Update app.py

app.py

@@ -1,38 +1,70 @@
 import streamlit as st
 from llama_cpp import Llama
-from huggingface_hub import snapshot_download
-import os
-
-st.title("GGUF Model Streamlit App")
-
-st.write("Downloading the model...")
-repo_id = "Divyansh12/check"
-model_dir = "model"
-
-# Download the model
-if not os.path.exists(model_dir):
-    snapshot_download(repo_id=repo_id, cache_dir=model_dir)
-    st.write("Model downloaded successfully!")
-
-# List downloaded files for debugging
-for root, dirs, files in os.walk(model_dir):
-    st.write(f"Files in {root}: {files}")
-
-# Update this path based on the actual file structure found
-model_path = "model/models--Divyansh12--check/unsloth.F16.gguf"
-if not os.path.exists(model_path):
-    st.error(f"Model file not found at {model_path}")
-else:
-    st.write(f"Found model file at {model_path}")
-    llm = Llama.from_pretrained(model_path=model_path)
-    st.write("Model loaded successfully!")
-
-    # Example query
-    response = llm.create_chat_completion(
-        messages=[
-            {"role": "user", "content": "What is the capital of France?"}
-        ]
+
+# Initialize the model
+llm = Llama.from_pretrained(
+    repo_id="Divyansh12/check",
+    filename="unsloth.F16.gguf",  # Ensure this matches your model file name
+    verbose=True,
+    n_ctx=32768,
+    n_threads=2,
+    chat_format="chatml"
+)
+
+# Define the function to get responses from the model
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}]
+
+    for user_message, assistant_message in history:
+        if user_message:
+            messages.append({"role": "user", "content": user_message})
+        if assistant_message:
+            messages.append({"role": "assistant", "content": assistant_message})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+    # Stream the response from the model
+    response_stream = llm.create_chat_completion(
+        messages=messages,
+        stream=True,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p
     )
-    st.write("Response:", response)
+
+    for chunk in response_stream:
+        if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
+            response += chunk['choices'][0]["delta"]["content"]
+            yield response
+
+# Streamlit UI
+st.title("Chatbot Application")
+st.write("### Interact with the chatbot!")
+
+# User input fields
+system_message = st.text_input("System Message", value="You are a friendly Chatbot.")
+user_message = st.text_area("Your Message:")
+max_tokens = st.slider("Max New Tokens", min_value=1, max_value=2048, value=512)
+temperature = st.slider("Temperature", min_value=0.1, max_value=4.0, value=0.7)
+top_p = st.slider("Top-p (Nucleus Sampling)", min_value=0.1, max_value=1.0, value=0.95)
+
+# Chat history
+if 'history' not in st.session_state:
+    st.session_state.history = []
+
+if st.button("Send"):
+    # Get the response from the model
+    response = respond(user_message, st.session_state.history, system_message, max_tokens, temperature, top_p)
+    
+    # Add user message and model response to history
+    st.session_state.history.append((user_message, response))
+
+    # Display the chat history
+    st.write("### Chat History")
+    for user_msg, assistant_msg in st.session_state.history:
+        st.write(f"**User:** {user_msg}")
+        st.write(f"**Assistant:** {assistant_msg}")
+