Update app.py

app.py

@@ -3,48 +3,34 @@ from llama_cpp import Llama
 from huggingface_hub import snapshot_download
 import os
 
-# Define the model repository and filename
+st.title("GGUF Model Streamlit App")
+
+st.write("Downloading the model...")
 repo_id = "Divyansh12/check"
-filename = "unsloth.F16.gguf"
+model_dir = "model/Divyansh12/check"
 
-# Download the model if not already present
-model_dir = "model"
+# Download the model
 if not os.path.exists(model_dir):
-    st.write("Downloading the model...")
     snapshot_download(repo_id=repo_id, cache_dir=model_dir)
     st.write("Model downloaded successfully!")
 
-# Since the model file is directly inside the downloaded directory
-# Find the exact file path
-model_path = os.path.join(model_dir, filename)
+# List downloaded files for debugging
+downloaded_files = os.listdir(model_dir)
+st.write("Downloaded model files:", downloaded_files)
 
-# Check if the file exists at the specified location
+model_path = os.path.join(model_dir, "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}")
-
-    # Load the GGUF model using llama-cpp
-    st.write("Loading the model...")
-    llm = Llama.from_pretrained(
-        model_path=model_path
-    )
+    llm = Llama.from_pretrained(model_path=model_path)
     st.write("Model loaded successfully!")
 
-    # Streamlit input for the user to enter a prompt
-    prompt = st.text_input("Enter your prompt:", "What is the capital of France?")
+    # Example query
+    response = llm.create_chat_completion(
+        messages=[
+            {"role": "user", "content": "What is the capital of France?"}
+        ]
+    )
+    st.write("Response:", response)
 
-    # Generate the response when a prompt is given
-    if st.button("Generate Response"):
-        with st.spinner("Generating..."):
-            response = llm.create_chat_completion(
-                messages=[
-                    {
-                        "role": "user",
-                        "content": prompt
-                    }
-                ]
-            )
-            # Extract the message content from the response and display it
-            st.write("Response:")
-            st.write(response['choices'][0]['message']['content'])