Create app.py

app.py

@@ -0,0 +1,109 @@
+from huggingface_hub import InferenceClient
+import gradio as gr
+from langchain_community.tools import DuckDuckGoSearchRun
+
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+# Initialize DuckDuckGo search tool
+duckduckgo_search = DuckDuckGoSearchRun()
+
+def format_prompt(message, history):
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+
+    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+    
+    # Generate response using model
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+
+    for response in stream:
+        output += response.token.text
+        # Yield model's response first
+        yield output
+        
+    # Now, perform DuckDuckGo search and yield results
+    search_result = duckduckgo_search.run(prompt)
+    if search_result:
+        yield search_result
+    else:
+        yield "Sorry, I couldn't find any relevant information."
+
+additional_inputs = [
+    gr.Textbox(
+        label="System Prompt",
+        max_lines=1,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=256,
+        minimum=0,
+        maximum=1048,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.0,
+        maximum=1,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    )
+]
+
+examples = [
+    ["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None],
+    ["What are some best tourist places to visit in Lajpat nagar, Delhi?", None, None, None, None, None],
+    ["Ronaldo or Messi?", None, None, None, None, None],
+    ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None],
+]
+
+gr.ChatInterface(
+    fn=generate,
+    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    additional_inputs=additional_inputs,
+    title="🤗 friday2.0 🤗 WELCOME TO OPEN-SOURCE FREEDOM",
+    examples=examples,
+    concurrency_limit=20,
+).launch(show_api=False, share=True)