Update app.py

app.py

@@ -6,84 +6,126 @@ from langchain_openai import AzureChatOpenAI
 from pypdf import PdfReader
 import os
 import gradio as gr
-
-chat = AzureChatOpenAI(azure_deployment = "GPT-3")
-
-def extract_text( pdf_path):
-    # creating a pdf reader object
-    reader = PdfReader(pdf_path)
-    all_text = ""
-
-    for page in reader.pages:
-        all_text += page.extract_text()
-    return all_text
-
-def get_response( candidate, chat_history, resume, jd):
-    
-    resume = extract_text(resume.name)
-    jd = extract_text(jd.name)
-
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            (
-                "system",
-                """Your Task is Perform as intelligent interviewer, Your Task is ask question to the resume's candidate by following candidate Answer.
-                  at the end exit with greeting to the candidate.
-                **Ask question follow up on the candidate response. get chat history.**
-                """,
-            ),
-            MessagesPlaceholder(variable_name="messages"),
-        ]
-    )
-
-    chain = prompt | chat  
-
-    # chat_histroy_prompt = chat_history
-
-    answer = chain.invoke(
-        {
-            "messages": [
-                HumanMessage(
-                    content=f" job description :{jd}\n Resume :{resume}"
+from openai import OpenAI
+from gtts import gTTS
+from IPython.display import Audio
+import requests
+
+
+hf_token = os.getenv("HF_TOKEN")
+API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3"
+headers = {"Authorization": f"Bearer {hf_token}"}
+
+class ScreeningAssistant:
+    def __init__(self):
+        self.chat = AzureChatOpenAI(azure_deployment = "GPT4")
+        # self.client = OpenAI()
+
+    def extract_text(self, pdf_path):
+        # creating a pdf reader object
+        reader = PdfReader(pdf_path)
+        all_text = ""
+
+        for page in reader.pages:
+            all_text += page.extract_text()
+        return all_text
+
+    def audio_to_text(self, audio_path):
+        with open(audio_path, "rb") as f:
+            data = f.read()
+        response = requests.post(API_URL, headers=headers, data=data)
+        print(response.json()['text'])
+        return response.json()['text']
+
+
+    def text_to_audio(self, mytext):
+        # Language in which you want to convert
+        language = 'en'
+        # have a high speed
+        myobj = gTTS(text=mytext, lang=language, slow=False)
+        
+        audio_path = "welcome.mp3"
+        # Saving the converted audio in a mp3 file named
+        # welcome 
+        myobj.save(audio_path)
+
+        # Audio(filename=audio_path, autoplay=True)
+
+        # os.remove(audio_path)
+        return audio_path
+
+    def get_response(self, audio_path, chat_history, resume, jd):
+        
+        candidate = self.audio_to_text(audio_path)
+        resume = self.extract_text(resume.name)
+        jd = self.extract_text(jd.name)
+
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """Your Task is Perform as intelligent interviewer, Your Task is ask question to the resume's candidate by following candidate Answer.
+                      at the end exit with greeting to the candidate.
+                    **Ask question follow up on the candidate response. get chat history.**
+                    """,
                 ),
-                AIMessage(content=f"""Perform as intelligent interviewer, Your Task is ask question to the resume's candidate by following candidate Answer.
-                 chat history : {chat_history}"""),
-                HumanMessage(content=candidate),
-            ],
-        }
-    )
-    # print("INTERVIEWER :", answer.content)
-    # chat_history.append({"candidate":candidate,"interviewer":answer.content })
-
-    result = answer.content
-    chat_history.append((candidate, result))
-    print("chat_history", chat_history)
-    return "", chat_history
-
-def gradio_interface() -> None:
-    """Create a Gradio interface for the chatbot."""
-    with gr.Blocks(css = "style.css" ,theme="HaleyCH/HaleyCH_Theme") as demo:
-
-        gr.HTML("""<center class="darkblue" text-align:center;padding:30px;'><center>
-        <center><h1 class ="center" style="color:#fff">ADOPLE AI</h1></center>
-        <br><center><h1 style="color:#fff">Screening Assistant Chatbot</h1></center>""")
-
-        chatbot = gr.Chatbot()
-
-        with gr.Row():
-            with gr.Column(scale=1):    
-                msg = gr.Textbox(label="Question")
-      
-        with gr.Row():
-            with gr.Column(scale=0.15):
-                resume = gr.File(label="Resume")
-            with gr.Column(scale=0.15):
-                jd = gr.File(label="Job Description")
-            with gr.Column(scale=0.85):
-                clear = gr.ClearButton([msg, chatbot])
-
-        msg.submit(get_response, [msg, chatbot, resume, jd], [msg, chatbot])
-
-    demo.launch(debug =True, share=True)   
-
-gradio_interface() 
+                MessagesPlaceholder(variable_name="messages"),
+            ]
+        )
+
+        chain = prompt | self.chat  
+
+        # chat_histroy_prompt = chat_history
+
+        answer = chain.invoke(
+            {
+                "messages": [
+                    HumanMessage(
+                        content=f" job description :{jd}\n Resume :{resume}"
+                    ),
+                    AIMessage(content=f"""Perform as intelligent interviewer, Your Task is ask question to the resume's candidate by following candidate Answer.
+                    chat history : {chat_history}"""),
+                    HumanMessage(content=candidate),
+                ],
+            }
+        )
+
+        result = answer.content
+        chat_history.append((candidate, result))
+        print("chat_history", chat_history)
+        audio_output = self.text_to_audio(result)
+        return "", chat_history, audio_output
+
+    def gradio_interface(self) -> None:
+        """Create a Gradio interface for the chatbot."""
+        with gr.Blocks(css = "style.css" ,theme="HaleyCH/HaleyCH_Theme") as demo:
+
+            gr.HTML("""<center class="darkblue" text-align:center;padding:30px;'><center>
+            <center><h1 class ="center" style="color:#fff">ADOPLE AI</h1></center>
+            <br><center><h1 style="color:#fff">Screening Assistant Chatbot</h1></center>""")
+
+            chatbot = gr.Chatbot()
+
+            with gr.Row():
+                with gr.Column(scale=1):    
+                    msg = gr.Textbox(label="Question", show_label=False)
+            with gr.Row():
+                with gr.Column(scale=0.50):
+                    audio_path = gr.Audio(sources=["microphone"], type="filepath")
+                with gr.Column(scale=0.50):
+                    play_audio = gr.Audio( value=None, autoplay=True)                    
+            with gr.Row():
+                with gr.Column(scale=0.25):
+                    resume = gr.File(label="Resume")
+                with gr.Column(scale=0.25):
+                    jd = gr.File(label="Job Description")
+                with gr.Column(scale=0.50):
+                    clear = gr.ClearButton([chatbot])
+
+            audio_path.stop_recording(self.get_response, [audio_path, chatbot, resume, jd], [msg, chatbot, play_audio])
+
+        demo.launch(debug =True, share=True)   
+
+if __name__=="__main__":
+    assistant = ScreeningAssistant()
+    assistant.gradio_interface()