got chatbot working

.gitignore

@@ -0,0 +1 @@
+.env

app.py

@@ -1,31 +1,58 @@
+import os
 import gradio as gr
-from langchain_huggingface import HuggingFacePipeline
-
-llm = HuggingFacePipeline.from_model_id(
-    model_id="openai-community/gpt2",
-    task="text-generation",
-    pipeline_kwargs={
-        "max_new_tokens": 5,
-        "temperature": 1,
-    },
-)
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+
+# Function to handle responses
+def respond(user_input, history):
+    # Configure Hugging Face model endpoint
+    llm = HuggingFaceEndpoint(
+        repo_id="HuggingFaceTB/SmolLM2-1.7B-Instruct",
+        task="text-generation",
+        temperature=0.5,
+        repetition_penalty=1.03,
+        huggingfacehub_api_token=os.getenv('HUGGING_FACE_TOKEN')
+    )
+    chat = ChatHuggingFace(llm=llm, verbose=True)
+
+    # Prepare system message to set the context
+    system_message = "You are playing a word guessing game. Guess ONE word based on previous guesses and avoid repeats. DO NOT ANSWER WITH MORE THAN ONE WORD."
+
+    # Track guessed words from history
+    last_human_message, last_ai_message = history[-1] if history else ("", "")
+    # guessed_words_summary = "Past guesses: " + ", ".join() if guessed_words else "No past guesses."
+    # print(guessed_words_summary)
+    # Add messages to the conversation
+    messages = [
+        SystemMessage(content=system_message),
+        SystemMessage(content="Last human message: " + last_human_message + " | Last AI message: " + last_ai_message),
+    ]
+
+    # Load past conversation into messages
+    for human, ai in history:
+        messages.append(HumanMessage(content=human))
+        messages.append(AIMessage(content=ai))
+
+    # Add the current user input
+    messages.append(HumanMessage(content=user_input))
+
+    # Initialize response as an empty string
+    response = ""
+
+    # Stream response from the model
+    for message in chat.stream(messages):
+        if isinstance(message, str):
+            response += message
+        else:
+            response += message.content
+
+        yield response
 
-def greet(name):
-    return "Hello " + name + "!!"
-
-# Define a function to generate responses
-def chat_with_model(user_input):
-    print(llm.invoke(user_input))
-    return llm.invoke(user_input)
-
-# Create a Gradio interface
-demo = gr.Interface(
-    fn=chat_with_model,
-    inputs="text",
-    outputs="text",
-    title="Chat with GPT-2",
-    description="Enter a prompt and get a response from GPT-2."
+# Set up the Gradio interface
+demo = gr.ChatInterface(
+    respond,
+    title="WordSync",
+    description="A word guessing game where you and the AI try to guess the same word. The AI remembers past guesses!"
 )
 
-# Launch the interface
-demo.launch(share=True)
+demo.launch(share=True)

requirements.txt

@@ -1,2 +1,3 @@
 gradio
+langchain-core
 langchain-huggingface