Update genesis/providers.py

genesis/providers.py

@@ -2,27 +2,19 @@
 import os
 import requests
 from typing import List, Dict
-from openai import OpenAI
 
-# Load API keys
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-HF_TOKEN = os.getenv("HF_TOKEN")  # Still used for Hugging Face models if needed
+HF_TOKEN = os.getenv("HF_TOKEN")
 GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
 NCBI_API_KEY = os.getenv("NCBI_API_KEY")
 NCBI_EMAIL = os.getenv("NCBI_EMAIL")
-DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
-
-client = OpenAI(api_key=OPENAI_API_KEY)
 
 # -------- DeepSeek Summary --------
 def run_deepseek_summary(prompt: str) -> str:
     """Run a dense scientific summary using DeepSeek API."""
-    if not DEEPSEEK_API_KEY:
-        print("[DeepSeek] API key not set, skipping.")
-        return prompt
     try:
         url = "https://api.deepseek.com/v1/chat/completions"
-        headers = {"Authorization": f"Bearer {DEEPSEEK_API_KEY}", "Content-Type": "application/json"}
+        headers = {"Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json"}
         payload = {
             "model": "deepseek-science",
             "messages": [{"role": "user", "content": prompt}],
@@ -43,9 +35,7 @@ def run_gemini_polish(text: str) -> str:
         return text
     try:
         url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key={GEMINI_API_KEY}"
-        payload = {
-            "contents": [{"parts": [{"text": f"Polish and clarify this research report for scientists:\n\n{text}"}]}]
-        }
+        payload = {"contents": [{"parts": [{"text": f"Polish and clarify this research report:\n\n{text}"}]}]}
         r = requests.post(url, json=payload, timeout=30)
         r.raise_for_status()
         data = r.json()
@@ -54,22 +44,40 @@ def run_gemini_polish(text: str) -> str:
         print(f"[Gemini] Failed: {e}")
         return text
 
-# -------- OpenAI Image Generation (DALL·E) --------
-def run_openai_image(query: str) -> str:
-    """Generate a research diagram using OpenAI DALL·E (gpt-image-1)."""
-    if not OPENAI_API_KEY:
-        print("[OpenAI Image] API key not set.")
-        return None
-    try:
-        resp = client.images.generate(
-            model="gpt-image-1",
-            prompt=f"Detailed, realistic scientific diagram about: {query}",
-            size="1024x1024"
-        )
-        return resp.data[0].url
-    except Exception as e:
-        print(f"[OpenAI Image] Failed: {e}")
-        return None
+# -------- OpenAI + HuggingFace Image Generation --------
+def run_gemini_image(query: str) -> str:
+    """Generate a research diagram — OpenAI first, Hugging Face fallback."""
+    # Try OpenAI first
+    if OPENAI_API_KEY:
+        try:
+            url = "https://api.openai.com/v1/images/generations"
+            headers = {"Authorization": f"Bearer {OPENAI_API_KEY}"}
+            payload = {"model": "gpt-image-1", "prompt": f"Scientific diagram about: {query}", "size": "1024x1024"}
+            r = requests.post(url, headers=headers, json=payload, timeout=60)
+            r.raise_for_status()
+            data = r.json()
+            return data["data"][0]["url"]
+        except Exception as e:
+            print(f"[OpenAI Image] Failed: {e}")
+
+    # Hugging Face Fallback
+    if HF_TOKEN:
+        try:
+            print("[Image] Falling back to Hugging Face Stable Diffusion...")
+            url = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
+            headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+            payload = {"inputs": f"highly detailed scientific diagram about: {query}"}
+            r = requests.post(url, headers=headers, json=payload, timeout=60)
+            r.raise_for_status()
+            # Save to local file
+            img_path = f"/tmp/{query.replace(' ', '_')}.png"
+            with open(img_path, "wb") as f:
+                f.write(r.content)
+            return img_path
+        except Exception as e:
+            print(f"[HF Image] Failed: {e}")
+
+    return None
 
 # -------- PubMed Fallback --------
 def pubmed_fallback_search(query: str, api_key: str, email: str) -> List[Dict]: