Update app.py

app.py

@@ -1,131 +1,47 @@
 import gradio as gr
+from langchain_together import ChatTogether
+from langchain_community.llms import Together
+from langchain_pinecone import PineconeVectorStore
+from langchain_openai import OpenAIEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_community.document_loaders import PyPDFLoader
+from elevenlabs.client import ElevenLabs
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import os
-from PyPDF2 import PdfReader
-from transformers import pipeline
-from langchain.prompts import ChatPromptTemplate
+import torch
 from io import BytesIO
-import time
-import wandb
-from rouge import Rouge
 
-# Environment variables setup
-os.environ['TOGETHER_API_KEY'] = 'your_together_api_key'
-os.environ['PINECONE_API_KEY'] = 'your_pinecone_api_key'
-ELEVENLABS_API_KEY = 'your_elevenlabs_api_key'
+# Environment Variables
+os.environ['TOGETHER_API_KEY'] = 'e83925ff068ab5e4598a56f68385fd37144469f50eec94f5c2e6647798f1be9e'
+os.environ['PINECONE_API_KEY'] = 'f7413055-9b13-4bbc-8c92-56132e034bff'
 
-# Summarization prompt
-summary_prompt = """
-    You are an expert AI summarization model tasked with creating a comprehensive summary for 10 years old kids of the provided context. The summary should be approximately one page long and well-structured.
+# Define your functions and classes here based on your original code
 
-    this is the context:
-    ```{context}```
-
-    Please follow these specific guidelines for the summary:
-
-    ### Detailed Summary
-    - **Section 1: Key Concepts**
-      - Introduce the first major topic or theme.
-      - Use bullet points to list important details and insights.
-
-    - **Section 2: Supporting Details**
-      - Discuss secondary topics or supporting arguments.
-      - Use bullet points to outline critical information and findings.
-
-    ### Conclusion
-    - Suggest any potential actions, solutions, or recommendations.
-
-    this is the summary:
-"""
-summary_prompt_template = ChatPromptTemplate.from_template(summary_prompt)
-
-# Define the PDF extraction function
-def extract_text_from_pdf(file):
-    reader = PdfReader(file)
-    text = ""
-    for page in reader.pages:
-        text += page.extract_text()
-    return text
-
-# Define the text-to-speech function
-def text_to_speech_stream(text):
-    client = ElevenLabs(api_key=ELEVENLABS_API_KEY)
-    response = client.text_to_speech.convert(
-        voice_id="jBpfuIE2acCO8z3wKNLl",
-        optimize_streaming_latency="0",
-        output_format="mp3_44100_64",
-        text=text,
-        model_id="eleven_multilingual_v2",
-        voice_settings=VoiceSettings(
-            stability=0.5,
-            similarity_boost=0.75,
-            style=0,
-            use_speaker_boost=True,
-        ),
-    )
-
-    audio_data = BytesIO()
-    for chunk in response:
-        if chunk:
-            audio_data.write(chunk)
-
-    audio_data.seek(0)
-    if not os.path.exists('samples'):
-        os.makedirs('samples')
-
-    with open('samples/output.mp3', 'wb') as f:
-        f.write(audio_data.read())
-
-    return 'samples/output.mp3'
-
-# Define the evaluation function
-def evaluate_summary(generated_summary):
-    wandb.init(project="learnverse")
-    
-    reference_summaries = ["Reference summary 1...", "Reference summary 2...", "Reference summary 3..."]
-    rouge = Rouge()
-    
-    rouge_1, rouge_2, rouge_l = {'r': 0, 'p': 0, 'f': 0}, {'r': 0, 'p': 0, 'f': 0}, {'r': 0, 'p': 0, 'f': 0}
-    
-    for reference in reference_summaries:
-        scores = rouge.get_scores(generated_summary, reference)
-        rouge_1['r'] += scores[0]['rouge-1']['r']
-        rouge_1['p'] += scores[0]['rouge-1']['p']
-        rouge_1['f'] += scores[0]['rouge-1']['f']
-        rouge_2['r'] += scores[0]['rouge-2']['r']
-        rouge_2['p'] += scores[0]['rouge-2']['p']
-        rouge_2['f'] += scores[0]['rouge-2']['f']
-        rouge_l['r'] += scores[0]['rouge-l']['r']
-        rouge_l['p'] += scores[0]['rouge-l']['p']
-        rouge_l['f'] += scores[0]['rouge-l']['f']
-    
-    num_references = len(reference_summaries)
-    rouge_1 = {key: value / num_references for key, value in rouge_1.items()}
-    rouge_2 = {key: value / num_references for key, value in rouge_2.items()}
-    rouge_l = {key: value / num_references for key, value in rouge_l.items()}
-    
-    wandb.log(rouge_1)
-    wandb.log(rouge_2)
-    wandb.log(rouge_l)
-    wandb.finish()
-    
-    return {'ROUGE-1': rouge_1, 'ROUGE-2': rouge_2, 'ROUGE-L': rouge_l}
-
-# Define the main processing function
+# Example function to process questions
 def process_question(file):
+    # Use your existing functions to process the PDF and generate outputs
     pdffile = extract_text_from_pdf(file)
-    summary = summary_prompt_template.invoke({"context": pdffile})
+    three_topics = topic_chain.invoke({"context": pdffile})
+    summary = summary_pdf_chain.invoke(pdffile)
     evaluation = evaluate_summary(summary)
     audio_file = text_to_speech_stream(summary)
-    return summary, evaluation, audio_file
-
-# Define the Gradio interface
-def gradio_interface(file):
-    summary, evaluation, audio_file = process_question(file)
-    return summary, evaluation, audio_file
-
-# Launch the Gradio app
-gr.Interface(
-    fn=gradio_interface,
-    inputs=gr.File(type="file", label="Upload PDF"),
-    outputs=[gr.Textbox(label="Summary"), gr.Textbox(label="Evaluation"), gr.Audio(label="Generated Audio")]
-).launch()
+    prompt = topics_prompt
+    shape = generate_gif(prompt)
+    ai_asistant = animate_image(audio_file)
+    return summary, evaluation, ai_asistant, shape
+
+# Define Gradio Interface
+iface = gr.Interface(
+    fn=process_question,
+    inputs=gr.inputs.File(label="Upload PDF File"),
+    outputs=[
+        gr.outputs.Textbox(label="Summary"),
+        gr.outputs.Textbox(label="Evaluation"),
+        gr.outputs.Audio(label="AI Assistant"),
+        gr.outputs.Image(label="3D Shape")
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()