Update app.py

app.py

@@ -1,27 +1,196 @@
-import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-def load_model():
-    tokenizer = AutoTokenizer.from_pretrained("quantized_model")
-    model = AutoModelForCausalLM.from_pretrained(
-        "quantized_model",
-        device_map="auto",
-        torch_dtype=torch.bfloat16,
-    )
-    return tokenizer, model
-
-tokenizer, model = load_model()
-
-st.title("Quantized Model Inference")
-
-user_input = st.text_input("Enter your prompt:")
-
-if st.button("Generate"):
-    if user_input:
-        inputs = tokenizer(user_input, return_tensors="pt").to("cuda")
-        outputs = model.generate(**inputs)
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        st.write(f"Response: {response}")
-    else:
-        st.write("Please enter a prompt.")
+import streamlit as st 
+from crewai import Agent, Task, Crew, LLM
+from crewai_tools import SerperDevTool
+from gtts import gTTS
+import speech_recognition as sr
+import os 
+
+gemini_key=os.getenv("gemini_api")
+
+
+serper_key=os.getenv("serper_api")
+
+search_tool = SerperDevTool()
+
+recognizer = sr.Recognizer()
+
+def recognize_speech():
+    with sr.Microphone() as source:
+        st.info("Listening... Speak now!")
+        recognizer.adjust_for_ambient_noise(source)
+        try:
+            audio = recognizer.listen(source, timeout=5,stream=False)
+            text = recognizer.recognize_google(audio)
+            return text
+        except sr.UnknownValueError:
+            return "Sorry, could not understand the audio."
+        except sr.RequestError:
+            return "Could not request results, check your internet."
+def text_input():
+    text= st.text_input("Enter the answer")
+    return (text)
+    
+
+# Initialize LLM
+llm = LLM(model="gemini/gemini-1.5-flash",
+          verbose=True,
+          temperature=0.5,
+          api_key=gemini_key)
+
+
+# Define Agents
+question_agent = Agent(
+    role="interviewer",
+    goal="Frame {number} questions based on the {job_description}",
+    verbose=False,
+    memory=True,
+    backstory="You need to frame interview questions based on the job description.",
+    llm=llm,
+    tools=[search_tool], 
+    allow_delegation=True
+)
+
+generator_agent = Agent(
+    role="answer generator",
+    goal="frame answer to {question} based on the {job_description}.",
+    verbose=False,
+    memory=True,
+    backstory="you are expert in answering the question based on {job_description}.",
+    llm=llm,
+    tools=[search_tool],
+    allow_delegation=True
+)
+
+evaluation_agent = Agent(
+    role="evulation",
+    goal="frame answer to {question} based on the {job_description}.",
+    verbose=False,
+    memory=True,
+    backstory="you are expert in answering the question based on {job_description}.",
+    llm=llm,
+    tools=[search_tool],
+    allow_delegation=True
+)
+
+
+# Define Tasks
+question_task = Task(
+    description="Generate {number}  interview questions based on the  {job_description} .",
+    expected_output="A list of {number} questions.",
+    tools=[search_tool],
+    agent=question_agent
+)
+
+generator_task = Task(
+    description="frame the answer to the {question} based on the {job_description}.",
+    expected_output="Correct if the answer is right; otherwise, return the correct answer.",
+    tools=[search_tool],
+    agent=generator_agent
+)
+
+
+evaluation_task = Task(
+    description="frame the answer to the {question} based on the {job_description}.",
+    expected_output="Correct if the answer is right; otherwise, return the correct answer.",
+    tools=[search_tool],
+    agent=generator_agent
+)
+
+# Define Crews
+crew1 = Crew(agents=[question_agent], tasks=[question_task])
+crew2 = Crew(agents=[generator_agent], tasks=[generator_task])
+crew3 = Crew(agents=[evaluation_agent],tasks=[evaluation_task])
+
+# Initialize Streamlit App
+st.title("Interview Preparation Bot")
+
+# *Reset function to clear all session variables*
+def reset_session():
+    st.session_state.job_description = ""
+    st.session_state.number=1
+    st.session_state.questions = []
+    st.session_state.current_question_index = 0
+    st.session_state.answers = []
+    st.session_state.evaluations = []
+    st.session_state.completed = False
+
+# Initialize session state variables
+if "questions" not in st.session_state:
+    reset_session()
+
+if "number" not in st.session_state:
+    st.session_state.number = 1
+
+# User Input
+st.session_state.job_description = st.text_input("Enter the Topic you need Practice", value=st.session_state.job_description)
+st.session_state.number = st.number_input("Enter the Number of Question you need",min_value=1,value=st.session_state.number)
+
+# Button to Generate Questions
+if st.button("Start") and st.session_state.job_description and st.session_state.number:
+    result = crew1.kickoff(inputs={"job_description": st.session_state.job_description,'number':st.session_state.number})
+    st.session_state.questions = [q.strip(' ```') for q in result.raw.split("\n") if q.strip()]
+    
+    st.session_state.current_question_index = 0
+    st.session_state.answers = []
+    st.session_state.evaluations = []
+    st.session_state.completed = False
+    
+# # Display Questions One by One
+if st.session_state.questions and st.session_state.current_question_index < len(st.session_state.questions):
+    question = st.session_state.questions[st.session_state.current_question_index]
+    st.write(f"*Question {st.session_state.current_question_index + 1}:* {question}")
+    tts = gTTS(question, lang="en")
+    tts.save("response.mp3")
+    audio_file = open("response.mp3", "rb")
+    audio_bytes = audio_file.read()
+    st.audio(audio_bytes, format="audio/mp3")
+    
+    # User enters answer
+    type=st.radio("select the type",["Answer with voice ","Answer by type"])    
+
+    if type=="Answer with voice ":
+        output=""
+        
+        if st.button("Start Recording"):
+            output = recognize_speech()
+            st.write("Transcription: ", output)
+            
+
+    elif type=="Answer by type":
+        
+        output=st.text_input(f"Enter your answer for Question ")
+        
+        
+    # st.session_state.answers .append(st.session_state.current_question_index + 1)
+
+    answer=answer = f"{st.session_state.current_question_index + 1}{output}"
+
+        
+    # answer = st.text_input(f"Enter your answer for Question {st.session_state.current_question_index + 1}")
+
+    if st.button("Submit Answer") and answer:
+        # Evaluate Answer
+        result2 = crew2.kickoff(inputs={"job_description": st.session_state.job_description, "question": question, "answer": answer})
+        evaluation_result = result2.raw
+
+        # Store answer and evaluation
+        st.session_state.answers.append(answer)
+        st.session_state.evaluations.append(evaluation_result)
+
+        # Move to the next question
+        st.session_state.current_question_index += 1
+
+        # If all questions are answered, mark as completed
+        if st.session_state.current_question_index == len(st.session_state.questions)-1:
+            st.session_state.completed = True
+col1,col2=st.columns(2)
+# Show Evaluations After All Answers
+if st.session_state.completed:
+    with col2:
+        if st.button("Review The Answers"):
+            st.write("### Final Evaluation:")
+            for i in range(len(st.session_state.questions)-1):
+                st.write(f"*Q{i+1}:* {st.session_state.questions[i]}")
+                st.write(f"*Your Answer:* {st.session_state.answers[i]}")
+                st.write(f"*Evaluation:* {st.session_state.evaluations[i]}")
+                st.write("---")