multi round dialoging

.gitignore

@@ -1 +1 @@
-BACKUP.py
+CODE*

app.py

@@ -2,12 +2,28 @@ import os
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Setup clients for each provider
+# Clients for each model provider
 llama_client = InferenceClient(provider="sambanova", api_key=os.environ["HF_TOKEN"])
 minimax_client = InferenceClient(provider="novita", api_key=os.environ["HF_TOKEN"])
 mistral_client = InferenceClient(provider="together", api_key=os.environ["HF_TOKEN"])
 
-# Context-aware response function
+# Format chat history for Markdown display
+def format_chat_history(chat_history):
+    formatted = ""
+    for msg in chat_history:
+        role = msg["role"]
+        content = msg["content"]
+        if isinstance(content, list):  # For LLaMA image+text input
+            for item in content:
+                if "text" in item:
+                    formatted += f"**{role.capitalize()}:** {item['text']}\n\n"
+                elif "image_url" in item:
+                    formatted += f"**{role.capitalize()}:** 🖼️ Image: {item['image_url']['url']}\n\n"
+        else:
+            formatted += f"**{role.capitalize()}:** {content}\n\n"
+    return formatted.strip()
+
+# Main chat handler
 def chat_with_model(model_choice, prompt, image_url, chat_history):
     if not prompt:
         return "Please enter a text prompt.", chat_history
@@ -29,7 +45,6 @@ def chat_with_model(model_choice, prompt, image_url, chat_history):
             )
             bot_msg = response.choices[0].message.content
             chat_history.append({"role": "assistant", "content": bot_msg})
-            return bot_msg, chat_history
 
         # === MiniMax ===
         elif model_choice == "MiniMax M1 (Novita)":
@@ -40,7 +55,6 @@ def chat_with_model(model_choice, prompt, image_url, chat_history):
             )
             bot_msg = response.choices[0].message.content
             chat_history.append({"role": "assistant", "content": bot_msg})
-            return bot_msg, chat_history
 
         # === Mistral ===
         elif model_choice == "Mistral Mixtral-8x7B (Together)":
@@ -51,18 +65,19 @@ def chat_with_model(model_choice, prompt, image_url, chat_history):
             )
             bot_msg = response.choices[0].message.content
             chat_history.append({"role": "assistant", "content": bot_msg})
-            return bot_msg, chat_history
 
         else:
             return "Unsupported model selected.", chat_history
 
+        return format_chat_history(chat_history), chat_history
+
     except Exception as e:
         return f"Error: {e}", chat_history
 
-# Gradio UI
+# Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("## 🤖 Multi-Model Context-Aware Chatbot")
-    gr.Markdown("Supports LLaMA 4 (with optional image), MiniMax, and Mistral. Conversation memory is preserved.")
+    gr.Markdown("Supports LLaMA 4 (with optional image), MiniMax, and Mistral. Memory is preserved for multi-turn dialog.")
 
     model_dropdown = gr.Dropdown(
         choices=[
@@ -77,10 +92,9 @@ with gr.Blocks() as demo:
     prompt_input = gr.Textbox(label="Text Prompt", placeholder="Ask something...", lines=2)
     image_url_input = gr.Textbox(label="Optional Image URL (for LLaMA only)", placeholder="https://example.com/image.jpg")
 
-    submit_btn = gr.Button("Generate Response")
+    submit_btn = gr.Button("💬 Generate Response")
     reset_btn = gr.Button("🔄 Reset Conversation")
-    output_box = gr.Textbox(label="Response", lines=8)
-
+    output_box = gr.Markdown(label="Chat History", value="")
     state = gr.State([])
 
     submit_btn.click(
@@ -90,7 +104,7 @@ with gr.Blocks() as demo:
     )
 
     reset_btn.click(
-        fn=lambda: ("Conversation reset. You can start a new one.", []),
+        fn=lambda: ("🧹 Conversation reset. You can start a new one.", []),
         inputs=[],
         outputs=[output_box, state]
     )