"):
+        try:
+            usage = shutil.disk_usage(".")
+            used = usage.used / (1024**3)
+            free = usage.free / (1024**3)
+            st.info(f"Disk Used: {used:.2f} GB | Free: {free:.2f} GB")
+        except Exception as e:
+            st.error(f"Disk usage error: {e}")
+
 # Model info
 selected_model = MODELS[selected_model_name]
 model_path = os.path.join("models", selected_model["filename"])
 
+# Init state
+if "model_name" not in st.session_state:
+    st.session_state.model_name = None
+if "llm" not in st.session_state:
+    st.session_state.llm = None
+
 # Ensure model directory exists
 os.makedirs("models", exist_ok=True)
 
@@ -70,7 +95,6 @@ def cleanup_old_models():
             except Exception as e:
                 st.warning(f"Couldn't delete old model {f}: {e}")
 
-# Function to download the selected model
 def download_model():
     with st.spinner(f"Downloading {selected_model['filename']}..."):
         hf_hub_download(
@@ -80,26 +104,86 @@ def download_model():
             local_dir_use_symlinks=False,
         )
 
-# Function to validate or download the model
+def try_load_model(path):
+    try:
+        return Llama(model_path=path, n_ctx=1024, n_threads=2, n_threads_batch=2, n_batch=4, n_gpu_layers=0, use_mlock=False, use_mmap=True, verbose=False)
+    except Exception as e:
+        return str(e)
+
 def validate_or_download_model():
     if not os.path.exists(model_path):
         cleanup_old_models()
         download_model()
-    try:
-        # Attempt to load the model with minimal resources to validate
-        _ = Llama(model_path=model_path, n_ctx=16, n_threads=1)
-    except Exception as e:
-        st.warning(f"Model file was invalid or corrupt: {e}\nRedownloading...")
+
+    # First load attempt
+    result = try_load_model(model_path)
+    if isinstance(result, str):
+        st.warning(f"Initial load failed: {result}\nAttempting re-download...")
         try:
             os.remove(model_path)
         except:
             pass
         cleanup_old_models()
         download_model()
-
-# Validate or download the selected model
-validate_or_download_model()
+        result = try_load_model(model_path)
+        if isinstance(result, str):
+            st.error(f"Model still failed after re-download: {result}")
+            st.stop()
+        return result
+    return result
 
 # Load model if changed
-if "model_name" not in st.session_state or st.session_state.model_name != selected_model_name:
-    if "llm" in st.session_state and st.session_state.llm
+if st.session_state.model_name != selected_model_name:
+    if st.session_state.llm is not None:
+        del st.session_state.llm
+        gc.collect()
+    st.session_state.llm = validate_or_download_model()
+    st.session_state.model_name = selected_model_name
+
+llm = st.session_state.llm
+
+# Chat history state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+st.title(f"🧠 {selected_model['description']} (Streamlit + GGUF)")
+st.caption(f"Powered by `llama.cpp` | Model: {selected_model['filename']}")
+
+user_input = st.chat_input("Ask something...")
+
+if user_input:
+    # Prevent appending user message if assistant hasn't replied yet
+    if len(st.session_state.chat_history) % 2 == 1:
+        st.warning("Please wait for the assistant to respond before sending another message.")
+    else:
+        st.session_state.chat_history.append({"role": "user", "content": user_input})
+
+        with st.chat_message("user"):
+            st.markdown(user_input)
+
+        # Trim conversation history to max 8 turns (user+assistant)
+        MAX_TURNS = 8
+        trimmed_history = st.session_state.chat_history[-MAX_TURNS * 2:]
+        messages = [{"role": "system", "content": system_prompt}] + trimmed_history
+
+        with st.chat_message("assistant"):
+            full_response = ""
+            response_area = st.empty()
+
+            stream = llm.create_chat_completion(
+                messages=messages,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                top_k=top_k,
+                top_p=top_p,
+                repeat_penalty=repeat_penalty,
+                stream=True,
+            )
+
+            for chunk in stream:
+                if "choices" in chunk:
+                    delta = chunk["choices"][0]["delta"].get("content", "")
+                    full_response += delta
+                    response_area.markdown(full_response)
+
+            st.session_state.chat_history.append({"role": "assistant", "content": full_response})