Update app.py

app.py

@@ -1,64 +1,130 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+import asyncio
+from deepgram import DeepgramClientOptions, DeepgramClient
+from pydub import AudioSegment
+from pydub.playback import play
+import io
+import os
+import time
+import re
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import LLMChain
+import httpx
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+class LanguageModelProcessor:
+    def __init__(self):
+        self.llm = ChatGroq(temperature=0, model_name="llama3-8b-8192", groq_api_key=os.getenv("GROQ_API_KEY"))
+        self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+        with open('system_prompt.txt', 'r') as file:
+            system_prompt = file.read().strip()
+        self.prompt = ChatPromptTemplate.from_messages([
+            SystemMessagePromptTemplate.from_template(system_prompt),
+            ChatPromptTemplate.MessagesPlaceholder(variable_name="chat_history"),
+            ChatPromptTemplate.HumanMessagePromptTemplate.from_template("{text}")
+        ])
+        self.conversation = LLMChain(
+            llm=self.llm,
+            prompt=self.prompt,
+            memory=self.memory
+        )
+
+    def process(self, text):
+        self.memory.chat_memory.add_user_message(text)
+        start_time = time.time()
+        response = self.conversation.invoke({"text": text})
+        end_time = time.time()
+        self.memory.chat_memory.add_ai_message(response['text'])
+        elapsed_time = int((end_time - start_time) * 1000)
+        return response['text']
+
+
+class TextToSpeech:
+    DG_API_KEY = os.getenv("DEEPGRAM_API_KEY")
+    MODEL_NAME = "aura-asteria-en"
+
+    def speak(self, text):
+        DEEPGRAM_URL = f"https://api.deepgram.com/v1/speak?model={self.MODEL_NAME}"
+        headers = {
+            "Authorization": f"Token {self.DG_API_KEY}",
+            "Content-Type": "application/json"
+        }
+        payload = {"text": text}
+        response = requests.post(DEEPGRAM_URL, headers=headers, json=payload)
+        if response.status_code == 200:
+            audio = AudioSegment.from_mp3(io.BytesIO(response.content))
+            play(audio)
+        else:
+            print(f"Error: {response.status_code}, {response.text}")
+
+
+class ConversationManager:
+    def __init__(self):
+        self.llm = LanguageModelProcessor()
+        self.tts = TextToSpeech()
+        self.commodity = None
+        self.state = None
+        self.district = None
+
+    def run_conversation(self, transcription_response):
+        if not self.commodity or not self.state or not self.district:
+            llm_response = self.llm.process(transcription_response)
+            details = self.extract_details_from_llm_response(llm_response)
+            if details == 'No details.':
+                self.tts.speak(llm_response)
+                return llm_response
+        else:
+            return self.fetch_commodity_prices()
+
+    def extract_details_from_llm_response(self, response):
+        pattern = r'\[commodity, state, district\] = \[([^,]+), ([^,]+), ([^\]]+)\]'
+        match = re.search(pattern, response)
+        if match:
+            self.commodity = match.group(1)
+            self.state = match.group(2)
+            self.district = match.group(3)
+            return f"Commodity: {self.commodity}, State: {self.state}, District: {self.district}"
+        else:
+            return "No details."
+
+    async def fetch_commodity_prices(self):
+        return "Fetching commodity prices..."
+
+
+def gradio_interface(input_text):
+    # Instantiate the Conversation Manager
+    manager = ConversationManager()
+    # Process the input and return response
+    response = manager.run_conversation(input_text)
+    return response
+
+
+# Gradio UI Definition
+def gradio_app():
+    # Define Gradio Interface
+    with gr.Blocks() as demo:
+        gr.Markdown("# Commodity Price Chatbot with LLM and Speech")
+
+        with gr.Row():
+            input_box = gr.Textbox(label="Speak/Type your message", placeholder="Enter your message here...")
+            output_box = gr.Textbox(label="Response", interactive=False)
+
+        with gr.Row():
+            mic_button = gr.Audio(source="microphone", type="microphone", label="Use microphone")
+            submit_button = gr.Button("Submit")
+
+        submit_button.click(fn=gradio_interface, inputs=input_box, outputs=output_box)
+        mic_button.change(fn=gradio_interface, inputs=mic_button, outputs=output_box)
+
+    return demo
 
 
 if __name__ == "__main__":
-    demo.launch()
+    app = gradio_app()
+    app.launch()
+