Update app.py

app.py

@@ -1,142 +1,25 @@
 import streamlit as st
-import openai
-from pytube import YouTube
+import requests
 import os
-import tempfile
-from fpdf import FPDF
-# Set your OpenAI API key
-openai_api_key = os.environ.get("OPENAI_API_KEY")
-openai.api_key = openai_api_key
 
-def generate_sentiment_score(input_text, parameters):
-    prompt = f'''
-    You are an experienced interview reviewer and consultant for a reputable company. Your role is to evaluate the sentiment displayed by job candidates during their interviews based on the transcripts of their responses.
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
+headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
 
-    The hiring team has provided you with an interview transcript and has asked you to analyze the candidate's sentiment for the following parameters: {', '.join(parameters)}. Your assessment will help the team make more informed hiring decisions and identify candidates who demonstrate genuine positive sentiment towards the role and the company.
-
-    The parameters to evaluate are:
-    {', '.join(parameters)}.
-
-    To complete this task, you will:
-
-    1. Carefully review the provided interview transcript.
-    2. Consider phrases, word choices, or patterns of speech that convey positive or negative sentiment for each parameter.
-    3. Based on your analysis, provide a sentiment score on a scale of 1-5 for each parameter, with 1 being extremely negative and 5 being extremely positive.
-
-    Provide your scores in the format: Parameter: Score.
-    '''
-
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        temperature=0,
-        top_p=0.7,
-        messages=[
-            {
-                "role": "system",
-                "content": prompt
-            },
-            {
-                "role": "user",
-                "content": input_text
-            }
-        ]
-    )
-    return response['choices'][0]['message']['content']
-
-def generate_detailed_feedback(input_text, parameters):
-    prompt = f'''
-    As an experienced interview reviewer, provide a detailed analysis of the candidate's responses based on the following parameters: {', '.join(parameters)}. 
-    
-    Include specific examples, quotes, and adjectives from the transcript that support your analysis. Offer actionable insights and recommendations for the hiring team to make informed decisions. Summarize the candidate's overall sentiment and demeanor.
-    '''
-
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        temperature=0,
-        top_p=0.7,
-        messages=[
-            {
-                "role": "system",
-                "content": prompt
-            },
-            {
-                "role": "user",
-                "content": input_text
-            }
-        ]
-    )
-    return response['choices'][0]['message']['content']
-
-def transcript(video_link):
-    try:
-        # Create a YouTube object
-        yt = YouTube(video_link)
-
-        # Choose the stream with the desired quality and resolution
-        stream = yt.streams.filter(only_audio=True).first()
-
-        # Download the video to a temporary location
-        temp_file_path = stream.download()
-
-        print(f"Video '{yt.title}.mp4' downloaded successfully!")
-
-        # Transcribe the video using OpenAI's Whisper
-        with open(temp_file_path, 'rb') as audio_data:
-            total_transcript = (openai.Audio.transcribe("whisper-1", audio_data))["text"]
-            print("Done with the video processing\n")
-
-        # Remove the temporary file
-        os.remove(temp_file_path)
-
-        return total_transcript
-
-    except Exception as e:
-        print(f"Error: {e}")
-        return None
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
 def main():
-    st.set_page_config(page_title="Insight Hire", page_icon=":bar_chart:")
     st.title("Insight Hire")
     st.write("Analyze interview transcripts or videos to gain valuable insights into candidate sentiment.")
 
-    st.sidebar.markdown("## About")
-    st.sidebar.markdown("""
-    <div style='color: #1f77b4; font-weight: bold;'>Streamline Your Interview Evaluation</div>
-    - Get data-driven sentiment scores for key parameters
-    - Identify top candidates based on sentiment analysis
-    - Make informed hiring decisions with actionable insights
-    """, unsafe_allow_html=True)
-
-    st.sidebar.markdown("<hr>", unsafe_allow_html=True)  # Horizontal separator
-
-    st.sidebar.markdown("## Tips")
-    st.sidebar.markdown("""
-    <div style='color: #2ca02c; font-weight: bold;'>📝 Input Preparation</div>
-    - Provide clear interview transcripts or valid video links
-    - Specify relevant parameters for sentiment analysis
-    """, unsafe_allow_html=True)
-
-    st.sidebar.markdown("<hr>", unsafe_allow_html=True)  # Horizontal separator
-
-    st.sidebar.markdown("## About Me")
-    st.sidebar.markdown("""
-    <div style='color: #d62728; font-weight: bold;'>👋 Hi, I'm Dhruv!</div>
-    I want to make a real impact in the field of AI/ML . My main interest lies in model building and deployment. I'm passionate about leveraging cutting-edge technologies to solve real-world problems.
-    """, unsafe_allow_html=True)
-
     input_option = st.radio("Select input type", ("Text", "YouTube Video Link"))
 
-    input_text = ""
     if input_option == "Text":
         input_text = st.text_area("Enter the interview transcript:")
     else:
         video_link = st.text_input("Enter the YouTube video link:")
-        if video_link:
-            with st.spinner("Processing video..."):
-                input_text = transcript(video_link)
-                if not input_text:
-                    st.error("Error processing the video. Please try again with a different link.")
-                    return
+        input_text = "Mock transcript from YouTube"  # Replace with actual transcript function
 
     parameters = st.text_input("Enter the parameters for sentiment analysis (comma-separated):", "Enthusiasm, Communication Skills, Technical Knowledge")
     parameters = [param.strip() for param in parameters.split(",")]
@@ -144,66 +27,18 @@ def main():
     if st.button("Analyze"):
         if input_text and parameters:
             with st.spinner("Generating sentiment scores..."):
-                sentiment_scores = generate_sentiment_score(input_text, parameters)
-                sentiment_scores = sentiment_scores.strip().split("\n")
-
+                payload = {
+                    "inputs": f"You are an experienced interview reviewer and consultant for a reputable company. Your role is to evaluate the sentiment displayed by job candidates during their interviews based on the transcripts of their responses. The hiring team has provided you with an interview transcript and has asked you to analyze the candidate's sentiment for the following parameters: {', '.join(parameters)}.",
+                    "parameters": parameters
+                }
+                sentiment_scores = query(payload)
                 st.subheader("Sentiment Scores")
-                valid_scores = []
-                for score in sentiment_scores:
-                    if ":" in score:
-                        param, score_value = score.split(":")
-                        param = param.strip()
-                        score_value = score_value.strip()
-                        if param in parameters:
-                            try:
-                                score_value = float(score_value.split("/")[0].strip())
-                                valid_scores.append((param, score_value))
-                                if score_value >= 4:
-                                    color = "#2ca02c"  # Green
-                                elif score_value >= 3:
-                                    color = "#ff7f0e"  # Orange
-                                else:
-                                    color = "#d62728"  # Red
-                                st.markdown(f"**{param}**: <span style='color: {color}'>{score_value}/5</span>", unsafe_allow_html=True)
-                            except ValueError:
-                                pass
-
-            if valid_scores:
-                # Generate detailed feedback
-                with st.spinner("Generating detailed feedback..."):
-                    detailed_feedback = generate_detailed_feedback(input_text, parameters)
-                    st.subheader("Detailed Feedback")
-                    st.write(detailed_feedback)
-
-                    # Provide an option to download detailed feedback as a .txt or .pdf file
-                    temp_txt_path = tempfile.mktemp(suffix=".txt")
-                    with open(temp_txt_path, 'w') as f:
-                        f.write(detailed_feedback)
-
-                    st.download_button(
-                        label="Download Detailed Feedback as .txt",
-                        data=open(temp_txt_path, 'r').read(),
-                        file_name="detailed_feedback.txt",
-                        mime="text/plain"
-                    )
-
-                    pdf = FPDF()
-                    pdf.add_page()
-                    pdf.set_font("Arial", size=12)
-                    pdf.multi_cell(0, 10, detailed_feedback)
-
-                    temp_pdf_path = tempfile.mktemp(suffix=".pdf")
-                    pdf.output(temp_pdf_path)
-
-                    with open(temp_pdf_path, "rb") as f:
-                        st.download_button(
-                            label="Download Detailed Feedback as .pdf",
-                            data=f.read(),
-                            file_name="detailed_feedback.pdf",
-                            mime="application/pdf"
-                        )
+                st.write(sentiment_scores)
         else:
             st.warning("Please provide input and parameters for sentiment analysis.")
 
 if __name__ == "__main__":
-    main()
+    main()
+
+
+