Update app.py

app.py

@@ -1,65 +1,21 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# ✅ This model supports chat completions through the free Inference API
-MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+client = InferenceClient("microsoft/phi-4")
 
-client = InferenceClient(model=MODEL_NAME)
+def respond(message, history):
+    messages = [{"role": "system", "content": "You are a friendly chatbot"}]
+    if history:
+        messages.extend(history)
+    messages.append({"role": "user", "content": message})
 
-# Example fake recipe database
-recipes = [
-    {
-        "name": "Veggie Pasta",
-        "ingredients": ["pasta", "tomato", "garlic", "olive oil"],
-        "allergies": ["gluten"],
-        "budget": "low"
-    },
-    {
-        "name": "Chicken Stir Fry",
-        "ingredients": ["chicken", "soy sauce", "broccoli", "garlic"],
-        "allergies": ["soy"],
-        "budget": "medium"
-    }
-]
-
-def find_recipes(budget, have_items, allergies):
-    results = []
-    for recipe in recipes:
-        if recipe["budget"] != budget:
-            continue
-        if any(a in recipe["allergies"] for a in allergies):
-            continue
-        if not all(item in have_items for item in recipe["ingredients"]):
-            continue
-        results.append(recipe["name"])
-    return results or ["No matching recipes found."]
-
-def chatbot(message, history, budget, have_items, allergies):
-    recipes_found = find_recipes(budget, have_items.split(","), allergies.split(","))
-    system_prompt = (
-        f"You are a friendly cooking assistant. The user has budget '{budget}', "
-        f"these ingredients: {have_items}, and allergies: {allergies}. "
-        f"Suggest recipes from this list: {recipes_found}."
+    response = client.chat_completion(
+        messages,
+        max_tokens=100
     )
 
-    msgs = [{"role": "system", "content": system_prompt}]
-    for user_msg, bot_msg in history:
-        msgs.append({"role": "user", "content": user_msg})
-        msgs.append({"role": "assistant", "content": bot_msg})
-    msgs.append({"role": "user", "content": message})
-
-    response_text = ""
-    for resp in client.chat_completion(messages=msgs, stream=True):
-        token = resp.choices[0].delta.content or ""
-        response_text += token
-        return response_text
-
-with gr.Blocks() as demo:
-    gr.Markdown("## 🍳 Recipe Suggestion Chatbot")
-
-    budget = gr.Dropdown(["low", "medium", "high"], label="Budget", value="low")
-    have_items = gr.Textbox(label="Ingredients you have (comma separated)", placeholder="pasta,tomato,garlic")
-    allergies = gr.Textbox(label="Allergies (comma separated)", placeholder="gluten,soy")
-    chatbot_ui = gr.ChatInterface(fn=lambda msg, hist: chatbot(msg, hist, budget.value, have_items.value, allergies.value))
+    return response['choices'][0]['message']['content'].strip()
+    
 
-demo.launch()
+chatbot = gr.ChatInterface(respond, type = "messages", title="truth bot")
+chatbot.launch()