feat: select model from list of available ollama models

ollama/ollama_app.py

@@ -4,27 +4,38 @@
 import json
 import time
 
-def make_api_call(messages, max_tokens, is_final_answer=False):
+
+def make_api_call(messages, max_tokens, model, is_final_answer=False):
     for attempt in range(3):
         try:
             response = ollama.chat(
-                model="llama3.1:70b",
+                model=model,
                 messages=messages,
-                options={"temperature":0.2, "max_length":max_tokens},
-                format='json',
+                options={"temperature": 0.2, "max_length": max_tokens},
+                format="json",
             )
-            return json.loads(response['message']['content'])
+            return json.loads(response["message"]["content"])
         except Exception as e:
             if attempt == 2:
                 if is_final_answer:
-                    return {"title": "Error", "content": f"Failed to generate final answer after 3 attempts. Error: {str(e)}"}
+                    return {
+                        "title": "Error",
+                        "content": f"Failed to generate final answer after 3 attempts. Error: {str(e)}",
+                    }
                 else:
-                    return {"title": "Error", "content": f"Failed to generate step after 3 attempts. Error: {str(e)}", "next_action": "final_answer"}
+                    return {
+                        "title": "Error",
+                        "content": f"Failed to generate step after 3 attempts. Error: {str(e)}",
+                        "next_action": "final_answer",
+                    }
             time.sleep(1)  # Wait for 1 second before retrying
 
-def generate_response(prompt):
+
+def generate_response(prompt, model, max_tokens):
     messages = [
-        {"role": "system", "content": """You are an expert AI assistant that explains your reasoning step by step. For each step, provide a title that describes what you're doing in that step, along with the content. Decide if you need another step or if you're ready to give the final answer. Respond in JSON format with 'title', 'content', and 'next_action' (either 'continue' or 'final_answer') keys. USE AS MANY REASONING STEPS AS POSSIBLE. AT LEAST 3. BE AWARE OF YOUR LIMITATIONS AS AN LLM AND WHAT YOU CAN AND CANNOT DO. IN YOUR REASONING, INCLUDE EXPLORATION OF ALTERNATIVE ANSWERS. CONSIDER YOU MAY BE WRONG, AND IF YOU ARE WRONG IN YOUR REASONING, WHERE IT WOULD BE. FULLY TEST ALL OTHER POSSIBILITIES. YOU CAN BE WRONG. WHEN YOU SAY YOU ARE RE-EXAMINING, ACTUALLY RE-EXAMINE, AND USE ANOTHER APPROACH TO DO SO. DO NOT JUST SAY YOU ARE RE-EXAMINING. USE AT LEAST 3 METHODS TO DERIVE THE ANSWER. USE BEST PRACTICES.
+        {
+            "role": "system",
+            "content": """You are an expert AI assistant that explains your reasoning step by step. For each step, provide a title that describes what you're doing in that step, along with the content. Decide if you need another step or if you're ready to give the final answer. Respond in JSON format with 'title', 'content', and 'next_action' (either 'continue' or 'final_answer') keys. USE AS MANY REASONING STEPS AS POSSIBLE. AT LEAST 3. BE AWARE OF YOUR LIMITATIONS AS AN LLM AND WHAT YOU CAN AND CANNOT DO. IN YOUR REASONING, INCLUDE EXPLORATION OF ALTERNATIVE ANSWERS. CONSIDER YOU MAY BE WRONG, AND IF YOU ARE WRONG IN YOUR REASONING, WHERE IT WOULD BE. FULLY TEST ALL OTHER POSSIBILITIES. YOU CAN BE WRONG. WHEN YOU SAY YOU ARE RE-EXAMINING, ACTUALLY RE-EXAMINE, AND USE ANOTHER APPROACH TO DO SO. DO NOT JUST SAY YOU ARE RE-EXAMINING. USE AT LEAST 3 METHODS TO DERIVE THE ANSWER. USE BEST PRACTICES.
 
 Example of a valid JSON response:
 ```json
@@ -33,82 +44,129 @@ def generate_response(prompt):
     "content": "To begin solving this problem, we need to carefully examine the given information and identify the crucial elements that will guide our solution process. This involves...",
     "next_action": "continue"
 }```
-"""},
+""",
+        },
         {"role": "user", "content": prompt},
-        {"role": "assistant", "content": "Thank you! I will now think step by step following my instructions, starting at the beginning after decomposing the problem."}
+        {
+            "role": "assistant",
+            "content": "Thank you! I will now think step by step following my instructions, starting at the beginning after decomposing the problem.",
+        },
     ]
-    
+
     steps = []
     step_count = 1
     total_thinking_time = 0
-    
+
     while True:
         start_time = time.time()
-        step_data = make_api_call(messages, 300)
+        step_data = make_api_call(messages, max_tokens, model)
         end_time = time.time()
         thinking_time = end_time - start_time
         total_thinking_time += thinking_time
-
-        steps.append((f"Step {step_count}: {step_data['title']}", step_data['content'], thinking_time))
-
+
+        steps.append(
+            (
+                f"Step {step_count}: {step_data['title']}",
+                step_data["content"],
+                thinking_time,
+            )
+        )
+
         messages.append({"role": "assistant", "content": json.dumps(step_data)})
-
-        if step_data['next_action'] == 'final_answer' or step_count > 25: # Maximum of 25 steps to prevent infinite thinking time. Can be adjusted.
+
+        if (
+            step_data["next_action"] == "final_answer" or step_count > 25
+        ):  # Maximum of 25 steps to prevent infinite thinking time. Can be adjusted.
             break
-        
+
         step_count += 1
 
         # Yield after each step for Streamlit to update
         yield steps, None  # We're not yielding the total time until the end
 
     # Generate final answer
-    messages.append({"role": "user", "content": "Please provide the final answer based on your reasoning above."})
-
+    messages.append(
+        {
+            "role": "user",
+            "content": "Please provide the final answer based on your reasoning above.",
+        }
+    )
+
     start_time = time.time()
     final_data = make_api_call(messages, 200, is_final_answer=True)
     end_time = time.time()
     thinking_time = end_time - start_time
     total_thinking_time += thinking_time
-    
-    steps.append(("Final Answer", final_data['content'], thinking_time))
+
+    steps.append(("Final Answer", final_data["content"], thinking_time))
 
     yield steps, total_thinking_time
 
+
+def get_models():
+    models_dict = ollama.list().items()
+    models = []
+    for _, value in models_dict:
+        for model in value:
+            models.append(model["model"])
+    return models
+
+
 def main():
     st.set_page_config(page_title="g1 prototype", page_icon="🧠", layout="wide")
-
-    st.title("g1: Using Llama-3.1 70b on Groq to create o1-like reasoning chains")
-
-    st.markdown("""
+
+    st.title("g1: Using Ollama models to create o1-like reasoning chains")
+
+    st.markdown(
+        """
     This is an early prototype of using prompting to create o1-like reasoning chains to improve output accuracy. It is not perfect and accuracy has yet to be formally evaluated. It is powered by Ollama.
-                
+
     Open source [repository here](https://github.com/bklieger-groq)
-    """)
-
+    """
+    )
+
+    models = get_models()
+    model = st.selectbox("Select an Ollama model:", models)
+    max_tokens = st.slider(
+        "Max tokens for each step:", min_value=256, max_value=8192, value=512
+    )
+
     # Text input for user query
-    user_query = st.text_input("Enter your query:", placeholder="e.g., How many 'R's are in the word strawberry?")
-
+    user_query = st.text_input(
+        "Enter your query:",
+        placeholder="e.g., How many 'R's are in the word strawberry?",
+    )
+
     if user_query:
         st.write("Generating response...")
-        
+
         # Create empty elements to hold the generated text and total time
         response_container = st.empty()
         time_container = st.empty()
-        
+
         # Generate and display the response
-        for steps, total_thinking_time in generate_response(user_query):
+        for steps, total_thinking_time in generate_response(
+            user_query, model, max_tokens
+        ):
             with response_container.container():
                 for i, (title, content, thinking_time) in enumerate(steps):
                     if title.startswith("Final Answer"):
                         st.markdown(f"### {title}")
-                        st.markdown(content.replace('\n', '<br>'), unsafe_allow_html=True)
+                        st.markdown(
+                            content.replace("\n", "<br>"), unsafe_allow_html=True
+                        )
                     else:
                         with st.expander(title, expanded=True):
-                            st.markdown(content.replace('\n', '<br>'), unsafe_allow_html=True)
-
+                            st.markdown(
+                                content.replace("\n", "<br>"), unsafe_allow_html=True
+                            )
+
             # Only show total time when it's available at the end
             if total_thinking_time is not None:
-                time_container.markdown(f"**Total thinking time: {total_thinking_time:.2f} seconds**")
+                time_container.markdown(
+                    f"**Total thinking time: {total_thinking_time:.2f} seconds**"
+                )
+
 
 if __name__ == "__main__":
     main()