Update app.py

app.py

@@ -1,25 +1,115 @@
 import streamlit as st
 import requests
 import os
+from pytube import YouTube
+import tempfile
+from fpdf import FPDF
 
-API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
-headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
+# Set your Together.AI API key
+together_api_key = os.environ.get("TOGETHER_API_KEY")
+url = "https://api.together.xyz/inference"
+headers = {
+    "Authorization": f"Bearer {together_api_key}",
+    "Content-Type": "application/json"
+}
+model = "togethercomputer/llama-2-70b-chat"
 
-def query(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
+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.
+    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.
+    '''
+    data = {
+        "model": model,
+        "prompt": f"[INST]{prompt}\n{input_text}[/INST]",
+        "temperature": 0.0,
+        "max_tokens": 1024
+    }
+    response = requests.post(url, headers=headers, json=data)
+    return response.json()['output']['choices'][0]['text']
+
+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.
+    '''
+    data = {
+        "model": model,
+        "prompt": f"[INST]{prompt}\n{input_text}[/INST]",
+        "temperature": 0.0,
+        "max_tokens": 1024
+    }
+    response = requests.post(url, headers=headers, json=data)
+    return response.json()['output']['choices'][0]['text']
+
+def transcript(video_link):
+    try:
+        yt = YouTube(video_link)
+        stream = yt.streams.filter(only_audio=True).first()
+        temp_file_path = stream.download()
+        print(f"Video '{yt.title}.mp4' downloaded successfully!")
+        with open(temp_file_path, 'rb') as audio_data:
+            total_transcript = requests.post(
+                "https://api.together.xyz/audio/transcribe",
+                headers=headers,
+                files={"file": audio_data}
+            ).json()["text"]
+            print("Done with the video processing\n")
+        os.remove(temp_file_path)
+        return total_transcript
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
 
 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)
+    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)
+    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:")
-        input_text = "Mock transcript from YouTube"  # Replace with actual transcript function
+        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
 
     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(",")]
@@ -27,13 +117,56 @@ def main():
     if st.button("Analyze"):
         if input_text and parameters:
             with st.spinner("Generating sentiment scores..."):
-                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)
+                sentiment_scores = generate_sentiment_score(input_text, parameters)
+                sentiment_scores = sentiment_scores.strip().split("\n")
                 st.subheader("Sentiment Scores")
-                st.write(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"
+                                elif score_value >= 3:
+                                    color = "#ff7f0e"
+                                else:
+                                    color = "#d62728"
+                                st.markdown(f"**{param}**: <span style='color: {color}'>{score_value}/5</span>", unsafe_allow_html=True)
+                            except ValueError:
+                                pass
+
+            if valid_scores:
+                with st.spinner("Generating detailed feedback..."):
+                    detailed_feedback = generate_detailed_feedback(input_text, parameters)
+                    st.subheader("Detailed Feedback")
+                    st.write(detailed_feedback)
+                    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"
+                        )
         else:
             st.warning("Please provide input and parameters for sentiment analysis.")